Predicting delirium: a review of risk-stratification models

Newman, Mark W.; O’Dwyer, Linda; Rosenthal, Lisa

doi:10.1016/j.genhosppsych.2015.05.003

Cited by 28 publications

(28 citation statements)

References 33 publications

(40 reference statements)

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“…In conclusion, we have shown that delirium is common in this setting [6], and the independent factors predictive of delirium in Tanzania are very similar to those found in HICs [4,5]. In particular, dementia, a widely established risk factor for delirium in HICs [22], has for the first time been shown to be a risk factor for delirium in SSA.…”

Section: Discussionmentioning

confidence: 65%

“…Elevated serum urea and functional dependency have both commonly been identified as delirium risk factors in HICs [5]. Raised serum urea or urea to creatinine ratio (used as a proxy for dehydration) was found to be significantly predictive of incident delirium, and authors have argued that these could form part of a simple screening tool [26,27].…”

Section: Discussionmentioning

confidence: 99%

“…Yet, there is significant heterogeneity in measurement, with some studies preferring a physician or nurse's subjective opinion over measurements of physiological parameters [27,34]. Where physiological parameters have been used, the most commonly employed tool is the Acute Physiology and Chronic Health Evaluation II (APACHE II) [4,5], originally devised and developed for use in critical care settings [35]. There is strong evidence for its association with delirium in older hospitalised patients [4,5], but it has been criticised for its cumbersome nature and inclusion of measures not routinely conducted on general medical wards.…”

Section: Discussionmentioning

confidence: 99%

“…Where physiological parameters have been used, the most commonly employed tool is the Acute Physiology and Chronic Health Evaluation II (APACHE II) [4,5], originally devised and developed for use in critical care settings [35]. There is strong evidence for its association with delirium in older hospitalised patients [4,5], but it has been criticised for its cumbersome nature and inclusion of measures not routinely conducted on general medical wards. We chose to use the NEWS for practicality, and given our resource-limited setting.…”

Section: Discussionmentioning

confidence: 99%

“…Most delirium risk research comes from high-income countries (HICs), whereas very little is known about risk factors for delirium in low- or middle-income counties (LMICs), including Sub-Saharan Africa (SSA). Established risk factors for delirium in HICs include pre-existing dementia, medical comorbidity, and increasing age [1,4,5]. The authors have shown that delirium prevalence in the acute hospital setting is comparable with that of HICs [6]; however, there is still an overwhelming lack of research in this field.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Risk Factors for Delirium in Older Medical Inpatients in Tanzania

Lewis¹,

Banks²,

Paddick³

et al. 2017

Dement Geriatr Cogn Disord

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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.

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Section: Discussionmentioning

confidence: 65%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Risk Factors for Delirium in Older Medical Inpatients in Tanzania

Lewis¹,

Banks²,

Paddick³

et al. 2017

Dement Geriatr Cogn Disord

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Delirium

Golimstok¹,

Moreno-Milicich²

2020

Frailty and Kidney Disease

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

et al. 2023

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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.

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Predicting delirium: a review of risk-stratification models

Cited by 28 publications

References 33 publications

Risk Factors for Delirium in Older Medical Inpatients in Tanzania

Risk Factors for Delirium in Older Medical Inpatients in Tanzania

Delirium

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

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