Patient-centered precision care in anaesthesia - the PC-square (PC) 2 approach.

PURPOSE OF REVIEW: This review navigates the landscape of precision anaesthesia, emphasising tailored and individualized approaches to anaesthetic administration. The aim is to elucidate precision medicine principles, applications, and potential advancements in anaesthesia. The review focuses on the current state, challenges, and transformative opportunities in precision anaesthesia. RECENT FINDINGS: The review explores evidence supporting precision anaesthesia, drawing insights from neuroscientific fields. It probes the correlation between high-dose intraoperative opioids and increased postoperative consumption, highlighting how precision anaesthesia, especially through initiatives like Safe Brain Initiative (SBI), could address these issues. The SBI represents multidisciplinary collaboration in perioperative care. SBI fosters effective communication among surgical teams, anaesthesiologists, and other medical professionals. SUMMARY: Precision anaesthesia tailors care to individual patients, incorporating genomic insights, personalised drug regimens, and advanced monitoring techniques. From EEG to cerebral/somatic oximetry, these methods enhance precision. Standardised reporting, patient-reported outcomes, and continuous quality improvement, alongside initiatives like SBI, contribute to improved patient outcomes. Precision anaesthesia, underpinned by collaborative programs, emerges as a promising avenue for enhancing perioperative care.

The way towards ethical anesthesia care: no aim - no game - no fame or blame?

PURPOSE OF REVIEW: This review explores the intricacies of ethical anesthesia, exploring the necessity for precision anesthesia and its impact on patient-reported outcomes. The primary objective is to advocate for a defined aim, promoting the implementation of rules and feedback systems. The ultimate goal is to enhance precision anesthesia care, ensuring patient safety through the implementation of a teamwork and the integration of feedback mechanisms. RECENT FINDINGS: Recent strategies in the field of anesthesia have evolved from intraoperative monitorization to a wider perioperative patient-centered precision care. Nonetheless, implementing this approach encounters significant obstacles. The article explores the evidence supporting the need for a defined aim and applicable rules for precision anesthesia's effectiveness. The implementation of the safety culture is underlined. The review delves into the teamwork description with structured feedback systems. SUMMARY: Anesthesia is a multifaceted discipline that involves various stakeholders. The primary focus is delivering personalized precision care. This review underscores the importance of establishing clear aims, defined rules, and fostering effective and well tolerated teamwork with accurate feedback for improving patient-reported outcomes. The Safe Brain Initiative approach, emphasizing algorithmic monitoring and systematic follow-up, is crucial in implementing a fundamental and standardized reporting approach within patient-centered anesthesia care practice.

[Prevention of Post-operative Delirium]. / Prävention des postoperativen Delirs.

Postoperative delirium (POD) is an adverse but often preventable complication of surgery and surgery-related anaesthesia, and increasingly prevalent. This article provides an overview on non-pharmacological preventive measures, divided into individualized and non-individualized measures. Non-individualized measures, such as the most minimally invasive surgical procedure, avoidance of unnecessary fasting before surgery, and the most tolerable anaesthesia are used to minimize the risk of POD in all patients. Based on the results of preoperative screenings for risk factors such as frailty or cognitive impairment, individualized measures may encompass prehabilitation, treatment of specific risk factors, operation room companionship or cognitive, motor, and sensory stimulation as well as social support. This article additionally lists several examples of best practice approaches already implemented in German-speaking countries and websites for further readings.

C-reactive protein for risk prediction of post-operative delirium and post-operative neurocognitive disorder.

BACKGROUND: Post-operative delirium (POD) and post-operative neurocognitive disorder (NCD) are frequently seen in the elderly. Development of biomarkers for pre-operative risk prediction is of major relevance. As inflammation present before surgery might predispose to POD and post-operative NCD development, we aim to determine associations between pre-operative C-reactive protein (CRP) and the incidence of POD and post-operative NCD. METHODS: In this observational study, we analyzed 314 patients enrolled in the SuDoCo trial, who had a pre-operative CRP measurement the day before surgery. Primary outcomes were POD assessed according DSM-4 from day 1 until day 7 after surgery and post-operative NCD assessed 3 months after surgery. We conducted multivariable logistic regression analysis adjusted for age, sex, randomization, body mass index, MMSE, ASA status, infection/autoimmune disease/malignoma and types of surgery to determine associations between CRP with POD and post-operative NCD, respectively. RESULTS: Pre-operative CRP was independently associated with POD [OR 1.158 (95% CI 1.040, 1.291); P = .008]. Patients with CRP values ≥5 mg/dL had a 4.8-fold increased POD risk [OR 4.771 (95% CI 1.765, 12.899; P = .002)] compared to patients with lower CRP values. However, no association was seen between pre-operative CRP and post-operative NCD [OR 0.552 (95% CI 0.193, 1.581); P = .269]. CONCLUSIONS: Pre-operative CRP levels were independently associated with POD but not post-operative NCD after three months. Moreover, higher pre-operative CRP levels showed higher risk for POD. This strengthens the role of inflammation in the development of POD. Assessment of CRP before surgery might allow risk stratification of POD. TRIAL REGISTRATION: This study was registered with ISRCTN Register 36437985 on 02 March 2009.

Diabetes, but Not Hypertension and Obesity, Is Associated with Postoperative Cognitive Dysfunction.

BACKGROUND/AIMS: Older people undergoing surgery are at risk of developing postoperative cognitive dysfunction (POCD), but little is known of risk factors predisposing patients to POCD. Our objective was to estimate the risk of POCD associated with exposure to preoperative diabetes, hypertension, and obesity. METHODS: Original data from 3 randomised controlled trials (OCTOPUS, DECS, SuDoCo) were obtained for secondary analysis on diabetes, hypertension, baseline blood pressure, obesity (BMI ≥30 kg/m2), and BMI as risk factors for POCD in multiple logistic regression models. Risk estimates were pooled across the 3 studies. RESULTS: Analyses totalled 1,034 patients. POCD occurred in 5.2% of patients in DECS, in 9.4% in SuDoCo, and in 32.1% of patients in OCTOPUS. After adjustment for age, sex, surgery type, randomisation, obesity, and hypertension, diabetes was associated with a 1.84-fold increased risk of POCD (OR 1.84; 95% CI 1.14, 2.97; p = 0.01). Obesity, BMI, hypertension, and baseline blood pressure were each not associated with POCD in fully adjusted models (all p > 0.05). CONCLUSION: Diabetes, but not obesity or hypertension, is associated with increased POCD risk. Consideration of diabetes status may be helpful for risk assessment of surgical patients.

Patients prone for postoperative delirium: preoperative assessment, perioperative prophylaxis, postoperative treatment.

PURPOSE OF REVIEW: The aim of this study was to review current literature on identification of patients at risk for postoperative delirium (POD) and to summarize recent findings on prophylaxis and treatment. RECENT FINDINGS: Age and preoperative cognitive impairment are among the most important risk factors of POD. POD is the result of a complex interplay of predisposing and precipitating factors. Thus, both prophylaxis and treatment require multicomponent intervention programs. No single medication to prevent or treat POD is available. Avoiding too deep anesthesia, avoiding additional psychoactive substances including benzodiazepines and intravenous opioids, and effective pain management as well as early mobilization are essential. SUMMARY: An increase of the proportion of elderly patients undergoing surgery will lead to a higher incidence of POD. Preoperative assessment should facilitate identification of patients at high risk. Perioperative management should include monitoring depth of anesthesia, preference for nonopioid pain therapy, early regular delirium monitoring starting in the recovery room, avoiding ICU-sedation, early mobilization and exercise, and cognitive training.

Postoperative delirium is an independent risk factor for posttraumatic stress disorder in the elderly patient: a prospective observational study.

BACKGROUND: Posttraumatic stress disorder (PTSD) may appear after hospitalisation for surgery with general anaesthesia in elderly patients. Prevalence and risk factors in this setting are unknown. Postoperative delirium could be a risk factor. OBJECTIVE: The purpose of this study was to identify the prevalence of, and risk factors for, PTSD 3 months after surgery with general anaesthesia in elderly patients. DESIGN: A prospective, clinical observational study. SETTING: This study was carried out between March 2009 and May 2010 in a German university hospital in Berlin and was part of a larger study focusing on depth of anaesthesia. INCLUSION CRITERIA: at least 60 years of age; noncardiac surgery with general anaesthesia. EXCLUSION CRITERIA: impaired preoperative cognitive function [mini-mental state examination (MMSE) score <24]; expected surgery time less than 1 h; nonproficiency in the German language. INTERVENTIONS: None. MAIN OUTCOME MEASURES: Screening for PTSD 3 months after surgery using the screening instrument Post-Traumatic Stress Syndrome 14-Questions Inventory Score (PTSS-14). The following risk factors for PTSD 3 months after surgery were tested: age; American Society of Anesthesiologists physical status; sex; duration of anaesthesia; postoperative delirium; PTSS-14 score 7 days after surgery; postoperative vomiting and nausea; postoperative pain; and preoperative depression. STATISTICS: Univariate statistical analysis was performed with Fisher's exact test and Spearman correlation. A backward logistic regression was performed. RESULTS: A total of 559 out of 1277 patients were included. Sixty-six patients (12%) were identified with PTSD 3 months after surgery. Seventy-seven patients (14%) were identified with postoperative delirium. Independent associated factors in the backward logistic regression were postoperative delirium (risk factor) and preoperative depression (protective factor). CONCLUSION: The prevalence of PTSD 3 months after surgery in elderly patients was high using the screening instrument PTSS-14. Postoperative delirium is a risk factor for PTSD 3 months after surgery. TRIAL REGISTRATION: ISRCTN Register: 36437985. http://www.controlled-trials.com/ISRCTN36437985/

Postoperative neurocognitive disorders: A clinical guide.

For years, postoperative cognitive outcomes have steadily garnered attention, and in the past decade, they have remained at the forefront. This prominence is primarily due to empirical research emphasizing their potential to compromise patient autonomy, reduce quality of life, and extend hospital stays, and increase morbidity and mortality rates, especially impacting elderly patients. The underlying pathophysiological process might be attributed to surgical and anaesthesiological-induced stress, leading to subsequent neuroinflammation, neurotoxicity, burst suppression and the development of hypercoagulopathy. The beneficial impact of multi-faceted strategies designed to mitigate the surgical and perioperative stress response has been suggested. While certain potential risk factors are difficult to modify (e.g., invasiveness of surgery), others - including a more personalized depth of anaesthesia (EEG-guided), suitable analgesia, and haemodynamic stability - fall under the purview of anaesthesiologists. The ESAIC Safe Brain Initiative research group recommends implementing a bundle of non-invasive preventive measures as a standard for achieving more patient-centred care. Implementing multi-faceted preoperative, intraoperative, and postoperative preventive initiatives has demonstrated the potential to decrease the incidence and duration of postoperative delirium. This further validates the importance of a holistic, team-based approach in enhancing patients' clinical and functional outcomes. This review aims to present evidence-based recommendations for preventing, diagnosing, and treating postoperative neurocognitive disorders with the Safe Brain Initiative approach.

A first assessment of the safe brain initiative care bundle for addressing postoperative delirium in the postanesthesia care unit.

BACKGROUND: Postoperative delirium (POD) following surgery is a prevalent and distressing condition associated with adverse patient outcomes and an increased healthcare burden. OBJECTIVES: To assess the effectiveness of the Safe Brain Initiative care bundle (SBI-CB) in reducing POD in the postanesthesia care unit (PACU). DESIGN: A multicenter, quality-improvement initiative with retrospective analysis of collected data. SETTING: The study was conducted in the operating rooms and postanesthesia care units (PACUs) of four hospitals across Denmark and Turkey. PATIENTS: The convenience sample of patients were aged ≥18 years, scheduled for surgery, and could communicate verbally. Age, sex, preoperative delirium, and the American Society for Anesthesiology physical status classification were used in statistical methods to control for potential confounding influences. INTERVENTION: The SBI-CB, 18 delirium-reducing recommendations aligned with international guidelines. The intervention included patient education, staff training, coordination meetings across centers, and a dashboard for the monitoring of outcomes in the PACU. MAIN OUTCOME MEASURES: The primary outcome was the POD trend in the PACU during implementation months, assessed through Nu-DESC screening at up to three time points in the PACU. We also examined the length of hospital stay. RESULTS: Data were collected from 18,697 adult patients across four hospitals. Initial POD incidence in the PACU after the first three months was 16.36% across all sites (n = 1021). POD in the PACU was observed across all age groups, with peak incidence in younger (18-35 years) and older (>75 years) patients. General anesthesia and longer surgical duration (>1 h) were identified as significant risk factors for POD in the PACU. Matched patients who experienced POD in the PACU had longer stays in hospital, with a mean increase from 35 to 69 h (p < 0.001). Implementation of the SBI-CB was associated with a decreased risk of POD in the PACU for each month of SBI-CB implementation (adjusted odds ratio 0.96, 95% confidence interval: [0.94, 0.97], p < 0.001). CONCLUSIONS: The presented pragmatic implementation of a multidisciplinary care bundle, encompassing pre-, intra-, and postoperative measures alongside outcome monitoring, has the potential to significantly reduce the incidence of POD in the PACU. Improved patient outcomes may be achieved for general surgical departments with patient cohorts not typically considered at risk for developing POD. TRIAL REGISTRATION: Clinicaltrials.gov, identifier NCT05765162.

The Delphi Delirium Management Algorithms. A practical tool for clinicians, the result of a modified Delphi expert consensus approach.

Delirium is common in hospitalised patients, and there is currently no specific treatment. Identifying and treating underlying somatic causes of delirium is the first priority once delirium is diagnosed. Several international guidelines provide clinicians with an evidence-based approach to screening, diagnosis and symptomatic treatment. However, current guidelines do not offer a structured approach to identification of underlying causes. A panel of 37 internationally recognised delirium experts from diverse medical backgrounds worked together in a modified Delphi approach via an online platform. Consensus was reached after five voting rounds. The final product of this project is a set of three delirium management algorithms (the Delirium Delphi Algorithms), one for ward patients, one for patients after cardiac surgery and one for patients in the intensive care unit.

Desflurane is risk factor for postoperative delirium in older patients' independent from intraoperative burst suppression duration.

Background: Postoperative Delirium (POD) is the most frequent neurocognitive complication after general anesthesia in older patients. The development of POD is associated with prolonged periods of burst suppression activity in the intraoperative electroencephalogram (EEG). The risk to present burst suppression activity depends not only on the age of the patient but is also more frequent during propofol anesthesia as compared to inhalative anesthesia. The aim of our study is to determine, if the risk to develop POD differs depending on the anesthetic agent given and if this correlates with a longer duration of intraoperative burst suppression. Methods: In this secondary analysis of the SuDoCo trail [ISRCTN 36437985] 1277 patients, older than 60 years undergoing general anesthesia were included. We preprocessed and analyzed the raw EEG files from each patient and evaluated the intraoperative burst suppression duration. In a logistic regression analysis, we assessed the impact of burst suppression duration and anesthetic agent used for maintenance on the risk to develop POD. Results: 18.7% of patients developed POD. Burst suppression duration was prolonged in POD patients (POD 27.5 min ± 21.3 min vs. NoPOD 21.4 ± 16.2 min, p < 0.001), for each minute of prolonged intraoperative burst suppression activity the risk to develop POD increased by 1.1% (OR 1.011, CI 95% 1.000-1.022, p = 0.046). Burst suppression duration was prolonged under propofol anesthesia as compared to sevoflurane and desflurane anesthesia (propofol 32.5 ± 20.3 min, sevoflurane 17.1 ± 12.6 min and desflurane 20.1 ± 16.0 min, p < 0.001). However, patients receiving desflurane anesthesia had a 1.8fold higher risk to develop POD, as compared to propofol anesthesia (OR 1.766, CI 95% 1.049-2.974, p = 0.032). Conclusion: We found a significantly increased risk to develop POD after desflurane anesthesia in older patients, even though burst suppression duration was shorter under desflurane anesthesia as compared to propofol anesthesia. Our finding might help to explain some discrepancies in studies analyzing the impact of burst suppression duration and EEG-guided anesthesia on the risk to develop POD.

EEG microstate quantifiers and state space descriptors during anaesthesia in patients with postoperative delirium: a descriptive analysis.

Postoperative delirium is a serious sequela of surgery and surgery-related anaesthesia. One recommended method to prevent postoperative delirium is using bi-frontal EEG recording. The single, processed index of depth of anaesthesia allows the anaesthetist to avoid episodes of suppression EEG and excessively deep anaesthesia. The study data presented here were based on multichannel (19 channels) EEG recordings during anaesthesia. This enabled the analysis of various parameters of global electrical brain activity. These parameters were used to compare microstate topographies under anaesthesia with those in healthy volunteers and to analyse changes in microstate quantifiers and EEG global state space descriptors with increasing exposure to anaesthesia. Seventy-three patients from the Surgery Depth of Anaesthesia and Cognitive Outcome study (SRCTN 36437985) received intraoperative multichannel EEG recordings. Altogether, 720 min of artefact-free EEG data, including 210 min (29.2%) of suppression EEG, were analysed. EEG microstate topographies, microstate quantifiers (duration, frequency of occurrence and global field power) and the state space descriptors sigma (overall EEG power), phi (generalized frequency) and omega (number of uncorrelated brain processes) were evaluated as a function of duration of exposure to anaesthesia, suppression EEG and subsequent development of postoperative delirium. The major analyses involved covariate-adjusted linear mixed-effects models. The older (71 ± 7 years), predominantly male (60%) patients received a median exposure of 210 (range: 75-675) min of anaesthesia. During seven postoperative days, 21 patients (29%) developed postoperative delirium. Microstate topographies under anaesthesia resembled topographies from healthy and much younger awake persons. With increasing duration of exposure to anaesthesia, single microstate quantifiers progressed differently in suppression or non-suppression EEG and in patients with or without subsequent postoperative delirium. The most pronounced changes occurred during enduring suppression EEG in patients with subsequent postoperative delirium: duration and frequency of occurrence of microstates C and D progressed in opposite directions, and the state space descriptors showed a pattern of declining uncorrelated brain processes (omega) combined with increasing EEG variance (sigma). With increasing exposure to general anaesthesia, multiple changes in the dynamics of microstates and global EEG parameters occurred. These changes varied partly between suppression and non-suppression EEG and between patients with or without subsequent postoperative delirium. Ongoing suppression EEG in patients with subsequent postoperative delirium was associated with reduced network complexity in combination with increased overall EEG power. Additionally, marked changes in quantifiers in microstate C and in microstate D occurred. These putatively adverse intraoperative trajectories in global electrical brain activity may be seen as preceding and ultimately predicting postoperative delirium.

Machine-learning model predicting postoperative delirium in older patients using intraoperative frontal electroencephalographic signatures.

Objective: In older patients receiving general anesthesia, postoperative delirium (POD) is the most frequent form of cerebral dysfunction. Early identification of patients at higher risk to develop POD could provide the opportunity to adapt intraoperative and postoperative therapy. We, therefore, propose a machine learning approach to predict the risk of POD in elderly patients, using routine intraoperative electroencephalography (EEG) and clinical data that are readily available in the operating room. Methods: We conducted a retrospective analysis of the data of a single-center study at the Charité-Universitätsmedizin Berlin, Department of Anesthesiology [ISRCTN 36437985], including 1,277 patients, older than 60 years with planned surgery and general anesthesia. To deal with the class imbalance, we used balanced ensemble methods, specifically Bagging and Random Forests and as a performance measure, the area under the ROC curve (AUC-ROC). We trained our models including basic clinical parameters and intraoperative EEG features in particular classical spectral and burst suppression signatures as well as multi-band covariance matrices, which were classified, taking advantage of the geometry of a Riemannian manifold. The models were validated with 10 repeats of a 10-fold cross-validation. Results: Including EEG data in the classification resulted in a robust and reliable risk evaluation for POD. The clinical parameters alone achieved an AUC-ROC score of 0.75. Including EEG signatures improved the classification when the patients were grouped by anesthetic agents and evaluated separately for each group. The spectral features alone showed an AUC-ROC score of 0.66; the covariance features showed an AUC-ROC score of 0.68. The AUC-ROC scores of EEG features relative to patient data differed by anesthetic group. The best performance was reached, combining both the EEG features and the clinical parameters. Overall, the AUC-ROC score was 0.77, for patients receiving Propofol it was 0.78, for those receiving Sevoflurane it was 0.8 and for those receiving Desflurane 0.73. Applying the trained prediction model to an independent data set of a different clinical study confirmed these results for the combined classification, while the classifier on clinical parameters alone did not generalize. Conclusion: A machine learning approach combining intraoperative frontal EEG signatures with clinical parameters could be an easily applicable tool to early identify patients at risk to develop POD.

The Thai version of the nursing delirium screening scale-Thai: Adaptation and validation study in postoperative patients.

Background: The Nursing Delirium Screening Scale (Nu-DESC) is an effective instrument for assessing postoperative delirium (POD). This study translated the Nu-DESC into Thai ("Nu-DESC-Thai"), validated it, and compared its accuracy with the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5). Methods: The translation process followed the International Society for Pharmacoeconomics Outcome Research guidelines. Recruited participants were ≥ 70 years old, fluent in Thai, and scheduled for surgery. The exclusion criteria were cancellation or postponement of an operation, severe visual or auditory impairment, and patients with a Richmond Agitation Sedation Scale score of -4 or less before delirium assessment. Post-anesthesia care unit (PACU) nurses and residents on wards each used the Nu-DESC to assess delirium in 70 participants (i.e., 140 assessments) after the operation and after patient arrival at wards, respectively. Geriatricians confirmed the diagnoses using video observations and direct patient contact. Results: The participants' mean age was 76.5 ± 4.6 years. The sensitivity and specificity of the Nu-DESC-Thai at a threshold of ≥ 2 were 55% (95% CI, 31.5-76.9%) and 90.8% (84.2-95.3%), respectively, with an area under a receiver operating characteristic curve (AUC) of 0.73. At a threshold of ≥ 1, the sensitivity and specificity were 85% (62.1-96.8%) and 71.7% (62.7-79.5%), respectively (AUC, 0.78). Adding 1 point for failing backward-digit counting (30-1) to the Nu-DESC-Thai and screening at a threshold of ≥ 2 increased its sensitivity to 85% (62.1-96.8%) with the same specificity of 90.8% (84.2-95.3%). Conclusion: The Nu-DESC-Thai showed good validity and reliability for postoperative use. Its sensitivity was inadequate at a cutoff ≥ 2. However, the sensitivity improved when the threshold was ≥ 1 or with the addition of backward counting to Nu-DESC-Thai and screening at a threshold of ≥ 2.

Delirium in the postanaesthesia period.

PURPOSE OF REVIEW: Delirium is an acute, potentially life-threatening organ dysfunction with an incidence reported to range from 10-70% after surgery. Postoperative delirium was found to be associated with persisting cognitive deficits, increased physical dependence and institutionalization, and increased mortality. It is a condition particularly relevant to patients with increasing age. RECENT FINDINGS: This study summarizes recent works of the past 2 years, giving a brief overview as well as background information with regard to risk factors, impact on outcome parameters, mechanisms of pathophysiology, current use of hospital medication, and prevention and treatment strategies of postoperative delirium. SUMMARY: Delirium may have an impact on patients' outcomes beyond their stay in hospital, depending on preoperative comorbidities. Delirium can be devastating for activity of daily living, cognitive performance and survival. Predisposing factors should be recognized preoperatively; precipitating factors such as preoperative fasting, deep sedation and choice of psychotropic drugs, including sedatives, should be reconsidered. Regular structured delirium screening is the precondition for early detection and treatment. Treatment options include cognitive training programmes, anti-inflammatory measures and antipsychotic drugs.

Different assessment tools for intensive care unit delirium: which score to use?

OBJECTIVE: To compare validity and reliability of three instruments for detection and assessment of delirium in intensive care unit (ICU) patients. Delirium in critically ill patients is associated with higher mortality, prolonged duration of ICU stay, and greater healthcare costs. Currently, there are several assessment tools available for detection of delirium, but only a few of these assessment systems are developed specifically to screen for delirium in ICU patients. DESIGN: Prospective cohort study. SETTING: ICU at a university hospital. PATIENTS: A total of 156 surgical patients aged > or = 60 yrs consecutively admitted to the ICU, with a length of stay of at least 24 hrs. MEASUREMENTS AND MAIN RESULTS: This study was approved by the institutional ethics committee. Trained staff members performed daily and independently the Confusion Assessment Method for the ICU (CAM-ICU), the Nursing Delirium Screening Scale (Nu-DESC), and the Delirium Detection Score (DDS). These evaluations were compared against the reference standard conducted by a delirium expert (blinded to the study), who used delirium criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Of 156 patients, 63 (40%) were identified as delirious by the reference standard during the study. Using the CAM-ICU and the Nu-DESC, we measured comparable sensitivities (CAM-ICU, 81%; Nu-DESC, 83%). The specificity of the CAM-ICU was significantly higher than that of the Nu-DESC (96% vs. 81%, p < .01). In contrast, the DDS showed poor sensitivity (30%), whereas the specificity was significantly higher compared with the Nu-DESC (DDS, 91%; Nu-DESC, 81%, p < .05). The interrater reliability was "almost perfect" for the CAM-ICU (kappa = 0.89) and "substantial" for DDS and Nu-DESC (kappa = 0.79, 0.68). CONCLUSION: The CAM-ICU showed the best validity of the evaluated scales to identify delirium in ICU patients. The Nu-DESC might be an alternative tool for detection of ICU delirium. The DDS should not be used as a screening tool.

A comparison of three scores to screen for delirium on the surgical ward.

BACKGROUND: Postoperative delirium is associated with adverse outcome. The aim of this study was to find a valid and easy-to-use tool to screen for postoperative delirium on the surgical ward. METHODS: Data were collected from 88 patients who underwent elective surgery. Delirium screening was performed daily until the sixth postoperative day using the Confusion Assessment Method (CAM), the Nursing Delirium Screening Scale (Nu-DESC), and the Delirium Detection Score (DDS), and the DSM-IV criteria as the gold standard. RESULTS: Seventeen of 88 patients (19%) developed delirium on at least one of the postoperative days according to the gold standard. The DDS scored positive for 40 (45%) patients, the CAM for 15 (17%), and the Nu-DESC for 28 (32%) patients. Sensitivity and specificity were 0.71 and 0.87 for the DDS, 0.75 and 1.00 for the CAM, and 0.98 and 0.92 for the Nu-DESC. The interrater reliability was 0.83 for the Nu-DESC, 0.77 for the DDS, and 1.00 for the CAM. CONCLUSIONS: All scores showed high specificity but differed in their sensitivity. The Nu-DESC proved to be the most sensitive test for screening for a postoperative delirium on the surgical ward followed by the CAM and DDS when compared to the gold standard.

Duration of fluid fasting and choice of analgesic are modifiable factors for early postoperative delirium.

BACKGROUND AND OBJECTIVE: Most therapeutic options for postoperative delirium are only symptom oriented; therefore, the best approach remains prevention. The aim of this study was to identify predisposing and precipitating factors for early postoperative delirium. METHODS: A total of 1002 patients were screened for delirium in an observational, cohort study. Nine hundred and ten patients were observed in the recovery room and 862 patients on the first postoperative day in the ward at the Charité-Universitaetsmedizin, Berlin. Delirium was measured with the nursing delirium screening scale. Risk factors were analysed in a multivariate analysis. RESULTS: Delirium was seen in 11.0% of the patients in the recovery room and in 4.2% of the patients on the ward. Delirium in the recovery room was associated with delirium on the ward (McNemar's test P = <0.001). Apart from age and site of surgery, we found the duration of preoperative fluid fasting to be a modifiable precipitating factor for delirium in the recovery room (odds ratio 2.69, 95% confidence interval 1.4-5.2) and on the ward (odds ratio 10.57, 95% confidence interval 1.4-78.6) and the choice of intraoperative opioid for delirium on the ward (odds ratio 2.27, 95% confidence interval 1.0-5.1). CONCLUSION: Duration of preoperative fluid fasting and the choice of intraoperative analgesic are risk factors for postoperative delirium, and their modification provides a promising approach to reduce the incidence of postoperative delirium.