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

Sex-stratified phenotyping of comorbidities associated with an inpatient delirium diagnosis using real world data

Delirium is a heterogeneous and detrimental mental condition often seen in older, hospitalized patients and is currently hard to predict. In this study, we leverage large-scale, real- world data using the electronic health records (EHR) to identify two cohorts comprised of 7,492 UCSF patients and 19,417 UC health system patients (excluding UCSF patients) with an inpatient delirium diagnosis and the same number of propensity score-matched control patients without delirium. We found significant associations between comorbidities or laboratory test values and an inpatient delirium diagnosis which were validated independently. Most of these associations were those previously-identified as risk factors for delirium, including metabolic abnormalities, mental health diagnoses, and infections. Some of the associations were sex- specific, including those related to dementia subtypes and infections. We further explored the diagnostic associations with anemia and bipolar disorder by conducting longitudinal analyses from the time of first diagnosis of the risk factor to development of delirium demonstrating a significant relationship across time. Finally, we show that an inpatient delirium diagnosis leads to dramatic increases in mortality outcome across both cohorts. These results demonstrate the powerful application of leveraging EHR data to shed insights into prior diagnoses and laboratory test values that could help predict development of inpatient delirium and emphasize the importance of considering patient demographic characteristics including documented sex when making these assessments.

One Sentence Summary

Longitudinal analysis of electronic health record data reveals associations between inpatient delirium, comorbidities, and mortality.

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.

Extremes of baseline cognitive function determine the severity of delirium: a population study

Although delirium is a significant clinical and public health problem, little is understood about how specific vulnerabilities underlie the severity of its presentation. Our objective was to quantify the relationship between baseline cognition and subsequent delirium severity. We prospectively investigated a population-representative sample of 1510 individuals aged ≥70 years, of whom 209 (13.6%) were hospitalized across 371 episodes (1999 person-days assessment). Baseline cognitive function was assessed using the modified Telephone Interview for Cognitive Status, supplemented by verbal fluency measures. We estimated the relationship between baseline cognition and delirium severity [Memorial Delirium Assessment Scale (MDAS)] and abnormal arousal (Observational Scale of Level of Arousal), adjusted by age, sex, frailty and illness severity. We conducted further analyses examining presentations to specific hospital settings and common precipitating aetiologies. The median time from baseline cognitive assessment to admission was 289 days (interquartile range 130 to 47 days). In admitted patients, delirium was present on at least 1 day in 45% of admission episodes. The average number of days with delirium (consecutively positive assessments) was 3.9 days. Elective admissions accounted for 88 bed days (4.4%). In emergency (but not elective) admissions, we found a non-linear U-shaped relationship between baseline global cognition and delirium severity using restricted cubic splines. Participants with baseline cognition 2 standard deviations below average (z-score = -2) had a mean MDAS score of 14 points (95% CI 10 to 19). Similarly, those with baseline cognition z-score = + 2 had a mean MDAS score of 7.9 points (95% CI 4.9 to 11). Individuals with average baseline cognition had the lowest MDAS scores. The association between baseline cognition and abnormal arousal followed a comparable pattern. C-reactive protein ≥20 mg/l and serum sodium <125 mM/l were associated with more severe delirium. Baseline cognition is a critical determinant of the severity of delirium and associated changes in arousal. Emergency admissions with lowest and highest baseline cognition who develop delirium should receive enhanced clinical attention.

Evaluating the performance of machine learning methods for risk estimation of delirium in patients hospitalized from the emergency department

INTRODUCTION

Delirium is a cerebral dysfunction seen commonly in the acute care setting. It is associated with increased mortality and morbidity and is frequently missed in the emergency department (ED) and inpatient care by clinical gestalt alone. Identifying those at risk of delirium may help prioritize screening and interventions in the hospital setting.

OBJECTIVE

Our objective was to leverage electronic health records to identify a clinically valuable risk estimation model for prevalent delirium in patients being transferred from the ED to inpatient units.

METHODS

This was a retrospective cohort study to develop and validate a risk model to detect delirium using patient data available from prior visits and ED encounter. Electronic health records were extracted for patients hospitalized from the ED between January 1, 2014, and December 31, 2020. Eligible patients were aged 65 or older, admitted to an inpatient unit from the emergency department, and had at least one DOSS assessment or CAM-ICU recorded within 72 h of hospitalization. Six machine learning models were developed to estimate the risk of delirium using clinical variables including demographic features, physiological measurements, medications administered, lab results, and diagnoses.

RESULTS

A total of 28,531 patients met the inclusion criteria with 8057 (28.4%) having a positive delirium screening within the outcome observation period. Machine learning models were compared using the area under the receiver operating curve (AUC). The gradient boosted machine achieved the best performance with an AUC of 0.839 (95% CI, 0.837-0.841). At a 90% sensitivity threshold, this model achieved a specificity of 53.5% (95% CI 53.0%-54.0%) a positive predictive value of 43.5% (95% CI 43.2%-43.9%), and a negative predictive value of 93.1% (95% CI 93.1%-93.2%). A random forest model and L1-penalized logistic regression also demonstrated notable performance with AUCs of 0.837 (95% CI, 0.835-0.838) and 0.831 (95% CI, 0.830-0.833) respectively.

CONCLUSION

This study demonstrated the use of machine learning algorithms to identify a combination of variables that enables an estimation of risk of positive delirium screens early in hospitalization to develop prevention or management protocols.

Validation and Recalibration of Modified Mayo Delirium Prediction (MDP) Tool in a Hospitalized Cohort

BACKGROUND

Delirium prediction can augment and optimize care of older adults. Mayo delirium prediction (MDP) tool is a robust tool, developed from a large retrospective data set. MDP tool predicts delirium risk for hospitalized older adults, within 24 hours of hospital admission, based on risk factor information available from electronic health record. We intend to validate the prediction performance of this tool and optimize the tool for clinical use.

DESIGN

Observational cohort study SETTINGS: Mayo Clinic Hospitals, Rochester, MN PARTICIPANTS: All Hospitalized older adults (age >50 years) from December 2019 to June 2020. Patients with an admitting diagnosis of substance use disorder were excluded.

INTERVENTION

Original MDP tool was modified to adjust for the fall risk variable as a binary variable that will facilitate broader applicability across different fall risk tools. The modified MDP tool was validated in the retrospective derivation and validation data set which yielded similar prediction capability (AUROC = 0.85, 0.83 respectively).

MEASUREMENTS

Diagnosis of delirium was captured by flowsheet diagnosis of delirium documented by nursing staff in medical record. Predictive variable data were collected daily.

RESULTS

8055 patients were included in the study (median age 71 years). The modified MDP tool delirium prediction compared to delirium occurrence was 4% in the low-risk group, 17.8% in the medium-risk group, and 45.3% in the high-risk group (AUROC of 0.80). Recalibration of the tool was attempted to further optimize the tool that resulted in both simplification and increased performance (AUROC 0.82). The simplified tool was able to predict delirium in hospitalized patients admitted to both medical and surgical services.

CONCLUSIONS

Validation of modified MDP tool revealed good prediction capabilities. Recalibration resulted in simplification with increased performance of the tool in both medical and surgical hospitalized patients.

Postoperative delirium prediction using machine learning models and preoperative electronic health record data

Background

Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression.

Methods

This was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models (“clinician-guided” and “ML hybrid”), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded.

Results

POD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 [95% CI 0. 816–0.863] and for XGBoost was 0.851 [95% CI 0.827–0.874], which was significantly better than the clinician-guided (AUC-ROC 0.763 [0.734–0.793], p < 0.001) and ML hybrid (AUC-ROC 0.824 [0.800–0.849], p < 0.001) regression models and AWOL-S (AUC-ROC 0.762 [95% CI 0.713–0.812], p < 0.001). Neural Net, XGBoost, and ML hybrid models demonstrated excellent calibration, while calibration of the clinician-guided and AWOL-S models was moderate; they tended to overestimate delirium risk in those already at highest risk.

Conclusion

Using pragmatically collected EHR data, two ML models predicted POD in a broad perioperative population with high discrimination. Optimal application of the models would provide automated, real-time delirium risk stratification to improve perioperative management of surgical patients at risk for POD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01543-y.

Impact of a system-wide multicomponent intervention on administrative diagnostic coding for delirium and other cognitive frailty syndromes: observational prospective study

BACKGROUND

We determined the impact of a system-wide multicomponent intervention to improve recognition and documentation of cognitive frailty syndromes on hospital administrative coding for delirium.

METHODS

A multicomponent intervention including introduction of structured patient assessment including cognitive/delirium screen, regular audit/feedback and educational seminars was undertaken (2012-17). Sensitivity and specificity of administrative International Classification of Diseases, 10th revision (ICD-10) delirium codes for the gold standard of prospectively clinically diagnosed delirium were calculated in consecutive patients admitted to acute medicine over five 8-week cycles (2010-18).

RESULTS

Among 1,281 consecutive unselected admissions to acute medicine overall (mean / standard deviation age = 70.0/19.2 years; n=615 (48.0%) male), 320 had clinical delirium diagnosis (n=220 delirium only; n=100 delirium on dementia). Sensitivity of delirium coding increased from 12.8% (95% confidence interval (CI) 5.6-26.7) in 2010 to 60.2% (95% CI 50.1-69.7; p_trend<0.0001) in 2018 while specificity remained at >99% throughout.

CONCLUSION

A multicomponent intervention increased sensitivity of hospital administrative diagnostic coding for delirium almost six-fold without increasing the false positive diagnosis rate.

Underestimation of Cognitive Impairment in Older Inpatients by the Abbreviated Mental Test Score versus the Montreal Cognitive Assessment: Cross-Sectional Observational Study

Background/Aims

Cognitive impairment is prevalent in older inpatients but may be unrecognized. Screening to identify cognitive deficits is therefore important to optimize care. The 10-point Abbreviated Mental Test Score (AMTS) is widely used in acute hospital settings but its reliability for mild versus more severe cognitive impairment is unknown. We therefore studied the AMTS versus the 30-point Montreal Cognitive Assessment (MoCA) in older (≥75 years) inpatients.

Methods

The AMTS and MoCA were administered to consecutive hospitalized patients at ≥72 h after admission in a prospective observational study. MoCA testing time was recorded. Reliability of the AMTS for the reference standard defined as mild (MoCA <26) or moderate/severe (MoCA <18) cognitive impairment was assessed using the area under the receiver-operating curve (AUC). Sensitivity, specificity, positive and negative predictive values of low AMTS (<8) for cognitive impairment were determined.

Results

Among 205 patients (mean/SD age = 84.9/6.3 years, 96 (46.8%) male, 74 (36.1%) dementia/delirium), mean/SD AMTS was 7.2/2.3, and mean/SD MoCA was 16.1/6.2 with mean/SD testing time = 17.9/7.2 min. 96/205 (46.8%) had low AMTS whereas 174/185 (94%) had low MoCA: 74/185 (40.0%) had mild and 100 (54.0%) had moderate/severe impairment. Moderate/severe cognitive impairment was more prevalent in the low versus the normal AMTS group: 74/83 (90%) versus 25/102 (25%, p < 0.0001). AUC of the AMTS for mild and moderate/severe impairment were 0.86 (95% CI = 0.80–0.93) and 0.88 (0.82–0.93), respectively. Specificity of AMTS <8 for both mild and moderate/severe cognitive impairment was high (100%, 71.5–100, and 92.7%, 84.8–97.3) but sensitivity was lower (44.8%, 37.0–52.8, and 72.8%, 62.6–81.6, respectively). The negative predictive value of AMTS <8 was therefore low for mild impairment (10.9%, 5.6–18.7) but much higher for moderate/severe impairment (75.2%, 65.7–83.3). All MoCA subtests discriminated between low and normal AMTS groups (all p < 0.0001, except p = 0.002 for repetition) but deficits in delayed recall, verbal fluency and visuo-executive function were prevalent even in the normal AMTS group.

Conclusion

The AMTS is highly specific but relatively insensitive for cognitive impairment: a quarter of those with normal AMTS had moderate/severe impairment on the MoCA with widespread deficits. The AMTS cannot therefore be used as a “rule-out” test, and more detailed cognitive assessment will be required in selected 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.

Renal function and delirium in older fracture patients: different information from different formulas?

OBJECTIVES

the association between renal function and delirium has not been investigated in older fracture patients. Creatinine is frequently low in these subjects, which may influence the association between delirium and renal function as estimated with creatinine-based formulas. Cystatin C could be a more reliable filtration marker in these patients.

AIM

to confirm the association between renal function and delirium in older fracture patients comparing creatinine- and cystatin-based estimated glomerular filtration rate (eGFR) METHODS: patients aged 65+ requiring surgery for traumatic bone fractures were included. Six equations were used to calculate eGFR, based on serum creatinine and/or cystatin C obtained within 24 h of admission: Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology (CKD-EPI_cr, CKD-EPI_cys, CKD-EPI_cr-cys) and Berlin Initiative Study equations (BIS-1, BIS-2). Delirium was identified with a chart-based method.

RESULTS

571 patients (mean age 83) were enrolled. Delirium occurred in the 34% and was associated with a lower eGFR regardless of the equation used. In a multivariable model, the association between moderate renal impairment (eGFR 30-60 ml/min/1.73 m²) and delirium remained significant in patients aged 75-84 and only when estimated with cystatin-based or BIS-1 equations. Only dementia was significantly associated with delirium in subjects 85+.

CONCLUSIONS

in older fracture patients, moderate renal impairment was independently associated with delirium only among subjects aged 75-84, when eGFR was estimated with cystatin-based or BIS 1 equations, and not with the most commonly used equations (MDRD, CKD-EPI_cr).

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.

Have you SCAND MMe Please? A framework to prevent harm during acute hospitalisation of older persons: A retrospective audit

AIMS AND OBJECTIVES

To test the mnemonic Have you SCAND MMe Please? as a framework to audit nursing care to prevent harms common to older inpatients.

BACKGROUND

It is not known if acute hospital care comprehensively addresses eight interrelated factors that contribute to preventable harms common in older hospitalised patients.

DESIGN

Retrospective audit of medical records.

METHODS

A random selection of 400 medical records of inpatients over 65 years of age with an unplanned admission of longer than 72 hr in acute medical wards at four hospitals in Victoria, Australia, during 2011-12, was examined for frequency of documented evidence of assessments, interventions or new problems related to eight factors contributing to common preventable harms during hospitalisation.

RESULTS

Assessments of skin integrity (94%-97%), mobility (95%-98%) and pain (93%-97%) were most often documented. Gaps in assessment of continence (4%-31%), nutrition (9%-49%), cognition (delirium, depression and dementia) (10%-24%) were most common. No patient record had evidence of all eight factors being assessed. Almost 80% of records had interventions documented for one or more factors that contribute to preventable harms. In almost 20% of patient records, a new preventable harm was documented during hospitalisation.

CONCLUSIONS

The mnemonic Have you SCAND MMe Please? brings together eight factors known to contribute to preventable harms common in older hospitalised patients. This framework was useful to identify gaps in assessment and interventions for factors that contribute to preventable harms during acute hospital care. Future research should test if the mnemonic can assist nurses with comprehensive harm prevention during acute hospitalisation.

RELEVANCE TO CLINICAL PRACTICE

The mnemonic Have you SCAND MMe Please? represents eight factors that contribute to preventable harms common in older hospitalised patients. This framework provides a model for harm prevention to assist nurses to implement comprehensive harm prevention to improve quality of care and safety for older hospitalised patients.

Development and Validation of an Electronic Health Record-Based Machine Learning Model to Estimate Delirium Risk in Newly Hospitalized Patients Without Known Cognitive Impairment

IMPORTANCE

Current methods for identifying hospitalized patients at increased risk of delirium require nurse-administered questionnaires with moderate accuracy.

OBJECTIVE

To develop and validate a machine learning model that predicts incident delirium risk based on electronic health data available on admission.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective cohort study evaluating 5 machine learning algorithms to predict delirium using 796 clinical variables identified by an expert panel as relevant to delirium prediction and consistently available in electronic health records within 24 hours of admission. The training set comprised 14 227 adult patients with non-intensive care unit hospital stays and no delirium on admission who were discharged between January 1, 2016, and August 31, 2017, from UCSF Health, a large academic health institution. The test set comprised 3996 patients with hospital stays who were discharged between August 1, 2017, and November 30, 2017.

EXPOSURES

Patient demographic characteristics, diagnoses, nursing records, laboratory results, and medications available in electronic health records during hospitalization.

MAIN OUTCOMES AND MEASURES

Delirium was defined as a positive Nursing Delirium Screening Scale or Confusion Assessment Method for the Intensive Care Unit score. Models were assessed using the area under the receiver operating characteristic curve (AUC) and compared against the 4-point scoring system AWOL (age >79 years, failure to spell world backward, disorientation to place, and higher nurse-rated illness severity), a validated delirium risk-assessment tool routinely administered in this cohort.

RESULTS

The training set included 14 227 patients (5113 [35.9%] aged >64 years; 7335 [51.6%] female; 687 [4.8%] with delirium), and the test set included 3996 patients (1491 [37.3%] aged >64 years; 1966 [49.2%] female; 191 [4.8%] with delirium). In total, the analysis included 18 223 hospital admissions (6604 [36.2%] aged >64 years; 9301 [51.0%] female; 878 [4.8%] with delirium). The AWOL system achieved a baseline AUC of 0.678. The gradient boosting machine model performed best, with an AUC of 0.855. Setting specificity at 90%, the model had a 59.7% (95% CI, 52.4%-66.7%) sensitivity, 23.1% (95% CI, 20.5%-25.9%) positive predictive value, 97.8% (95% CI, 97.4%-98.1%) negative predictive value, and a number needed to screen of 4.8. Penalized logistic regression and random forest models also performed well, with AUCs of 0.854 and 0.848, respectively.

CONCLUSIONS AND RELEVANCE

Machine learning can be used to estimate hospital-acquired delirium risk using electronic health record data available within 24 hours of hospital admission. Such a model may allow more precise targeting of delirium prevention resources without increasing the burden on health care professionals.

Performance of Electronic Prediction Rules for Prevalent Delirium at Hospital Admission

IMPORTANCE

Delirium at admission is associated with increased hospital morbidity and mortality, but it may be missed in up to 70% of cases. Use of a predictive algorithm in an electronic medical record (EMR) system could provide critical information to target assessment of those with delirium at admission.

OBJECTIVES

To develop and assess a prediction rule for delirium using 2 populations of veterans and compare this rule with previously confirmed rules.

DESIGN, SETTING, AND PARTICIPANTS

In a diagnostic study, randomly selected EMRs of hospitalized veterans from the Veterans Affairs (VA) External Peer Review Program at 118 VA medical centers with inpatient facilities were reviewed for delirium risk factors associated with the National Institute for Health and Clinical Excellence (NICE) delirium rule in a derivation cohort (October 1, 2012, to September 30, 2013) and a confirmation cohort (October 1, 2013, to March 31, 2014). Delirium within 24 hours of admission was identified using key word terms. A total of 39 377 veterans 65 years or older who were admitted to a VA medical center for congestive heart failure, acute coronary syndrome, community-acquired pneumonia, and chronic obstructive pulmonary disease were included in the study.

EXPOSURE

The EMR calculated delirium risk.

MAIN OUTCOMES AND MEASURES

Delirium at admission as identified by trained nurse reviewers was the main outcome measure. Random forest methods were used to identify accurate risk factors for prevalent delirium. A prediction rule for prevalent delirium was developed, and its diagnostic accuracy was tested in the confirmation cohort. This consolidated NICE rule was compared with previously confirmed scoring algorithms (electronic NICE and Pendlebury NICE).

RESULTS

A total of 27 625 patients were included in the derivation cohort (28 118 [92.2%] male; mean [SD] age, 75.95 [8.61] years) and 11 752 in the confirmation cohort (11 536 [98.2%] male; mean [SD] age, 75.43 [8.55] years). Delirium at admission was identified in 2343 patients (8.5%) in the derivation cohort and 882 patients (7.0%) in the confirmation cohort. Modeling techniques identified cognitive impairment, infection, sodium level, and age of 80 years or older as the dominant risk factors. The consolidated NICE rule (area under the receiver operating characteristic [AUROC] curve, 0.91; 95% CI, 0.91-0.92; P < .001) had significantly higher discriminatory function than the eNICE rule (AUROC curve, 0.81; 95% CI, 0.80-0.82; P < .001) or Pendlebury NICE rule (AUROC curve, 0.87; 95% CI, 0.86-0.88; P < .001). These findings were confirmed in the confirmation cohort.

CONCLUSIONS AND RELEVANCE

This analysis identified preexisting cognitive impairment, infection, sodium level, and age of 80 years or older as delirium screening targets. Use of this algorithm in an EMR system could direct clinical assessment efforts to patients with delirium at admission.

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.

Is anemia associated with cognitive impairment and delirium among older acute surgical patients?

AIM

The determinants of cognitive impairment and delirium during acute illness are poorly understood, despite being common among older people. Anemia is common in older people, and there is ongoing debate regarding the association between anemia, cognitive impairment and delirium, primarily in non-surgical patients.

METHODS

Using data from the Older Persons Surgical Outcomes Collaboration 2013 and 2014 audit cycles, we examined the association between anemia and cognitive outcomes in patients aged ≥65 years admitted to five UK acute surgical units. On admission, the Confusion Assessment Method was carried out to detect delirium. Cognition was assessed using the Montreal Cognitive Assessment, and two levels of impairment were defined as Montreal Cognitive Assessment <26 and <20. Logistic regression models were constructed to examine these associations in all participants, and individuals aged ≥75 years only.

RESULTS

A total of 653 patients, with a median age of 76.5 years (interquartile range 73.0-80.0 years) and 53% women, were included. Statistically significant associations were found between anemia and age; polypharmacy; hyperglycemia; and hypoalbuminemia. There was no association between anemia and cognitive impairment or delirium. The adjusted odds ratios of cognitive impairment were 0.95 (95% CI 0.56-1.61) and 1.00 (95% CI 0.61-1.64) for the Montreal Cognitive Assessment <26 and <20, respectively. The adjusted odds ratio of delirium was 1.00 (95% CI 0.48-2.10) in patients with anemia compared with those without. Similar results were observed for the ≥75 years age group.

CONCLUSIONS

There was no association between anemia and cognitive outcomes among older people in this acute surgical setting. Considering the retrospective nature of the study and possible lack of power, findings should be taken with caution. Geriatr Gerontol Int 2018; 18: 1025-1030.

Management of delirium across an integrated health system

Facilitating throughput with systems thinking.

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.

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.

Early geriatric consultation increases adherence to TQIP Geriatric Trauma Management Guidelines

BACKGROUND

The American College of Surgeons' Trauma Quality Improvement Program (TQIP) Geriatric Trauma Management Guidelines recommend geriatric consultation for injured older adults. However it is not known how or whether geriatric consultation improves compliance to these quality measures.

METHODS

This study is a retrospective chart review of our institutional trauma databank. Adherence to quality measures was compared before and after implementation of specific triggers for geriatric consultation. Secondary analyses evaluated adherence by service: trauma service (Trauma) or a trauma service with early geriatric consultation (GeriTrauma).

RESULTS

The average age of the 245 patients was 76.7 years, 47% were women, and mean Injury Severity Score was 9.5 (SD ±8.1). Implementation of the GeriTrauma collaborative increased geriatric consultation rates from 2% to 48% but had minimal effect on overall adherence to TQIP quality measures. A secondary analysis comparing those in the post implementation group who received geriatric consultation (n = 94) to those who did not (n = 103) demonstrated higher rates of delirium diagnosis (36.2% vs 14.6%, P < 0.01) and better documentation of initial living situation, code status, and medication list in the GeriTrauma group. Physical therapy was consulted more frequently for GeriTrauma patients (95.7% vs 68.0%, P < 0.01) Documented goals of care discussions were rare and difficult to abstract. A subgroup analysis of only patients with fall-related injuries demonstrated similar outcomes.

CONCLUSIONS

Early geriatric consultation increases adherence to TQIP guidelines. Further research into the long term significance and validity of these geriatric trauma quality indicators is needed.

Delirium risk stratification in consecutive unselected admissions to acute medicine: validation of a susceptibility score based on factors identified externally in pooled data for use at entry to the acute care pathway

Background

recognition of prevalent delirium and prediction of incident delirium may be difficult at first assessment. We therefore aimed to validate a pragmatic delirium susceptibility (for any, prevalent and incident delirium) score for use in front-line clinical practice in a consecutive cohort of older acute medicine patients.

Methods

consecutive patients aged ≥65 years over two 8-week periods (2010–12) were screened prospectively for delirium using the Confusion Assessment Method (CAM), and delirium was diagnosed using the DSM IV criteria. The delirium susceptibility score was the sum of weighted risk factors derived using pooled data from UK-NICE guidelines: age >80 = 2, cognitive impairment (cognitive score below cut-off/dementia) = 2, severe illness (systemic inflammatory response syndrome) = 1, infection = 1, visual impairment = 1. Score reliability was determined by the area under the receiver operating curve (AUC).

Results

among 308 consecutive patients aged ≥65 years (mean age/SD = 81/8 years, 164 (54%) female), AUC was 0.78 (95% CI 0.71–0.84) for any delirium; 0.71 (0.64–0.79), for prevalent delirium; 0.81 (0.70–0.92), for incident delirium; odds ratios (ORs) for risk score 5–7 versus <2 were 17.9 (5.4–60.0), P < 0.0001 for any delirium, 8.1 (2.2–29.7), P = 0.002 for prevalent delirium, and 25.0 (3.0–208.9) P = 0.003 for incident delirium, with corresponding relative risks of 5.4, 4.7 and 13. Higher risk scores were associated with frailty markers, increased care needs and poor outcomes.

Conclusions

the externally derived delirium susceptibility score reliably identified prevalent and incident delirium using clinical data routinely available at initial patient assessment and might therefore aid recognition of vulnerability in acute medical admissions early in the acute care pathway.