Correction: Risk Factors for Delirium During Acute and Subacute States of Various Disorders in Patients Admitted to Rehabilitation Units

Jang, Soyeon; Jung, Kwang‐Ik; Yoo, Woo-Kyoung; Jung, Myung Hun; Ohn, Suk Hoon

doi:10.5535/arm.2017.41.1.168

Cited by 5 publications

(3 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The overall prevalence of delirium in IRFs is reported as approximately 10% to 16.3% which is consistent with our finding described in the section of implementation of delirium screening. 13,14 However, the prevalence varies based on the primary diagnosis. Stroke patients have a delirium prevalence of 33% upon admission to an IRF.…”

Section: Epidemiology Of Deliriummentioning

confidence: 99%

“…Delirium results from complex interactions among multiple predisposing and precipitating factors (Table 3). 1,13 Predisposing factors are unmodifiable and set baseline vulnerability for an individual. Precipitating factors trigger the development of delirium and are often modifiable.…”

Section: Epidemiology Of Deliriummentioning

confidence: 99%

“…In IRFs, patients with delirium stay approximately 6 days longer than those without delirium. 13 The main cost factors during hospitalization are personnel expenditure and increased length of stay. 18 There have been a number of studies reporting that systematic implementation of delirium screening, prevention, and intervention programs can reduce cost, fall risks, and pressure injuries in various care settings.…”

Section: Impact Of Delirium On Healthcare Costmentioning

confidence: 99%

See 2 more Smart Citations

Delirium Screening and Management in Inpatient Rehabilitation Facilities

Oh‐Park

Chen

Romel-Nichols

et al. 2018

Am J Phys Med Rehabil

View full text Add to dashboard Cite

Delirium is an acute and fluctuating disturbance in cognition attention and awareness that is often a reflection of abnormal physiological condition of an individual. Delirium is highly prevalent among an older population and is associated with high mortality, poor medical and functional outcomes, and high healthcare cost. Delirium often has iatrogenic triggers, and it has been recognized as a quality indicator of healthcare organizations. Despite its high prevalence and significance, more than 50% of the delirium cases are underrecognized by healthcare professionals and remained untreated. Most patients in inpatient rehabilitation facilities are older adults with multiple risk factors for delirium including operation, intensive care stay, multiple co-morbidities, and impaired mobility. Early detection, intervention, and primary prevention of delirium will allow patients to avoid additional morbidities and reach their maximum functional potential during their rehabilitation stay. After the systematic implementation of delirium screening in our inpatient rehabilitation facility, we found that 10.3% of patients were screened positive for delirium at admission. This review discusses the systematic implementation of screening and intervention for delirium as well as the epidemiology of delirium to increase the awareness and guide clinical practice for clinicians in inpatient rehabilitation facilities.

show abstract

Section: Epidemiology Of Deliriummentioning

confidence: 99%

Section: Epidemiology Of Deliriummentioning

confidence: 99%

Section: Impact Of Delirium On Healthcare Costmentioning

confidence: 99%

See 1 more Smart Citation

Delirium Screening and Management in Inpatient Rehabilitation Facilities

Oh‐Park

Chen

Romel-Nichols

et al. 2018

Am J Phys Med Rehabil

View full text Add to dashboard Cite

show abstract

Prevalence and features of delirium in older patients admitted to rehabilitation facilities: a multicenter study

et al. 2022

View full text Add to dashboard Cite

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

et al. 2022

View full text Add to dashboard Cite

Introduction Delirium occurrence is common and preventive strategies are resource intensive. Screening tools can prioritize patients at risk. Using machine learning, we can capture time and treatment effects that pose a challenge to delirium prediction. We aim to develop a delirium prediction model that can be used as a screening tool. Methods From the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care version III (MIMIC-III) database, patients with one or more Confusion Assessment Method-Intensive Care Unit (CAM-ICU) values and intensive care unit (ICU) length of stay greater than 24 h were included in our study. We validated our model using 21 quantitative clinical parameters and assessed performance across a range of observation and prediction windows, using different thresholds and applied interpretation techniques. We evaluate our models based on stratified repeated cross-validation using 3 algorithms, namely Logistic Regression, Random Forest, and Bidirectional Long Short-Term Memory (BiLSTM). BiLSTM represents an evolution from recurrent neural network-based Long Short-Term Memory, and with a backward input, preserves information from both past and future. Model performance is measured using Area Under Receiver Operating Characteristic, Area Under Precision Recall Curve, Recall, Precision (Positive Predictive Value), and Negative Predictive Value metrics. Results We evaluated our results on 16 546 patients (47% female) and 6294 patients (44% female) from eICU-CRD and MIMIC-III databases, respectively. Performance was best in BiLSTM models where, precision and recall changed from 37.52% (95% confidence interval [CI], 36.00%–39.05%) to 17.45 (95% CI, 15.83%–19.08%) and 86.1% (95% CI, 82.49%–89.71%) to 75.58% (95% CI, 68.33%–82.83%), respectively as prediction window increased from 12 to 96 h. After optimizing for higher recall, precision and recall changed from 26.96% (95% CI, 24.99%–28.94%) to 11.34% (95% CI, 10.71%–11.98%) and 93.73% (95% CI, 93.1%–94.37%) to 92.57% (95% CI, 88.19%–96.95%), respectively. Comparable results were obtained in the MIMIC-III cohort. Conclusions Our model performed comparably to contemporary models using fewer variables. Using techniques like sliding windows, modification of threshold to augment recall and feature ranking for interpretability, we addressed shortcomings of current models.

show abstract

Correction: Risk Factors for Delirium During Acute and Subacute States of Various Disorders in Patients Admitted to Rehabilitation Units

Cited by 5 publications

References 34 publications

Delirium Screening and Management in Inpatient Rehabilitation Facilities

Delirium Screening and Management in Inpatient Rehabilitation Facilities

Prevalence and features of delirium in older patients admitted to rehabilitation facilities: a multicenter study

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

Contact Info

Product

Resources

About