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

Kalimisetty, Sundeep; Askar, Wajih; Fay, Brenda; Khan, Ajmal

doi:10.17294/2330-0698.1414

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…This is important, because machine-learning approaches have emerged and evolved during these recent years. Other systematic reviews have exclusively studied older adults ( 23 , 24 ), excluded validation studies ( 25 ), or were restricted to cardiac surgery patients ( 22 ). The studies included in our review were summarized in detail in terms of candidate predictors, final model predictors, and risk of bias ( 26 ).…”

Section: Discussionmentioning

confidence: 99%

ICU Delirium-Prediction Models: A Systematic Review

Ruppert

Lipori

Patel

et al. 2020

Critical Care Explorations

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Section: Discussionmentioning

confidence: 99%

ICU Delirium-Prediction Models: A Systematic Review

Ruppert

Lipori

Patel

et al. 2020

Critical Care Explorations

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“…A total of four systematic reviews were identified that reported on CPMs for postoperative delirium in elderly non-ICU patients 9 21–23. Altogether, the systematic reviews covered a time period from 1 January 1990 to 1 January 2017 with partial overlap.…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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ObjectivesDelirium is associated with increased morbidity, mortality, prolonged hospitalisation and increased healthcare costs. The number of clinical prediction models (CPM) to predict postoperative delirium has increased exponentially. Our goal is to perform a head-to-head comparison of CPMs predicting postoperative delirium in non-intensive care unit (non-ICU) elderly patients to identify the best performing models.SettingSingle-site university hospital.DesignSecondary analysis of prospective cohort study.Participants and inclusionCPMs published within the timeframe of 1 January 1990 to 1 May 2020 were checked for eligibility (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). For the time period of 1 January 1990 to 1 January 2017, included CPMs were identified in systematic reviews based on prespecified inclusion and exclusion criteria. An extended literature search for original studies was performed independently by two authors, including CPMs published between 1 January 2017 and 1 May 2020. External validation was performed using a surgical cohort consisting of 292 elderly non-ICU patients.Primary outcome measuresDiscrimination, calibration and clinical usefulness.Results14 CPMs were eligible for analysis out of 366 full texts reviewed. External validation was previously published for 8/14 (57%) CPMs. C-indices ranged from 0.52 to 0.74, intercepts from −0.02 to 0.34, slopes from −0.74 to 1.96 and scaled Brier from −1.29 to 0.088. Based on predefined criteria, the two best performing models were those of Dai et al (c-index: 0.739; (95% CI: 0.664 to 0.813); intercept: −0.018; slope: 1.96; scaled Brier: 0.049) and Litaker et al (c-index: 0.706 (95% CI: 0.590 to 0.823); intercept: −0.015; slope: 0.995; scaled Brier: 0.088). For the remaining CPMs, model discrimination was considered poor with corresponding c-indices <0.70.ConclusionOur head-to-head analysis identified 2 out of 14 CPMs as best-performing models with a fair discrimination and acceptable calibration. Based on our findings, these models might assist physicians in postoperative delirium risk estimation and patient selection for preventive measures.

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“…For patients under mechanical ventilation admitted in the ICU the percentage of delirium exceeds 75% ( Ely et al, 2004 ). Older age, decreased functional status, cognitive impairment (even mild), depression and comorbidities constitute the most common predisposing factors for delirium, while a subjacent infection, acute or severe illness, pain, anemia, anesthesia and drugs (including sedative hypnotics and anticholinergics) are the most frequently encountered precipitating factors ( Kalimisetty, Askar, Fay, & Khan, 2017 ; Marcantonio, 2017 ; Wakefield, 2002 ). Undernourishment and dehydration on admission might be aggravating factors ( Wakefield, 2002 ).…”

Section: Usual Complications In Hospitalized Pwp and Covid-19mentioning

confidence: 99%