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

Machine learning-based model to predict delirium in patients with advanced cancer treated with palliative care: a multicenter, patient-based registry cohort

This study aimed to present a new approach to predict to delirium admitted to the acute palliative care unit. To achieve this, this study employed machine learning model to predict delirium in patients in palliative care and identified the significant features that influenced the model. A multicenter, patient-based registry cohort study in South Korea between January 1, 2019, and December 31, 2020. Delirium was identified by reviewing the medical records based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The study dataset included 165 patients with delirium among 2314 patients with advanced cancer admitted to the acute palliative care unit. Seven machine learning models, including extreme gradient boosting, adaptive boosting, gradient boosting, light gradient boosting, logistic regression, support vector machine, and random forest, were evaluated to predict delirium in patients with advanced cancer admitted to the acute palliative care unit. An ensemble approach was adopted to determine the optimal model. For k-fold cross-validation, the combination of extreme gradient boosting and random forest provided the best performance, achieving the following accuracy metrics: 68.83% sensitivity, 70.85% specificity, 69.84% balanced accuracy, and 74.55% area under the receiver operating characteristic curve. The performance of the isolated testing dataset was also validated, and the machine learning model was successfully deployed on a public website ( http://ai-wm.khu.ac.kr/Delirium/ ) to provide public access to delirium prediction results in patients with advanced cancer. Furthermore, using feature importance analysis, sex was determined to be the top contributor in predicting delirium, followed by a history of delirium, chemotherapy, smoking status, alcohol consumption, and living with family. Based on a large-scale, multicenter, patient-based registry cohort, a machine learning prediction model for delirium in patients with advanced cancer was developed in South Korea. We believe that this model will assist healthcare providers in treating patients with delirium and advanced cancer.

Role of Intraoperative Neuromonitoring to Predict Postoperative Delirium in Cardiovascular Surgery

OBJECTIVE

Postoperative delirium (POD) can occur in up to 50% of older patients undergoing cardiovascular surgery, resulting in hospitalization and significant morbidity and mortality. This study aimed to determine whether intraoperative neurophysiologic monitoring (IONM) modalities can be used to predict delirium in patients undergoing cardiovascular surgery.

DESIGN

Adult patients undergoing cardiovascular surgery with IONM between 2019 and 2021 were reviewed retrospectively. Delirium was assessed multiple times using the Intensive Care Delirium Screening Checklist (ICDSC). Patients with an ICDSC score ≥4 were considered to have POD. Significant IONM changes were evaluated based on a visual review of electroencephalography (EEG) and somatosensory evoked potentials data and documentation of significant changes during surgery.

SETTING

University of Pittsburgh Medical Center hospitals.

PARTICIPANTS

Patients 18 years old and older undergoing cardiovascular surgery with IONM monitoring.

MEASUREMENTS AND MAIN RESULTS

Of the 578 patients undergoing cardiovascular surgery with IONM, 126 had POD (21.8%). Significant IONM changes were noted in 134 patients, of whom 49 patients had delirium (36.6%). In contrast, 444 patients had no IONM changes during surgery, of whom 77 (17.3%) patients had POD. Upon multivariate analysis, IONM changes were associated with POD (odds ratio 2.12; 95% CI 1.31-3.44; p < 0.001). Additionally, baseline EEG abnormalities were associated with POD (p = 0.002).

CONCLUSION

Significant IONM changes are associated with an increased risk of POD in patients undergoing cardiovascular surgery. These findings offer a basis for future research and analysis of EEG and somatosensory evoked potential monitoring to predict, detect, and prevent POD.

Assessing the Risk of Developing Delirium on Admission to Inpatient Rehabilitation: A Clinical Prediction Model

OBJECTIVES

To develop a clinical model to predict the risk of an individual patient developing delirium during inpatient rehabilitation, based on patient characteristics and clinical data available on admission.

DESIGN

Retrospective observational study based on electronic health record data.

SETTING AND PARTICIPANTS

We studied a previously validated data set of inpatients including incident delirium episodes during rehabilitation. These patients were admitted to ZURZACH Care, Rehaklinik Bad Zurzach, a Swiss inpatient rehabilitation clinic, between January 1, 2015, and December 31, 2018.

METHODS

We performed logistic regression analysis using backward and forward selection with alpha = 0.01 to remove any noninformative potential predictor. We subsequentially used the Akaike information criterion (AIC) to select the final model among the resulting "intermediate" models. Discrimination of the final prediction model was evaluated using the C-statistic.

RESULTS

Of the 20 candidate predictor variables, 6 were included in the final prediction model: a linear spline of age with 1 knot at 60 years and a linear spline of the functional independence measure (FIM), a measure of the functional degree of patients independency, with 1 knot at 64 points, diagnosis of disorders of fluid, electrolyte, and acid-base balance (E87), use of other analgesic and antipyretics (N02B), use of anti-parkinson drugs (N04B), and an anticholinergic burden score (ACB) of ≥3 points.

CONCLUSIONS AND IMPLICATIONS

Our clinical prediction model could, upon validation, identify patients at risk of incident delirium at admission to inpatient rehabilitation, and thus enable targeted prevention strategies.

Potential Risk Factors for, and Clinical Implications of, Delirium during Inpatient Rehabilitation: A Matched Case-Control Study

OBJECTIVES

To investigate the association between a wide set of baseline characteristics (age, sex, rehabilitation discipline), functional scores [Functional Independence Measure (FIM), cumulative Illness Rating Scale (CIRS)], diseases, and administered drugs and incident delirium in rehabilitation inpatients and, furthermore, to assess clinical implications of developing delirium during rehabilitation.

DESIGN

Matched case-control study based on electronic health record data.

SETTING AND PARTICIPANTS

We studied rehabilitation stays of inpatients admitted between January 1, 2015, and December 31, 2018, to ZURZACH Care, Rehaklinik Bad Zurzach, an inpatient rehabilitation clinic in Switzerland.

METHODS

We conducted unconditional logistic regression analyses to estimate adjusted odds ratios (AORs) with 95% CIs of exposures that were recorded in ≥5 cases and controls.

RESULTS

Among a total of 10,503 rehabilitation stays, we identified 125 validated cases. Older age, undergoing neurologic rehabilitation, a low FIM, and a high CIRS were associated with an increased risk of incident delirium. Being diagnosed with a bacterial infection (AOR 2.62, 95% CI 1.06-6.49), a disorder of fluid, electrolyte, or acid-base balance (AOR 2.76, 95% CI 1.19-6.38), Parkinson's disease (AOR 5.68, 95% CI 2.54-12.68), and administration of antipsychotic drugs (AOR 8.06, 95% CI 4.26-15.22), antiparkinson drugs (AOR 2.86, 95% CI 1.42-5.77), drugs for constipation (AOR 2.11, 95% CI 1.25-3.58), heparins (AOR 2.04, 95% CI 1.29-3.24), or antidepressant drugs (AOR 1.88, 95% CI 1.14-3.10) during rehabilitation, or an increased anticholinergic burden (ACB ≥ 3) (AOR 2.59, 95% CI 1.41-4.73) were also associated with an increased risk of incident delirium.

CONCLUSIONS AND IMPLICATIONS

We identified a set of factors associated with an increased risk of incident delirium during inpatient rehabilitation. Our findings contribute to detect patients at risk of delirium during inpatient rehabilitation.

Delirium in older hospitalized patients-A prospective analysis of the detailed course of delirium in geriatric inpatients

BACKGROUND

Delirium in older hospitalized patients (> 65) is a common clinical syndrome, which is frequently unrecognized.

AIMS

We aimed to describe the detailed clinical course of delirium and related cognitive functioning in geriatric patients in a mainly non-postoperative setting in association with demographic and clinical parameters and additionally to identify risk factors for delirium in this common setting.

METHODS

Inpatients of a geriatric ward were screened for delirium and in the case of presence of delirium included into the study. Patients received three assessments including Mini-Mental-Status-Examination (MMSE) and the Delirium Rating Scale Revised 98 (DRS-R-98). We conducted correlation and linear mixed-effects model analyses to detect associations.

RESULTS

Overall 31 patients (82 years (mean)) met the criteria for delirium and were included in the prospective observational study. Within one week of treatment, mean delirium symptom severity fell below the predefined cut-off. While overall cognitive functioning improved over time, short- and long-term memory deficits remained. Neuroradiological conspicuities were associated with cognitive deficits, but not with delirium severity.

DISCUSSION

The temporal stability of some delirium symptoms (short-/long-term memory, language) on the one hand and on the other hand decrease in others (hallucinations, orientation) shown in our study visualizes the heterogeneity of symptoms attributed to delirium and their different courses, which complicates the differentiation between delirium and a preexisting cognitive decline. The recovery from delirium seems to be independent of preclinical cognitive status.

CONCLUSION

Treatment of the acute medical condition is associated with a fast decrease in delirium severity. Given the high incidence and prevalence of delirium in hospitalized older patients and its detrimental impact on cognition, abilities and personal independence further research needs to be done.

Noise pareidolia test for predicting delirium in hospitalized older patients with cognitive decline

AIM

Although older people are at an increased risk of developing delirium during hospitalization, no definitive screening tools exist to predict the condition. This study aimed to examine the effectiveness of the noise pareidolia test (NPT) as a tool for predicting the onset of post-hospitalization delirium in older adults.

METHODS

Hospitalized patients who were cared for by a multidisciplinary geriatric care team owing to behavioral symptoms, difficulties in communication, and a history of dementia or delirium were analyzed. The NPT was performed on patients who could complete a Mini-Mental State Examination within 3 days of admission. Demographic and clinical data were recorded on the same day as the NPT or within 3 days of admission. Delirium was assessed using the observation-based Delirium Screening Tool (DST).

RESULTS

Of 96 patients, 59 were in the DST-negative group and 37 in the DST-positive group. Benzodiazepine agonist use, serum potassium levels, and the number of images in which pareidolia was noted (i.e., the NPT score) significantly differed between groups. Logistic regression analysis identified benzodiazepine agonist use (odds ratio, 2.897; P = 0.032), serum potassium levels (odds ratio, 0.427; P = 0.041) and NPT scores (odds ratio, 1.253; P = 0.017) as significant predictors of DST results. The receiver operating characteristic curve analysis showed an NPT score of 1 as the appropriate cutoff value.

CONCLUSIONS

A positive NPT score was identified as an independent predictor of delirium in older patients admitted to an acute care hospital with cognitive dysfunction. Geriatr Gerontol Int 2022; ••: ••-••.

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

BACKGROUND

Delirium is thought to be common across various settings of care; however, still little research has been conducted in rehabilitation.

AIM

We investigated the prevalence of delirium, its features and motor subtypes in older patients admitted to rehabilitation facilities during the three editions of the "Delirium Day project".

METHODS

We conducted a cross-sectional study in which 1237 older patients (age ≥ 65 years old) admitted to 50 Italian rehabilitation wards during the three editions of the "Delirium Day project" (2015 to 2017) were included. Delirium was evaluated through the 4AT and its motor subtype with the Delirium Motor Subtype Scale.

RESULTS

Delirium was detected in 226 patients (18%), and the most recurrent motor subtype was mixed (37%), followed by hypoactive (26%), hyperactive (21%) and non-motor one (16%). In a multivariate Poisson regression model with robust variance, factors associated with delirium were: disability in basic (PR 1.48, 95%CI: 1.17-1.9, p value 0.001) and instrumental activities of daily living (PR 1.58, 95%CI: 1.08-2.32, p value 0.018), dementia (PR 2.10, 95%CI: 1.62-2.73, p value < 0.0001), typical antipsychotics (PR 1.47, 95%CI: 1.10-1.95, p value 0.008), antidepressants other than selective serotonin reuptake inhibitors (PR 1.3, 95%CI: 1.02-1.66, p value 0.035), and physical restraints (PR 2.37, 95%CI: 1.68-3.36, p value < 0.0001).

CONCLUSION

This multicenter study reports that 2 out 10 patients admitted to rehabilitations had delirium on the index day. Mixed delirium was the most prevalent subtype. Delirium was associated with unmodifiable (dementia, disability) and modifiable (physical restraints, medications) factors. Identification of these factors should prompt specific interventions aimed to prevent or mitigate delirium.

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

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.

Detecting Incident Delirium within Routinely Collected Inpatient Rehabilitation Data: Validation of a Chart-Based Method

Background: Delirium is a brain condition associated with poor outcomes in rehabilitation. It is therefore important to assess delirium incidence in rehabilitation. Purpose: To develop and validate a chart-based method to identify incident delirium episodes within the electronic database of a Swiss rehabilitation clinic, and to identify a study population of validated incident delirium episodes for further research purposes. Design: Retrospective validation study. Settings: Routinely collected inpatient clinical data from ZURZACH Care. Participants: All patients undergoing rehabilitation at ZURZACH Care, Rehaklinik Bad Zurzach between 2015 and 2018 were included. Methods: Within the study population, we identified all rehabilitation stays for which ≥2 delirium-predictive key words (common terms used to describe delirious patients) were recorded in the medical charts. We excluded all prevalent delirium episodes and defined the remaining episodes to be potentially incident. At least two physicians independently confirmed or refuted each potential incident delirium episode by reviewing the patient charts. We calculated the positive predictive value (PPV) with 95% confidence interval (95% CI) for all potential incident delirium episodes and for specific subgroups. Results: Within 10,515 rehabilitation stays we identified 554 potential incident delirium episodes. Overall, 125 potential incident delirium episodes were confirmed by expert review. The PPV of the chart-based method varied from 0.23 (95% CI 0.19–0.26) overall to 0.69 (95% CI 0.56–0.79) in specific subgroups. Conclusions: Our chart-based method was able to capture incident delirium episodes with low to moderate accuracy. By conducting an additional expert review of the medical charts, we identified a study population of validated incident delirium episodes. Our chart-based method contributes towards an automated detection of potential incident delirium episodes that, supplemented with expert review, efficiently yields a validated population of incident delirium episodes for research purposes.

Neutrophil-Lymphocyte Ratio as a Potential Biomarker for Delirium in the Intensive Care Unit

Background: Recognition and early detection of delirium in the intensive care unit (ICU) is essential to improve ICU outcomes. To date, neutrophil-lymphocyte ratio (NLR), one of inflammatory markers, has been proposed as a potential biomarker for brain disorders related to neuroinflammation. This study aimed to investigate whether NLR could be utilized in early detection of delirium in the ICU. Methods: Of 10,144 patients who admitted to the ICU, 1,112 delirium patients (DE) were included in the current study. To compare among inflammatory markers, NLR, C-reactive protein (CRP), and white blood cell (WBC) counts were obtained: the mean NLR, CRP levels, and WBC counts between the initial day of ICU admission and the day of initial delirium onset within DE were examined. The inflammatory marker of 1,272 non-delirium patients (ND) were also comparatively measured as a supplement. Further comparisons included a subgroup analysis based on delirium subtypes (non-hypoactive vs. hypoactive) or admission types (elective vs. emergent). Results: The NLR and CRP levels in DE increased on the day of delirium onset compared to the initial admission day. ND also showed increased CRP levels on the sixth day (the closest day to average delirium onset day among DE) of ICU admission compared to baseline, while NLR in ND did not show significant difference over time. In further analyses, the CRP level of the non-hypoactive group was more increased than that of the hypoactive group during the delirium onset. NLR, however, was more significantly increased in patients with elective admission than in those with emergent admission. Conclusion: Elevation of NLR was more closely linked to the onset of delirium compared to other inflammatory markers, indicating that NLR may play a role in early detection of delirium.

Initial researches on neuro-functional status and evolution in chronic ethanol consumers with recent traumatic spinal cord injury

We found differences related to the neuro-functional deficiency and clinical progress, among non-consumers and chronic consumers of ethanol, with recent traumatic spinal cord injury (SCI). We present a synthesis of related data on lesion mechanisms in post-traumatic myelogenous disorders, namely some of the alcohols and their actions on the nervous system, with details on the influences exerted, in such afflictions, by the chronic consumption of ethanol. The subject is not frequently approached - according to a literature review with systematic elements, which we have done before - thus constituting a niche that deserves to be further explored. The applicative component of the article highlights statistical data resulted from a retrospective study regarding the specialized casuistry from the Neuromuscular Recovery Clinic of the "Bagdasar Arseni" Emergency Clinical Hospital, following the comparative analysis of two groups of patients with recent SCI: non-consumers - the control group (n=780) - and chronic ethanol consumers - the study group (n=225) - with the addition of a prospective pilot component. Data processing has been achieved with SPSS 24. The American Spinal Injury Association Impairment Scale (AIS) mean motor scores differ significantly (tests: Mann-Whitney and t) between the control and study group in favor of the second, both at admission (p<0.001) and at discharge (p<0.001). AIS mean sensitive scores differ between the two lots, and also in favor of the study, but statistically significant only at discharge (p=0.048); the difference at admission is not significant (p=0.51) - possibly because of alcoholic-nutritional polyneuropathy. These findings, with numerous related details, later presented in the text, are surprising, which requires further studies and attempts of understanding.