Journal articles: 'Delirium prediction'

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Linkaitė, Gabrielė, Mantas Riauka, Ignė Bunevičiūtė, and Saulius Vosylius. "Evaluation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for the patients in the intensive care unit." Acta medica Lituanica 25, no. 1 (May 14, 2018): 14–22. http://dx.doi.org/10.6001/actamedica.v25i1.3699.

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Introduction. Delirium not only compromises patient care, but is also associated with poorer outcomes: increased duration of mechanical ventilation, higher mortality, and greater long-term cognitive dysfunction. The PRE-DELIRIC model is a tool used to calculate the risk of the development of delirium. The classification of the patients into groups by risk allows efficient initiation of preventive measures. The goal of this study was to validate the PRE-DELIRIC model using the CAM-ICU (The Confusion Assessment Method for the Intensive Care Unit) method for the diagnosis of delirium. Materials and methods. Patients admitted to the University Hospital of Vilnius during February 2015 were enrolled. Every day, data were collected for APACHE-II and PRE-DELIRIC scores. Out of 167 patients, 38 (23%) were included and screened using the CAM-ICU method within 24 hours of admission to the ICU. We defined patients as having delirium when they had at least one positive CAM-ICU screening or haloperidol administration due to sedation. To validate the PRE-DELIRIC model, we calculated the area under receiver operating characteristic curve. Results. The mean age of the patients was 69.2 ± 17.2 years, 19 (50%) were male, APACHE-II mean score 18.0 ± 7.4 points. Delirium was diagnosed in 22 (58%) of 38 patients. Data used for validation of the PRE-DELIRIC model resulted in an area under the curve of 0.713 (p < 0.05, 95% CI 0.539–0.887); sensitivity and specificity for the patients with 20% risk were, accordingly, 77.3% and 50%; 40% risk – 45.5% and 81.3%, 60% – 36.4%, and 87.5%. Conclusions. The PRE-DELIRIC model predicted delirium in the patients within 24 hours of admission to the ICU. Preventive therapy could be efficiently targeted at high-risk patients if both of the methods are to be implemented.

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Jauk, Stefanie, Diether Kramer, Birgit Großauer, Susanne Rienmüller, Alexander Avian, Andrea Berghold, Werner Leodolter, and Stefan Schulz. "Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study." Journal of the American Medical Informatics Association 27, no. 9 (September 1, 2020): 1383–92. http://dx.doi.org/10.1093/jamia/ocaa113.

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Abstract Objective Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest–based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting. Materials and Methods Delirium was predicted at admission and recalculated on the evening of admission. The defined prediction outcome was a delirium coded for the recent hospital stay. During 7 months of prospective evaluation, 5530 predictions were analyzed. In addition, 119 predictions for internal medicine patients were compared with ratings of clinical experts in a blinded and nonblinded setting. Results During clinical application, the algorithm achieved a sensitivity of 74.1% and a specificity of 82.2%. Discrimination on prospective data (area under the receiver-operating characteristic curve = 0.86) was as good as in the test dataset, but calibration was poor. The predictions correlated strongly with delirium risk perceived by experts in the blinded (r = 0.81) and nonblinded (r = 0.62) settings. A major advantage of our setting was the timely prediction without additional data entry. Discussion The implemented machine learning algorithm achieved a stable performance predicting delirium in high agreement with expert ratings, but improvement of calibration is needed. Future research should evaluate the acceptance of implemented machine learning algorithms by health professionals. Conclusions Our study provides new insights into the implementation process of a machine learning algorithm into a clinical workflow and demonstrates its predictive power for delirium.

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Amerongen, Hilde van Nieuw, Sandra Stapel, Jan Jaap Spijkstra, Dagmar Ouweneel, and Jimmy Schenk. "Comparison of Prognostic Accuracy of 3 Delirium Prediction Models." American Journal of Critical Care 32, no. 1 (January 1, 2023): 43–50. http://dx.doi.org/10.4037/ajcc2023213.

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Background Delirium is a severe complication in critical care patients. Accurate prediction could facilitate determination of which patients are at risk. In the past decade, several delirium prediction models have been developed. Objectives To compare the prognostic accuracy of the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, and to investigate the difference in prognostic accuracy of the PRE-DELIRIC model between patients receiving and patients not receiving mechanical ventilation. Methods This retrospective study involved adult patients admitted to the intensive care unit during a 2-year period. Delirium was assessed by using the Confusion Assessment Method for the Intensive Care Unit or any administered dose of haloperidol or quetiapine. Model discrimination was assessed by calculating the area under the receiver operating characteristic curve (AUC); values were compared using the DeLong test. Results The study enrolled 1353 patients. The AUC values were calculated as 0.716 (95% CI, 0.688–0.745), 0.681 (95% CI, 0.650–0.712), and 0.660 (95% CI, 0.629–0.691) for the PRE-DELIRIC, E-PRE-DELIRIC, and Lanzhou models, respectively. The difference in model discrimination was statistically significant for comparison of the PRE-DELIRIC with the E-PRE-DELIRIC (AUC difference, 0.035; P = .02) and Lanzhou models (AUC difference, 0.056; P < .001). In the PRE-DELIRIC model, the AUC was 0.711 (95% CI, 0.680–0.743) for patients receiving mechanical ventilation and 0.664 (95% CI, 0.586–0.742) for those not receiving it (difference, 0.047; P = .27). Conclusion Statistically significant differences in prognostic accuracy were found between delirium prediction models. The PRE-DELIRIC model was the best-performing model and can be used in patients receiving or not receiving mechanical ventilation.

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Pagali, Sandeep R., Donna Miller, Karen Fischer, Darrell Schroeder, Norman Egger, Dennis M. Manning, Maria I. Lapid, Robert J. Pignolo, and M. Caroline Burton. "Predicting Delirium Risk Using an Automated Mayo Delirium Prediction Tool." Mayo Clinic Proceedings 96, no. 5 (May 2021): 1229–35. http://dx.doi.org/10.1016/j.mayocp.2020.08.049.

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Matsumoto, Koutarou, Yasunobu Nohara, Mikako Sakaguchi, Yohei Takayama, Shota Fukushige, Hidehisa Soejima, and Naoki Nakashima. "Delirium Prediction Using Machine Learning Interpretation Method and Its Incorporation into a Clinical Workflow." Applied Sciences 13, no. 3 (January 25, 2023): 1564. http://dx.doi.org/10.3390/app13031564.

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Delirium in hospitalized patients is a worldwide problem, causing a burden on healthcare professionals and impacting patient prognosis. A machine learning interpretation method (ML interpretation method) presents the results of machine learning predictions and promotes guided decisions. This study focuses on visualizing the predictors of delirium using a ML interpretation method and implementing the analysis results in clinical practice. Retrospective data of 55,389 patients hospitalized in a single acute care center in Japan between December 2017 and February 2022 were collected. Patients were categorized into three analysis populations, according to inclusion and exclusion criteria, to develop delirium prediction models. The predictors were then visualized using Shapley additive explanation (SHAP) and fed back to clinical practice. The machine learning-based prediction of delirium in each population exhibited excellent predictive performance. SHAP was used to visualize the body mass index and albumin levels as critical contributors to delirium prediction. In addition, the cutoff value for age, which was previously unknown, was visualized, and the risk threshold for age was raised. By using the SHAP method, we demonstrated that data-driven decision support is possible using electronic medical record data.

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Matsuoka, Ayaka, Toru Miike, Mariko Miyazaki, Taku Goto, Akira Sasaki, Hirotaka Yamazaki, Moe Komaki, et al. "Development of a delirium predictive model for adult trauma patients in an emergency and critical care center: a retrospective study." Trauma Surgery & Acute Care Open 6, no. 1 (November 2021): e000827. http://dx.doi.org/10.1136/tsaco-2021-000827.

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BackgroundDelirium has been shown to prolong the length of intensive care unit stay, hospitalization, and duration of ventilatory control, in addition to increasing the use of sedatives and increasing the medical costs. Although there have been a number of reports referring to risk factors for the development of delirium, no model has been developed to predict delirium in trauma patients at the time of admission. This study aimed to create a scoring system that predicts delirium in trauma patients.MethodsIn this single-center, retrospective, observational study, trauma patients aged 18 years and older requiring hospitalization more than 48 hours were included and divided into the development and validation cohorts. Univariate analysis was performed in the development cohort to identify factors significantly associated with prediction of delirium. The final scoring system for predicting delirium was developed using multivariate analysis and internal validation was performed.ResultsOf the 308 patients in the development cohort, 91 developed delirium. Clinical Frailty Score, fibrin/fibrinogen degradation products, low body mass index, lactate level, and Glasgow Coma Scale score were independently associated with the development of delirium. We developed a scoring system using these factors and calculated the delirium predictive score, which had an area under the curve of 0.85. In the validation cohort, 46 of 206 patients developed delirium. The area under the curve for the validation cohort was 0.86, and the calibration plot analysis revealed the scoring system was well calibrated in the validation cohort.DiscussionThis scoring system for predicting delirium in trauma patients consists of only five risk factors. Delirium prediction at the time of admission may be useful in clinical practice.Level of evidencePrognostic and epidemiological, level III.

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Boettger, Soenke, Rafael Meyer, André Richter, Susana Franco Fernandez, Alain Rudiger, Maria Schubert, Josef Jenewein, and David Garcia Nuñez. "Screening for delirium with the Intensive Care Delirium Screening Checklist (ICDSC): Symptom profile and utility of individual items in the identification of delirium dependent on the level of sedation." Palliative and Supportive Care 17, no. 1 (May 24, 2018): 74–81. http://dx.doi.org/10.1017/s1478951518000202.

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AbstractObjectiveThe importance of the proper identification of delirium, with its high incidence and adversities in the intensive care setting, has been widely recognized. One common screening instrument is the Intensive Care Delirium Screening Checklist (ICDSC); however, the symptom profile and key features of delirium dependent on the level of sedation have not yet been evaluated.MethodIn this prospective cohort study, the ICDSC was evaluated versus the Diagnostic and Statistical Manual, 4th edition, text revision, diagnosis of delirium set as standard with respect to the symptom profile, and correct identification of delirium. The aim of this study was to identify key features of delirium in the intensive care setting dependent on the Richmond Agitation and Sedation Scale levels of sedation: drowsiness versus alert and calmness.ResultThe 88 delirious patients of 225 were older, had more severe disease, and prolonged hospitalization. Irrespective of the level of sedation, delirium was correctly classified by items related to inattention, disorientation, psychomotor alterations, inappropriate speech or mood, and symptom fluctuation. In the drowsy patients, inattention reached substantial sensitivity and specificity, whereas psychomotor alterations and sleep-wake cycle disturbances were sensitive lacked specificity. The positive prediction was substantial across items, whereas the negative prediction was only moderate. In the alert and calm patient, the sensitivities were substantial for psychomotor alterations, sleep-wake cycle disturbances, and symptom fluctuations; however, these fluctuations were not specific. The positive prediction was moderate and the negative prediction substantial. Between the nondelirious drowsy and alert, the symptom profile was similar; however, drowsiness was associated with alterations in consciousness.Significance of resultsIn the clinical routine, irrespective of the level of sedation, delirium was characterized by the ICDSC items for inattention, disorientation, psychomotor alterations, inappropriate speech or mood and symptom fluctuation. Further, drowsiness caused altered levels of consciousness.

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Green, Cameron, William Bonavia, Candice Toh, and Ravindranath Tiruvoipati. "Prediction of ICU Delirium." Critical Care Medicine 47, no. 3 (March 2019): 428–35. http://dx.doi.org/10.1097/ccm.0000000000003577.

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Kotfis, Katarzyna, Marta Bott-Olejnik, Aleksandra Szylińska, and Iwona Rotter. "Could Neutrophil-to-Lymphocyte Ratio (NLR) Serve as a Potential Marker for Delirium Prediction in Patients with Acute Ischemic Stroke? A Prospective Observational Study." Journal of Clinical Medicine 8, no. 7 (July 22, 2019): 1075. http://dx.doi.org/10.3390/jcm8071075.

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Delirium is an acute brain disorder that commonly occurs in patients with acute ischemic stroke (AIS). Pathomechanism of delirium is related to the neuroinflammatory process and oxidative stress. Search for readily available diagnostic marker that will aid clinicians in early identification of delirium is ongoing. The aim of this study was to investigate whether neutrophil-to-lymphocyte ratio (NLR) could serve as a potential marker for delirium prediction in patients with AIS and to find an easy diagnostic tool using laboratory and clinical parameters to predict delirium. Prospective observational study (NCT03944694) included patients with AIS admitted to the neurology department of a district general hospital. All patients were screened for delirium using CAM-ICU (Confusion Assessment Method for Intensive Care Unit). Demographic and medical history data and admission lab results, including differential white blood cell analysis, were collected from all patients. We included 1001 patients in the final analysis. The mean age of the sample was 71 years, and 52% of patients were males. The incidence of early-onset delirium was 17.2%. The NLR was elevated in delirious patients (6.39 ± 8.60 vs. 4.61 ± 5.61, p < 0.001). The best cut-off value of NLR to predict delirium using the receiver operating characteristics (ROC) was determined at 4.86. Multivariable logistic regression analysis showed that the odds ratio (OR) for developing delirium with NLR > 4.86 (adjusted for age, sex, body mass index (BMI), comorbidities, and baseline neurology) was 1.875 (95% CI 1.314–2.675, p = 0.001). As a result of different combinations of markers and clinical parameters based on logistic regression, a formula—DELirium in Acute Ischemic Stroke (DELIAS score)—was obtained with the area under the ROC curve of 0.801 (p < 0.001). After regression of the cut-off points of the obtained curve, a significant correlation of the DELIAS score was observed with the occurrence of early-onset delirium (OR = 8.976, p < 0.001) and with delirium until the fifth day after AIS (OR = 7.744, p < 0.001). In conclusion, NLR can be regarded as a potential marker for prediction of early-onset delirium after AIS. On the basis of combined laboratory and clinical parameters, the DELIAS score was calculated, which gave the highest predictive value for delirium in the analyzed group of patients after ischemic stroke. However, further studies are needed to validate these findings.

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Oosterhoff, Jacobien H. F., Aditya V. Karhade, Tarandeep Oberai, Esteban Franco-Garcia, Job N. Doornberg, and Joseph H. Schwab. "Prediction of Postoperative Delirium in Geriatric Hip Fracture Patients: A Clinical Prediction Model Using Machine Learning Algorithms." Geriatric Orthopaedic Surgery & Rehabilitation 12 (January 2021): 215145932110622. http://dx.doi.org/10.1177/21514593211062277.

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Introduction Postoperative delirium in geriatric hip fracture patients adversely affects clinical and functional outcomes and increases costs. A preoperative prediction tool to identify high-risk patients may facilitate optimal use of preventive interventions. The purpose of this study was to develop a clinical prediction model using machine learning algorithms for preoperative prediction of postoperative delirium in geriatric hip fracture patients. Materials & Methods Geriatric patients undergoing operative hip fracture fixation were queried in the American College of Surgeons National Surgical Quality Improvement Program database (ACS NSQIP) from 2016 through 2019. A total of 28 207 patients were included, of which 8030 (28.5%) developed a postoperative delirium. First, the dataset was randomly split 80:20 into a training and testing subset. Then, a random forest (RF) algorithm was used to identify the variables predictive for a postoperative delirium. The machine learning-model was developed on the training set and the performance was assessed in the testing set. Performance was assessed by discrimination (c-statistic), calibration (slope and intercept), overall performance (Brier-score), and decision curve analysis. Results The included variables identified using RF algorithms were (1) age, (2) ASA class, (3) functional status, (4) preoperative dementia, (5) preoperative delirium, and (6) preoperative need for mobility-aid. The clinical prediction model reached good discrimination (c-statistic = .79), almost perfect calibration (intercept = −.01, slope = 1.02), and excellent overall model performance (Brier score = .15). The clinical prediction model was deployed as an open-access web-application: https://sorg-apps.shinyapps.io/hipfxdelirium/ . Discussion & Conclusions We developed a clinical prediction model that shows promise in estimating the risk of postoperative delirium in geriatric hip fracture patients. The clinical prediction model can play a beneficial role in decision-making for preventative measures for patients at risk of developing a delirium. If found to be externally valid, clinicians might use the available web-based application to help incorporate the model into clinical practice to aid decision-making and optimize preoperative prevention efforts.

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Khan, Ariba, Kayla Heslin, Michelle Simpson, and Michael Malone. "Electronic Health Record Data Can be Used at the Bedside to Identify Older Hospitalized Patients With Delirium." Innovation in Aging 4, Supplement_1 (December 1, 2020): 136. http://dx.doi.org/10.1093/geroni/igaa057.447.

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Abstract Delirium is a serious condition that is often underrecognized. Several delirium predictive rules can assist in early detection. The coupling of prediction rules with features of the EHR are in their infancy but hold potential. This study aimed to determine variables within the EHR that can be used to identify older hospitalized patients with delirium. This is a prospective study among patients >=65 years admitted to the hospital. Researchers screened daily for delirium using the 3-D CAM. Predictive variables were extracted from the EHR. Basic descriptive statistics were conducted. Chi-squared and Fischer’s exact tests were used to compare differences among those diagnosed with or without delirium as appropriate; binary logistic regression was used for multivariate modeling. Among 408 participants, mean age was 75 years, 61% were female, and 83% were black. The overall rate of delirium was 16.7% (prevalent delirium 10.5%; incident delirium 6.1%). There was no statistical difference in 30-day mortality (2.9% vs. 2.7%) or 30-day readmission (13.2% vs. 14.7%) rates between those with and without delirium (both P>0.05). Even so, patients with delirium were older, more likely to have a diagnosis of infection and/or cognitive impairment, as well as increased severity of illness (all P’s <0.05). Moreover, patients with delirium had a lower Braden score and higher Morse fall score (both P’s <0.01). In multivariate analysis, cognitive impairment (OR 5.49; 95% CI 2.77-10.87) and lower Braden scores (OR 1.29; 95% CI 1.18-1.41) remained significant predictors of delirium. Further research is needed to develop an automated EHR prediction model.

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Rozner, M. A. "Preoperative prediction of postoperative delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (May 25, 1994): 1573a—1574. http://dx.doi.org/10.1001/jama.271.20.1573a.

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Carson, R. C. "Preoperative prediction of postoperative delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (May 25, 1994): 1573b—1573. http://dx.doi.org/10.1001/jama.271.20.1573b.

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O'Hara, D. A. "Preoperative prediction of postoperative delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (May 25, 1994): 1573c—1573. http://dx.doi.org/10.1001/jama.271.20.1573c.

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Carson, Richard C. "Preoperative Prediction of Postoperative Delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (May 25, 1994): 1573. http://dx.doi.org/10.1001/jama.1994.03510440033015.

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O'Hara, Dorene A. "Preoperative Prediction of Postoperative Delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (May 25, 1994): 1573. http://dx.doi.org/10.1001/jama.1994.03510440033016.

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Rozner, Marc A. "Preoperative Prediction of Postoperative Delirium." JAMA: The Journal of the American Medical Association 271, no. 20 (May 25, 1994): 1573. http://dx.doi.org/10.1001/jama.1994.03510440033017.

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McCusker, Jane, Martin G. Cole, Philippe Voyer, Johanne Monette, Nathalie Champoux, Antonio Ciampi, Minh Vu, and Eric Belzile. "Use of nurse-observed symptoms of delirium in long-term care: effects on prevalence and outcomes of delirium." International Psychogeriatrics 23, no. 4 (September 30, 2010): 602–8. http://dx.doi.org/10.1017/s1041610210001900.

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ABSTRACTBackground: Previous studies have reported that nurse detection of delirium has low sensitivity compared to a research diagnosis. As yet, no study has examined the use of nurse-observed delirium symptoms combined with research-observed delirium symptoms to diagnose delirium. Our specific aims were: (1) to describe the effect of using nurse-observed symptoms on the prevalence of delirium symptoms and diagnoses in long-term care (LTC) facilities, and (2) to compare the predictive validity of delirium diagnoses based on the use of research-observed symptoms alone with those based on research-observed and nurse-observed symptoms.Methods: Residents aged 65 years and over of seven LTC facilities were recruited into a prospective study. Using the Confusion Assessment Method (CAM), research assistants (RAs) interviewed residents and nurses to assess delirium symptoms. Delirium symptoms were also abstracted independently from nursing notes. Outcomes measured at five month follow-up were: death, the Hierarchic Dementia Scale (HDS), the Barthel ADL scale, and a composite outcome measure (death, or a 10-point decline in either the HDS or the ADL score).Results: The prevalence of delirium among 235 LTC residents increased from 14.0% (using research-observed symptoms only) to 24.7% (using research- and nurse-observed symptoms). The relative risks (and 95% confidence intervals) for prediction of the composite outcome, after adjustment for covariates, were: 1.43 (0.88, 1.96) for delirium using research-observed symptoms only; 1.77 (1.13, 2.28) for delirium using research- and nurse-observed symptoms, in comparison with no delirium.Conclusions: The inclusion of delirium symptoms observed by nurses not only increases the detection of delirium in LTC facilities but improves the prediction of outcomes.

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Mufti, Hani Nabeel, Gregory Marshal Hirsch, Samina Raza Abidi, and Syed Sibte Raza Abidi. "Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study." JMIR Medical Informatics 7, no. 4 (October 23, 2019): e14993. http://dx.doi.org/10.2196/14993.

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Background Delirium is a temporary mental disorder that occasionally affects patients undergoing surgery, especially cardiac surgery. It is strongly associated with major adverse events, which in turn leads to increased cost and poor outcomes (eg, need for nursing home due to cognitive impairment, stroke, and death). The ability to foresee patients at risk of delirium will guide the timely initiation of multimodal preventive interventions, which will aid in reducing the burden and negative consequences associated with delirium. Several studies have focused on the prediction of delirium. However, the number of studies in cardiac surgical patients that have used machine learning methods is very limited. Objective This study aimed to explore the application of several machine learning predictive models that can pre-emptively predict delirium in patients undergoing cardiac surgery and compare their performance. Methods We investigated a number of machine learning methods to develop models that can predict delirium after cardiac surgery. A clinical dataset comprising over 5000 actual patients who underwent cardiac surgery in a single center was used to develop the models using logistic regression, artificial neural networks (ANN), support vector machines (SVM), Bayesian belief networks (BBN), naïve Bayesian, random forest, and decision trees. Results Only 507 out of 5584 patients (11.4%) developed delirium. We addressed the underlying class imbalance, using random undersampling, in the training dataset. The final prediction performance was validated on a separate test dataset. Owing to the target class imbalance, several measures were used to evaluate algorithm’s performance for the delirium class on the test dataset. Out of the selected algorithms, the SVM algorithm had the best F1 score for positive cases, kappa, and positive predictive value (40.2%, 29.3%, and 29.7%, respectively) with a P=.01, .03, .02, respectively. The ANN had the best receiver-operator area-under the curve (78.2%; P=.03). The BBN had the best precision-recall area-under the curve for detecting positive cases (30.4%; P=.03). Conclusions Although delirium is inherently complex, preventive measures to mitigate its negative effect can be applied proactively if patients at risk are prospectively identified. Our results highlight 2 important points: (1) addressing class imbalance on the training dataset will augment machine learning model’s performance in identifying patients likely to develop postoperative delirium, and (2) as the prediction of postoperative delirium is difficult because it is multifactorial and has complex pathophysiology, applying machine learning methods (complex or simple) may improve the prediction by revealing hidden patterns, which will lead to cost reduction by prevention of complications and will optimize patients’ outcomes.

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Heesakkers, Hidde, John W. Devlin, Arjen J. C. Slooter, and Mark van den Boogaard. "Association between delirium prediction scores and days spent with delirium." Journal of Critical Care 58 (August 2020): 6–9. http://dx.doi.org/10.1016/j.jcrc.2020.03.008.

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Ling, Yu-Ting, Qian-Qian Guo, Si-Min Wang, Li-Nan Zhang, Jin-Hua Chen, Yi Liu, Ruo-Heng Xuan, et al. "Nomogram for Prediction of Postoperative Delirium after Deep Brain Stimulation of Subthalamic Nucleus in Parkinson’s Disease under General Anesthesia." Parkinson's Disease 2022 (November 29, 2022): 1–12. http://dx.doi.org/10.1155/2022/6915627.

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Introduction. Postoperative delirium can increase cognitive impairment and mortality in patients with Parkinson’s disease. The purpose of this study was to develop and internally validate a clinical prediction model of delirium after deep brain stimulation of the subthalamic nucleus in Parkinson’s disease under general anesthesia. Methods. We conducted a retrospective observational cohort study on the data of 240 patients with Parkinson’s disease who underwent deep brain stimulation of the subthalamic nucleus under general anesthesia. Demographic characteristics, clinical evaluation, imaging data, laboratory data, and surgical anesthesia information were collected. Multivariate logistic regression was used to develop the prediction model for postoperative delirium. Results. A total of 159 patients were included in the cohort, of which 38 (23.90%) had postoperative delirium. Smoking (OR 4.51, 95% CI 1.56–13.02, p < 0.01 ) was the most important risk factor; other independent predictors were orthostatic hypotension (OR 3.42, 95% CI 0.90–13.06, p = 0.07 ), inhibitors of type-B monoamine oxidase (OR 3.07, 95% CI 1.17–8.04, p = 0.02 ), preoperative MRI with silent brain ischemia or infarction (OR 2.36, 95% CI 0.90–6.14, p = 0.08 ), Hamilton anxiety scale score (OR 2.12, 95% CI 1.28–3.50, p < 0.01 ), and apolipoprotein E level in plasma (OR 1.48, 95% CI 0.95–2.29, p = 0.08 ). The area under the receiver operating characteristic curve (AUC) was 0.76 (95% CI 0.66–0.86). A nomogram was established and showed good calibration and clinical predictive capacity. After bootstrap for internal verification, the AUC was 0.74 (95% CI 0.66–0.83). Conclusion. This study provides evidence for the independent inducing factors of delirium after deep brain stimulation of the subthalamic nucleus in Parkinson’s disease under general anesthesia. By predicting the development of delirium, our model may identify high-risk groups that can benefit from early or preventive intervention.

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Wang, Gang, Lei Zhang, Ying Qi, Guangjian Chen, Juan Zhou, Huihui Zhu, and Yingxin Hao. "Development and Validation of a Postoperative Delirium Prediction Model for Elderly Orthopedic Patients in the Intensive Care Unit." Journal of Healthcare Engineering 2021 (June 8, 2021): 1–6. http://dx.doi.org/10.1155/2021/9959077.

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We developed a prediction model for delirium in elderly patients in the intensive care unit who underwent orthopedic surgery and then temporally validated its predictive power in the same hospital. In the development stage, we designed a prospective cohort study, and 319 consecutive patients aged over 65 years from January 2018 to December 2019 were screened. Demographic characteristics and clinical variables were evaluated, and a final prediction model was developed using the multivariate logistic regression analysis. In the validation stage, 108 patients were included for temporal validation between January 2020 and June 2020. The effectiveness of the model was evaluated through discrimination and calibration. As a result, the prediction model contains seven risk factors (age, anesthesia method, score of mini-mental state examination, hypoxia, major hemorrhage, level of interleukin-6, and company of family members), which had an area under the receiver operating characteristics curve of 0.82 (95% confidence interval 0.76–0.88) and was stable after bootstrapping. The temporal validation resulted in an area under the curve of 0.80 (95% confidence interval 0.67–0.93). Our prediction model had excellent discrimination power in predicting postoperative delirium in elderly patients and could assist intensive care physicians with early prevention.

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Boogaard, M. v. d., P. Pickkers, A. J. C. Slooter, M. A. Kuiper, P. E. Spronk, P. H. J. v. d. Voort, J. G. v. d. Hoeven, R. Donders, T. v. Achterberg, and L. Schoonhoven. "Development and validation of PRE-DELIRIC (PREdiction of DELIRium in ICu patients) delirium prediction model for intensive care patients: observational multicentre study." BMJ 344, feb09 3 (February 9, 2012): e420-e420. http://dx.doi.org/10.1136/bmj.e420.

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Ho, Mu-Hsing, Kee-Hsin Chen, Jed Montayre, Megan F. Liu, Chia-Chi Chang, Victoria Traynor, Shu-Tai Shen Hsiao, Hui-Chen (Rita) Chang, and Hsiao-Yean Chiu. "Diagnostic test accuracy meta-analysis of PRE-DELIRIC (PREdiction of DELIRium in ICu patients): A delirium prediction model in intensive care practice." Intensive and Critical Care Nursing 57 (April 2020): 102784. http://dx.doi.org/10.1016/j.iccn.2019.102784.

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Neefjes, Elisabeth, Maurice Van Der Vorst, Bertha Verdegaal, Aartjan TF Beekman, Johannes Berkhof, and Henk M. W. Verheul. "Identification of patients at risk for delirium on a medical oncology hospital ward." Journal of Clinical Oncology 32, no. 31_suppl (November 1, 2014): 130. http://dx.doi.org/10.1200/jco.2014.32.31_suppl.130.

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130 Background: Delirium is a distressing experience for patients with cancer. Incidence rates of delirium vary between 5 and 88 percent. We studied the incidence of delirium on our medical oncology ward, along with its predisposing and precipitating factors, in order to identify patients who may benefit from screening and early interventions. Methods: We evaluated patients admitted to our medical oncology ward between January 2011 and June 2012 for delirium. In this period a screening program with the Delirium Observation Screening Scale was initiated. Risk factors for delirium were extracted from the patient’s chart. We developed a prediction model to identify patients who are at risk to develop delirium and optimized this model with a cohort of patients with a delirium diagnosed between June 2012 and September 2013. Results: 1,733 admittances of 574 individual patients were recorded in the study period. Sixty episodes of delirium were identified in 52 patients. The patients had a mean age of 60 years, and most patients (70%) had advanced cancer. The most prevalent predisposing and precipitating factors were age >70, drug intoxication, infection and metabolic imbalances (abnormalities in sodium, potassium, calcium, albumin or glucose levels), which were present in 21, 25, 22, and 18 percent, respectively. The prediction model revealed that patients who were electively admitted had a very low risk to develop delirium (1%), but patients admitted for an emergency with at least one metabolic abnormality, such as hyperkalemia, were at high risk for developing a delirium (delirium risk 33%). Conclusions: Based on our analyses for risk factors of delirium, we developed a new prediction model for the risk for delirium in patients with cancer admitted to an oncology ward that may be used for targeted screening and to study preventive therapy in order to improve their quality of life.

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Marcantonio, Edward R. "Preoperative Prediction of Postoperative Delirium-Reply." JAMA: The Journal of the American Medical Association 271, no. 20 (May 25, 1994): 1574. http://dx.doi.org/10.1001/jama.1994.03510440033018.

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Sweerts, Lieke, Pepijn Dekkers, Philip van der Wees, Job van Susante, Lex de Jong, Thomas Hoogeboom, and Sebastiaan van de Groes. "External Validation of Prediction Models for Surgical Complications in People Considering Total Hip or Knee Arthroplasty Was Successful for Delirium but Not for Surgical Site Infection, Postoperative Bleeding, and Nerve Damage: A Retrospective Cohort Study." Journal of Personalized Medicine 13, no. 2 (January 31, 2023): 277. http://dx.doi.org/10.3390/jpm13020277.

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Although several models for the prediction of surgical complications after primary total hip or total knee replacement (THA and TKA, respectively) are available, only a few models have been externally validated. The aim of this study was to externally validate four previously developed models for the prediction of surgical complications in people considering primary THA or TKA. We included 2614 patients who underwent primary THA or TKA in secondary care between 2017 and 2020. Individual predicted probabilities of the risk for surgical complication per outcome (i.e., surgical site infection, postoperative bleeding, delirium, and nerve damage) were calculated for each model. The discriminative performance of patients with and without the outcome was assessed with the area under the receiver operating characteristic curve (AUC), and predictive performance was assessed with calibration plots. The predicted risk for all models varied between <0.01 and 33.5%. Good discriminative performance was found for the model for delirium with an AUC of 84% (95% CI of 0.82–0.87). For all other outcomes, poor discriminative performance was found; 55% (95% CI of 0.52–0.58) for the model for surgical site infection, 61% (95% CI of 0.59–0.64) for the model for postoperative bleeding, and 57% (95% CI of 0.53–0.61) for the model for nerve damage. Calibration of the model for delirium was moderate, resulting in an underestimation of the actual probability between 2 and 6%, and exceeding 8%. Calibration of all other models was poor. Our external validation of four internally validated prediction models for surgical complications after THA and TKA demonstrated a lack of predictive accuracy when applied in another Dutch hospital population, with the exception of the model for delirium. This model included age, the presence of a heart disease, and the presence of a disease of the central nervous system as predictor variables. We recommend that clinicians use this simple and straightforward delirium model during preoperative counselling, shared decision-making, and early delirium precautionary interventions.

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Al-Hoodar, Rasha Khamis, Eilean Rathinasamy Lazarus, Omar Alomari, and Omar Alzaabi. "Development of a Delirium Risk Predication Model among ICU Patients in Oman." Anesthesiology Research and Practice 2022 (July 31, 2022): 1–6. http://dx.doi.org/10.1155/2022/1449277.

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Background. Delirium is a common disorder among patients admitted to intensive care units. Identification of the predicators of delirium is very important to improve the patient’s quality of life. Methods. This study was conducted in a prospective observational design to build a predictive model for delirium among ICU patients in Oman. A sample of 153 adult ICU patients from two main hospitals participated in the study. The Intensive Care Delirium Screening Checklist (ICDSC) was used to assess the participants for delirium twice daily. Result. The results showed that the incidence of delirium was 26.1%. Multiple logistic regression analysis showed that sepsis (odds ratio (OR) = 9.77; 95% confidence interval (CI) = 1.91–49.92; P < 0.006 ), metabolic acidosis (odds ratio (OR) = 3.45; 95% confidence interval [CI] = 1.18–10.09; P = 0.024 ), nasogastric tube use (odds ratio (OR) 9.74; 95% confidence interval (CI) = 3.48–27.30; P ≤ 0.001 ), and APACHEII score (OR = 1.22; 95% CI = 1.09–1.37; P ≤ 0.001 ) were predictors of delirium among ICU patients in Oman (R2=0.519, adjusted R2=0.519, P ≤ 0.001 ). Conclusion. To prevent delirium in Omani hospitals, it is necessary to work on correcting those predictors and identifying other factors that had effects on delirium development. Designing of a prediction model may help on early delirium detection and implementation of preventative measures.

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Mestres Gonzalvo, Carlota, Hugo A. J. M. de Wit, Brigit P. C. van Oijen, Debbie S. Deben, Kim P. G. M. Hurkens, Wubbo J. Mulder, Rob Janknegt, et al. "Validation of an automated delirium prediction model (DElirium MOdel (DEMO)): an observational study." BMJ Open 7, no. 11 (November 2017): e016654. http://dx.doi.org/10.1136/bmjopen-2017-016654.

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ObjectivesDelirium is an underdiagnosed, severe and costly disorder, and 30%–40% of cases can be prevented. A fully automated model to predict delirium (DEMO) in older people has been developed, and the objective of this study is to validate the model in a hospital setting.SettingSecondary care, one hospital with two locations.DesignObservational study.ParticipantsThe study included 450 randomly selected patients over 60 years of age admitted to Zuyderland Medical Centre. Patients who presented with delirium on admission were excluded.Primary outcome measuresDevelopment of delirium through chart review.ResultsA total of 383 patients were included in this study. The analysis was performed for delirium within 1, 3 and 5 days after a DEMO score was obtained. Sensitivity was 87.1% (95% CI 0.756 to 0.939), 84.2% (95% CI 0.732 to 0.915) and 82.7% (95% CI 0.734 to 0.893) for 1, 3 and 5 days, respectively, after obtaining the DEMO score. Specificity was 77.9% (95% CI 0.729 to 0.882), 81.5% (95% CI 0.766 to 0.856) and 84.5% (95% CI 0.797 to 0.884) for 1, 3 and 5 days, respectively, after obtaining the DEMO score.ConclusionDEMO is a satisfactory prediction model but needs further prospective validation with in-person delirium confirmation. In the future, DEMO will be applied in clinical practice so that physicians will be aware of when a patient is at an increased risk of developing delirium, which will facilitate earlier recognition and diagnosis, and thus will allow the implementation of prevention measures.

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Hanison, J., S. Umar, K. Acharya, and D. Conway. "Evaluation of the PRE-DELIRIC delirium prediction tool on a general ICU." Critical Care 19, Suppl 1 (2015): P479. http://dx.doi.org/10.1186/cc14559.

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Ruppert, Matthew M., Jessica Lipori, Sandip Patel, Elizabeth Ingersent, Julie Cupka, Tezcan Ozrazgat-Baslanti, Tyler Loftus, Parisa Rashidi, and Azra Bihorac. "ICU Delirium-Prediction Models: A Systematic Review." Critical Care Explorations 2, no. 12 (December 2020): e0296. http://dx.doi.org/10.1097/cce.0000000000000296.

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SPLETE, HEIDI. "Tool Boosts Prediction Of Delirium in Adult." Hospitalist News 5, no. 7 (July 2012): 10. http://dx.doi.org/10.1016/s1875-9122(12)70147-5.

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Biju, Ashok, Babar Khan, and Heidi Lindroth. "VALIDATION OF A POSTOPERATIVE DELIRIUM PREDICTION MODEL." American Journal of Geriatric Psychiatry 28, no. 4 (April 2020): S96—S97. http://dx.doi.org/10.1016/j.jagp.2020.01.122.

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Hur, Sujeong, Ryoung-Eun Ko, Junsang Yoo, Juhyung Ha, Won Chul Cha, and Chi Ryang Chung. "A Machine Learning–Based Algorithm for the Prediction of Intensive Care Unit Delirium (PRIDE): Retrospective Study." JMIR Medical Informatics 9, no. 7 (July 26, 2021): e23401. http://dx.doi.org/10.2196/23401.

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Background Delirium frequently occurs among patients admitted to the intensive care unit (ICU). There is limited evidence to support interventions to treat or resolve delirium in patients who have already developed delirium. Therefore, the early recognition and prevention of delirium are important in the management of critically ill patients. Objective This study aims to develop and validate a delirium prediction model within 24 hours of admission to the ICU using electronic health record data. The algorithm was named the Prediction of ICU Delirium (PRIDE). Methods This is a retrospective cohort study performed at a tertiary referral hospital with 120 ICU beds. We only included patients who were 18 years or older at the time of admission and who stayed in the medical or surgical ICU. Patients were excluded if they lacked a Confusion Assessment Method for the ICU record from the day of ICU admission or if they had a positive Confusion Assessment Method for the ICU record at the time of ICU admission. The algorithm to predict delirium was developed using patient data from the first 2 years of the study period and validated using patient data from the last 6 months. Random forest (RF), Extreme Gradient Boosting (XGBoost), deep neural network (DNN), and logistic regression (LR) were used. The algorithms were externally validated using MIMIC-III data, and the algorithm with the largest area under the receiver operating characteristics (AUROC) curve in the external data set was named the PRIDE algorithm. Results A total of 37,543 cases were collected. After patient exclusion, 12,409 remained as our study population, of which 3816 (30.8%) patients experienced delirium incidents during the study period. Based on the exclusion criteria, out of the 96,016 ICU admission cases in the MIMIC-III data set, 2061 cases were included, and 272 (13.2%) delirium incidents occurred. The average AUROCs and 95% CIs for internal validation were 0.916 (95% CI 0.916-0.916) for RF, 0.919 (95% CI 0.919-0.919) for XGBoost, 0.881 (95% CI 0.878-0.884) for DNN, and 0.875 (95% CI 0.875-0.875) for LR. Regarding the external validation, the best AUROC were 0.721 (95% CI 0.72-0.721) for RF, 0.697 (95% CI 0.695-0.699) for XGBoost, 0.655 (95% CI 0.654-0.657) for DNN, and 0.631 (95% CI 0.631-0.631) for LR. The Brier score of the RF model is 0.168, indicating that it is well-calibrated. Conclusions A machine learning approach based on electronic health record data can be used to predict delirium within 24 hours of ICU admission. RF, XGBoost, DNN, and LR models were used, and they effectively predicted delirium. However, with the potential to advise ICU physicians and prevent ICU delirium, prospective studies are required to verify the algorithm’s performance.

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Panteleev, O. O., and V. V. Ryabov. "Delirium in a patient with myocardial infarction." Siberian Journal of Clinical and Experimental Medicine 37, no. 3 (October 19, 2022): 49–55. http://dx.doi.org/10.29001/2073-8552-2022-37-3-49-55.

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Delirium is a predictor of poor outcome in both myocardial infarction and other nosologies. Despite the growing interest in this problem, no eff ective methods for prediction, prevention, and treatment of delirium have been found. This literature review highlights the current ideas about delirium etiology, pathogenesis, approaches to prevention and treatment, and features of delirium in patients with myocardial infarction. The review presents the analysis of clinical trials and meta-analyses with the identifi cation of causes for clinical trials failures and the search for future promising directions of research focusing on this syndrome.

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Paton, Lia, Sara Elliott, and Sanjiv Chohan. "Utility of the PRE-DELIRIC delirium prediction model in a Scottish ICU cohort." Journal of the Intensive Care Society 17, no. 3 (June 22, 2016): 202–6. http://dx.doi.org/10.1177/1751143716638373.

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Park, Woo Rhim, Hye Rim Kim, Jin Young Park, Hesun Erin Kim, Jaehwa Cho, and Jooyoung Oh. "Potential Usefulness of Blood Urea Nitrogen to Creatinine Ratio in the Prediction and Early Detection of Delirium Motor Subtype in the Intensive Care Unit." Journal of Clinical Medicine 11, no. 17 (August 29, 2022): 5073. http://dx.doi.org/10.3390/jcm11175073.

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Prediction and early detection of delirium can improve patient outcomes. A high blood urea nitrogen to creatinine ratio (BCR), which reflects dehydration, has been reported as a risk factor for delirium. Additionally, BCR represents skeletal muscle loss in intensive care unit (ICU) patients, which can have critical implications for clinical outcomes. We investigated whether BCR could be used to predict the occurrence and motor subtype of delirium in ICU patients through a retrospective cohort study that included 7167 patients (50 years or older) admitted to the ICU. Patients were assessed daily using the Richmond Agitation-Sedation Scale and the Confusion Assessment Method for ICU and categorized according to the delirium subtype. Participants were split into 10 groups according to BCR at ICU admission and the prevalence of each delirium subtype was compared. Multivariable logistic regression was then used for analysis. A higher BCR at ICU admission was associated with the development of hypoactive delirium. Moreover, BCR > 24.9 was associated with higher rates of hypoactive delirium. Our findings showed that a high BCR at ICU admission was associated with the development of hypoactive delirium, which suggested that BCR could be a potential biomarker for hypoactive delirium in ICU patients.

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Kaźmierski, J., P. Miler, A. Pawlak, H. Jerczyńska, E. Frankowska, J. Woźniak, K. Woźniak, A. Brzezińska, and M. Wilczyński. "Raised preoperative monocyte chemoattractant protein-1 as the independent predictor of delirium after cardiac surgery. A prospective cohort study." European Psychiatry 64, S1 (April 2021): S252. http://dx.doi.org/10.1192/j.eurpsy.2021.676.

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IntroductionDelirium is a frequent and serious complication of cardiac surgery. However, the knowledge regarding pathogenesis of postoperative delirium is limited.ObjectivesTo investigate whether increased levels of monocyte chemoattractant protein-1 (MCP-1) and hyper-sensitive C-Reactive Protein (hsCRP) are associated with postoperative delirium in cardiac surgery patients.MethodsPatients were examined and screened for major depressive disorder (MDD) and cognitive impairment one day preoperatively, using the Mini International Neuropsychiatric Interview and The Mini-Mental State Examination Test. Blood samples were collected pre- and postoperatively for hsCRP and chemokine levels. Following surgical interventions, the Confusion Assessment Method for the Intensive Care Unit and the Memorial Delirium Assessment Scale with the cut-off score 10 were used to diagnose delirium.ResultsPostoperative delirium screening was found positive in 34% (61 of 177) of patients. Both, pre- and postoperative hsCRP, and preoperative MCP-1 levels were associated with postoperative delirium in univariate comparisons; p=0.001; p=0.0004; p < 0.001, respectively. However, according to a multivariate stepwise logistic regression analysis only MCP-1 concentration raised before surgery was independently associated with postoperative delirium, and related to advancing age of participants (Spearman’s Rank Correlation 0.192; p=0.0103). According to ROC analysis, the most optimal cut-off for MCP-1 concentration in predicting the development of delirium was 371.81 ng/ml with sensitivity of 77.0% and specificity of 58.6%.Conclusions The present study suggests that raised preoperative MCP-1 concentration is independently associated with delirium after cardiac surgery. Preoperative monitoring of pro-inflammatory markers combined with regular surveillance may be helpful in the prediction and early detection of postoperative delirium in this patient group.

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Evensen, Sigurd, Anette Hylen Ranhoff, Stian Lydersen, and Ingvild Saltvedt. "The delirium screening tool 4AT in routine clinical practice: prediction of mortality, sensitivity and specificity." European Geriatric Medicine 12, no. 4 (April 4, 2021): 793–800. http://dx.doi.org/10.1007/s41999-021-00489-1.

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Abstract Purpose Delirium is common and associated with poor outcomes, partly due to underdetection. We investigated if the delirium screening tool 4 A’s test (4AT) score predicts 1 year mortality and explored the sensitivity and specificity of the 4AT when applied as part of a clinical routine. Methods Secondary analyses of a prospective study of 228 patients acutely admitted to a Medical Geriatric Ward. Physicians without formal training conducted the index test (the 4AT); a predefined cut-off ≥ 4 suggested delirium. Reference standard was delirium diagnosed by two geriatricians using the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5). We calculated hazard ratios (HR) using Cox regression based on the groups 4AT = 0, 1–3, 4–7 and ≥ 8, first unadjusted, then adjusted for the covariates age, comorbidity, and personal activities of daily living. We calculated sensitivity, specificity, and the area under the receiver operating curve (AUC). Results Mean age of patients was 86.6 years, 139 (61.0%) were female, 78 (34.2%) had DSM-5 delirium; of these, 56 had 4AT-delirium. 1 year mortality was 27.6% (63 patients). Compared to 4AT score 0, the group 4AT ≥ 8 had increased 1 year mortality (HR 2.86, 95% confidence interval 1.28–6.37, p = 0.010). The effect was reduced in multiadjusted analyses (HR 1.69, 95% confidence interval 0.70–4.07, p = 0.24). Sensitivity, specificity, and AUC were 0.72, 0.84, and 0.88, respectively. Conclusions 4AT ≥ 8 indicates increased mortality, but the effect was reduced in multiadjusted analyses. 4AT had acceptable sensitivity and specificity when applied as a clinical routine.

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Azuma, Kazunari, Shiro Mishima, Keiichiro Shimoyama, Yuri Ishii, Yasuhiro Ueda, Masako Sakurai, Kentaro Morinaga, Tsubasa Fujikawa, and Jun Oda. "Validation of the Prediction of Delirium for Intensive Care model to predict subsyndromal delirium." Acute Medicine & Surgery 6, no. 1 (December 3, 2018): 54–59. http://dx.doi.org/10.1002/ams2.378.

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Alibhai, Shabbir M. H., Patrick Jung, Zuhair Alam, Lily Yeung, Uzair Malik, Ali Taqi Syed, Carla Rosario, et al. "Delirium incidence, risk factors, and treatment in older adults receiving chemotherapy: A scoping review." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e23025-e23025. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e23025.

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e23025 Background: Older adults with cancer are at increased risk of delirium given their advanced age, multiple comorbidities and medications, prevalence of cognitive impairment, and possibly cancer treatment. Awareness of such risks and interventions to prevent or treat delirium is important to clinicians and to provide high quality care. However, there is scant published information on the risks of delirium with chemotherapy or evidence-based approaches to prevent or treat it. We performed a scoping review to summarize the available evidence. Methods: We conducted a scoping review using the framework of Arksey and O’Malley. We systematically searched peer-reviewed journal articles in English, French, and German from Medline, Embase, PsychINFO, CINAHL Plus, and Cochrane Central from inception until January 2017 to identify studies that examined delirium in patients receiving chemotherapy. We also attempted to identify any studies that reported on multivariable delirium risk prediction models and any clinical trials that examined prevention or treatment of delirium. Article titles and abstracts as well as full text articles were reviewed using Covidence software by two or more reviewers independently. Similarly, data extraction was performed by two independent reviewers. Results: A total of 21,678 titles and abstracts were screened, and 1,166 full-text articles were reviewed. Nineteen articles with varying study designs (retrospective administrative databases to clinical trials) reported on delirium using an acceptable diagnostic standard. Sample sizes varied from 15 to over 21,000. No one tumour site or treatment protocol constituted the majority of studies. The incidence of delirium ranged from 0 to 51% (mean 13.5%). The time course of delirium relative to the cycle of chemotherapy was inconsistently reported. No studies reported on risk prediction models for delirium, and no intervention studies to prevent or treat delirium were identified. An additional 109 studies reported on outcomes that could be part of the delirium syndrome but did not meet even our broad inclusion criteria (e.g. cognitive disturbance). Conclusions: Delirium may occur in over 1 in 8 older adults receiving chemotherapy, although there were substantial limitations in reported studies. This scoping review highlights the dearth of knowledge in the area, particularly for risk factors, prevention, and treatment, and emphasizes the need for high-quality studies examining these important outcomes in the oncology setting.

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Keijzer, Hanneke M., Marjolein Klop, Michel J. A. M. van Putten, and Jeannette Hofmeijer. "Delirium after cardiac arrest: Phenotype, prediction, and outcome." Resuscitation 151 (June 2020): 43–49. http://dx.doi.org/10.1016/j.resuscitation.2020.03.020.

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Gonzalvo, C. Mestres, H. A. J. M. de Wit, B. P. C. van Oijen, D. Deben, K. P. G. M. Hurkens, W. J. Mulder, R. Janknegt, et al. "Prospective Validation of an Automated Delirium Prediction Model." Clinical Therapeutics 39, no. 8 (August 2017): e40. http://dx.doi.org/10.1016/j.clinthera.2017.05.126.

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Denk, Alexander, Karolina Müller, Sophie Schlosser, Klaus Heissner, Karsten Gülow, Martina Müller, and Stephan Schmid. "Liver diseases as a novel risk factor for delirium in the ICU–Delirium and hepatic encephalopathy are two distinct entities." PLOS ONE 17, no. 11 (November 22, 2022): e0276914. http://dx.doi.org/10.1371/journal.pone.0276914.

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Background Delirium prevalence is high in critical care settings. We examined the incidence, risk factors, and outcome of delirium in a medical intensive care unit (MICU) with a particular focus on liver diseases. We analyzed this patient population in terms of delirium risk prediction and differentiation between delirium and hepatic encephalopathy. Methods We conducted an observational study and included 164 consecutive patients admitted to an MICU of a university hospital. Patients were assessed for delirium using the Confusion Assessment Method for ICUs and the Richmond Agitation-Sedation Scale (RASS). On admission and at the onset of delirium Sequential Organ Failure Assessment (SOFA) score was determined. A population of patients with liver disease was compared to a population with gastrointestinal diseases. In the population with liver diseases, hepatic encephalopathy was graded according to the West Haven classification. We analyzed the incidence, subtype, predisposing, precipitating, and health-care setting-related factors, treatment, outcome of delirium and the association between delirium and hepatic encephalopathy in patients with liver diseases. Results The incidence of delirium was 32.5% (n = 53). Univariable binary regression analyses adjusted by the Holm-Bonferroni method showed that the development of delirium was significantly determined by 10 risk factors: Alcohol abuse (p = 0.016), severity of disease (Simplified Acute Physiology Score (SAPS) II, p = 0.016), liver diseases (p = 0.030) and sepsis (p = 0.016) compared to the control group (gastrointestinal (GI) diseases and others), increased sodium (p = 0.016), creatinine (p = 0.030), urea (p = 0.032) or bilirubin (p = 0.042), decreased hemoglobin (p = 0.016), and mechanical ventilation (p = 0.016). Of note, we identified liver diseases as a novel and relevant risk factor for delirium. Hepatic encephalopathy was not a risk factor for delirium. Delirium and hepatic encephalopathy are both life-threatening but clearly distinct conditions. The median SOFA score for patients with delirium at delirium onset was significantly higher than the SOFA score of all patients at admission (p = 0.008). Patients with delirium had five times longer ICU stays (p = 0.004) and three times higher in-hospital mortality (p = 0.036). Patients with delirium were five times more likely to be transferred to an intensive medical rehabilitation unit for post-intensive care (p = 0.020). Treatment costs per case were more than five times higher in patients with delirium than in patients without delirium (p = 0.004). Conclusions The 10 risk factors identified in this study should be assessed upon admission to ICU for effective detection, prevention, and treatment of delirium. Liver diseases are a novel risk factor for delirium with a level of significance comparable to sepsis as an established risk factor. Of note, in patients with liver diseases delirium and hepatic encephalopathy should be recognized as distinct entities to initiate appropriate treatment. Therefore, we propose a new algorithm for efficient diagnosis, characterization, and treatment of altered mental status in the ICU. This algorithm integrates the 10 risk factor prediction-model for delirium and prompts grading of the severity of hepatic encephalopathy using the West Haven classification if liver disease is present or newly diagnosed.

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Kotfis, Ślozowska, Safranow, Szylińska, and Listewnik. "The Practical Use of White Cell Inflammatory Biomarkers in Prediction of Postoperative Delirium after Cardiac Surgery." Brain Sciences 9, no. 11 (November 2, 2019): 308. http://dx.doi.org/10.3390/brainsci9110308.

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: Introduction: Postoperative delirium (POD) is associated with unfavorable outcomes. It may result from neuroinflammation and oxidative stress. The aim of this study was to evaluate the role of routinely available inflammatory markers derived from white blood cell count (WBC), for prognostic value in diagnosing delirium after cardiac surgery. Methods: We performed an analysis of data collected from patients undergoing planned coronary artery bypass grafting (CABG). Differential WBC and CRP concentration were evaluated preoperatively (T0) and postoperatively at day 1 (T1), 3 (T3), 5 (T5) after CABG. Differences in neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and platelet-to-WBC ratio (PWR) between patients with (Del+) and without delirium (Del−) were evaluated. Patients were screened using CAM-ICU. Results: We included 968 patients in the study. Incidence of delirium was 13.3%. In the group with POD, the majority of patients were men (87/129, 67.44%), and the mean age was 72 years. Preoperative WBC (8.21 ± 3.04 G/l vs. 7.55 ± 1.86 G/l, p = 0.029) were higher and mean platelet count was lower (217.7 ± 69.07 G/l vs. 227.44 ± 59.31 G/l, p = 0.031) in patients with POD. Lower pre-operative PLR values (109.87 ± 46.38 vs. 120.36 ± 52.98, p = 0.026) and PWR values (27.69 7.50 vs. 31.32 9.88 p < 0.001) were found in patients with POD. Association was strongest for PWR and remained significant at T1 (p < 0.001), T3 (p < 0.001) and T5 (p < 0.001). Basing on coefficients of logistic regression a model for optimal prediction of POD was calculated: CARDEL Index = 0.108 × Age + 0.341 × HBA1C − 0.049 × PWR with AUC of 0.742 (p < 0.001). Conclusions: The results of this study show that lower pre-operative levels of PLR and PWR were associated with POD after cardiac surgery. Pre-operative PWR showed strongest correlation with POD and may be a potential new biomarker associated with postoperative delirium. CARDEL prognosis index composed of age, HbA1c and PWR is good at predicting development of delirium after CABG.

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Wang, Hong, Dengbang Hou, Xiaqiu Tian, Liangshan Wang, Chenglong Li, Ming Jia, and Xiaotong Hou. "Risk factors for agitation and hyperactive delirium in adult postcardiotomy patients with extracorporeal membrane oxygenation support: an observational study." Perfusion 35, no. 6 (July 4, 2020): 534–42. http://dx.doi.org/10.1177/0267659120937549.

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Background: Agitation and delirium in critically ill patients after cardiac surgery carry poor in-hospital prognosis. Identifying risk factors may promote its prevention and management. Accordingly, this study aimed to evaluate the incidence of agitation and hyperactive delirium in postcardiotomy patients during the extracorporeal membrane oxygenation support and to identify the risk factors for its development. Methods: This single center, retrospective study was conducted at Beijing Anzhen Hospital, Capital Medical University. Data were extracted from the prospective institutional registry database of extracorporeal membrane oxygenation patients. Univariate and multivariate logistic regression analyses were performed to predict risk factors. Results: A total of 170 consecutive adult patients underwent extracorporeal membrane oxygenation in our hospital from January 2016 to December 2017. Ninety-four patients were included in the final analysis. The incidence of agitation and hyperactive delirium was 35% in our population of extracorporeal membrane oxygenation–supported postcardiotomy patients. Agitation and delirium usually occurred within the first 3 days of extracorporeal membrane oxygenation. Multivariable analysis showed that history of previous stroke (without preoperative cognitive dysfunction; odds ratio, 4.425, 95% confidence interval: 1.171-16.716; p = 0.028) and mean arterial pressure reduction (before extracorporeal membrane oxygenation initiation) ⩾ 49 mmHg (odds ratio, 7.570, 95% confidence interval: 2.366-24.219, p = 0.001) were independent risk factors for agitation and hyperactive delirium during extracorporeal membrane oxygenation support. The areas under the receiver operating characteristic curve for the prediction of agitation and hyperactive delirium was 0.704 (95% confidence interval 0.589-0.820, p = 0.001). There was more severe arrhythmia in the agitation patients. Conclusion: Our results suggest that the prevalence of agitation and hyperactive delirium in postcardiotomy patients with extracorporeal membrane oxygenation support is high. In addition, previous stroke and severe mean arterial pressure reduction before extracorporeal membrane oxygenation initiation is predictive of agitation and hyperactive delirium.

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Boettger, Soenke, David Garcia Nuñez, Rafael Meyer, André Richter, Susana Franco Fernandez, Alain Rudiger, Maria Schubert, and Josef Jenewein. "Delirium in the intensive care setting: A reevaluation of the validity of the CAM–ICU and ICDSC versus the DSM–IV–TR in determining a diagnosis of delirium as part of the daily clinical routine." Palliative and Supportive Care 15, no. 6 (February 8, 2017): 675–83. http://dx.doi.org/10.1017/s1478951516001176.

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ABSTRACTBackground:In the intensive care setting, delirium is a common occurrence that comes with subsequent adversities. Therefore, several instruments have been developed to screen for and detect delirium. Their validity and psychometric properties, however, remain controversial.Method:In this prospective cohort study, the Confusion Assessment Method for the Intensive Care Unit (CAM–ICU) and the Intensive Care Delirium Screening Checklist (ICDSC) were evaluated versus the DSM–IV–TR in the diagnosis of delirium with respect to their validity and psychometric properties.Results:Out of some 289 patients, 210 with matching CAM–ICU, ICDSC, and DSM–IV–TR diagnoses were included. Between the scales, the prevalence of delirium ranged from 23.3% with the CAM–ICU, to 30.5% with the ICDSC, to 43.8% with the DSM–IV–TR criteria. The CAM–ICU showed only moderate concurrent validity (Cohen's κ = 0.44) and sensitivity (50%), but high specificity (95%). The ICDSC also reached moderate agreement (Cohen's κ = 0.60) and sensitivity (63%) while being very specific (95%). Between the CAM–ICU and the ICDSC, the concurrent validity was again only moderate (Cohen's κ = 0.56); however, the ICDSC yielded higher sensitivity and specificity (78 and 83%, respectively).Significance of Results:In the daily clinical routine, neither the CAM–ICU nor the ICDSC, common tools used in screening and detecting delirium in the intensive care setting, reached sufficient concurrent validity; nor did they outperform the DSM–IV–TR diagnostic criteria with respect to sensitivity or positive prediction, but they were very specific. Thus, the non-prediction by the CAM–ICU or ICDSC did not refute the presence of delirium. Between the CAM–ICU and ICDSC, the ICDSC proved to be the more accurate instrument.

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Rahimi-Bashar, Farshid, Ghazal Abolhasani, Nahid Manouchehrian, Nasrin Jiryaee, Amir Vahedian-Azimi, and Amirhossein Sahebkar. "Incidence and Risk Factors of Delirium in the Intensive Care Unit: A Prospective Cohort." BioMed Research International 2021 (January 8, 2021): 1–9. http://dx.doi.org/10.1155/2021/6219678.

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Purpose. The purpose of this study was to determine the incidence, risk factors, and impact of delirium on outcomes in ICU patients. In addition, the scoring systems were measured consecutively to characterize how these scores changed with time in patients with and without delirium. Material and Methods. A prospective cohort study enrolling 400 consecutive patients admitted to the ICU between 2018 and 2019 due to trauma or surgery. Patients were followed up for the development of delirium over ICU days using the Confusion Assessment Method (CAM) for the ICU and Intensive Care Delirium Screening Checklist (ICDSC). Cox model logistic regression analysis was used to explore delirium risk factors. Results. Delirium occurred in 108 (27%) patients during their ICU stay, and the median onset of delirium was 4 (IQR 3–4) days after admission. According to multivariate cox regression, the expected hazard for delirium was 1.523 times higher in patients who used mechanical ventilator as compared to those who did not (HR: 1.523, 95% CI: 1.197-2.388, P < 0.001 ). Conclusion. Our findings suggest that an important opportunity for improving the care of critically ill patients may be the determination of modifiable risk factors for delirium in the ICU. In addition, the scoring systems (APACHE IV, SOFA, and RASS) are useful for the prediction of delirium in critically ill patients.

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Kappen, P. R., H. J. Kappen, C. Dirven, M. Klimek, R. Osse, and A. Vincent. "OS10.3.A Predicting delirium after craniotomy in neuro-oncology." Neuro-Oncology 23, Supplement_2 (September 1, 2021): ii13. http://dx.doi.org/10.1093/neuonc/noab180.041.

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Abstract BACKGROUND Post-operative delirium (POD) is a frequent and severe complication after neurosurgical operations. Good prediction of POD after craniotomy in neuro-oncologic patients is important to install prophylactic measures, increase recognition and apply early treatment. Hence, we compared logistic regression with machine learning to build an accurate predictive model in a large dataset. MATERIAL AND METHODS POD was defined in case of a Delirium Observation Scale (DOS) ≥ 3 or start of antipsychotic treatment for delirium within 10 days after surgery. Adult patients undergoing a craniotomy for a neuro-oncologic disease in the Erasmus Medical Centre in Rotterdam were retrospectively included. The cohort was split into a training (75%), after three-fold cross validation, and test set (25%). Logistic regression and Lasso Elastic-Net Regularized Generalized Linear Models (GLMNet) were trained based on 19 pre- and intra-operative features and risk factors were identified based on the superior model. RESULTS We included 1025 neuro-oncologic craniotomies between June 2017 and September 2020. Overall incidence of POD was 18.6% (95%CI 17.4–19.8). Compared to logistic regression, Lasso GLMNet performed superior (AUC 0.73 vs. 0.76) based on the optimal tuning parameters (α=1, λ=0.014). Several non-modifiable risk factors such as age (OR1.01), prior delirium (OR1.04), memory problems (OR1.12), surgery duration (OR1.01) and modifiable risk factors, such as low potassium (OR0.97) levels and opioid administration (OR1.03), were identified. CONCLUSION POD is a frequent complication after craniotomy in neuro-oncologic patients. Lasso GLMNet was useful in predicting POD in this cohort. Validation in a prospective cohort of this model should be applied to further evaluate its value in diminishing POD.

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Onuma, Hiroaki, Hiroyuki Inose, Toshitaka Yoshii, Takashi Hirai, Masato Yuasa, Shigenori Kawabata, and Atsushi Okawa. "Preoperative risk factors for delirium in patients aged ≥75 years undergoing spinal surgery: a retrospective study." Journal of International Medical Research 48, no. 10 (October 2020): 030006052096121. http://dx.doi.org/10.1177/0300060520961212.

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Objective The increasing number of spinal surgeries being performed in the elderly has increased the incidence of postoperative delirium. The prediction of delirium is complex, and few studies have been performed to examine the preoperative risk factors for delirium after spinal surgery in the elderly. This study was performed to clarify such risk factors in patients aged ≥75 years undergoing spinal surgery. Method This retrospective observational study included 299 patients aged ≥75 years. Comorbidities, medication history, preoperative examination findings, surgery-related characteristics, and health scale assessments, including the 36-Item Short-Form Survey (SF-36) score and prognostic nutritional index (PNI), were examined as potential risk factors for delirium. Results Delirium occurred in 53 patients (17.7%). The preoperative risk factors for delirium were a history of stroke and mental disorders, hypnotic drug use, malnutrition, hyponatremia, anemia, respiratory dysfunction, and cervical surgery. Logistic regression analysis demonstrated that the independent predictors of delirium were a history of stroke, non-benzodiazepine hypnotic drug use, preoperative hyponatremia, the PNI, and the SF-36 physical component summary (PCS) score. Conclusions Independent preoperative predictors of delirium in elderly patients undergoing spinal surgery included a history of stroke, non-benzodiazepine hypnotic drug use, preoperative hyponatremia, the PNI, and the SF-36 PCS score.

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Journal articles on the topic 'Delirium prediction'

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