The 4AT, a rapid delirium detection tool for use in hospice inpatient units: Findings from a validation study.

BACKGROUND: Delirium is a serious neuropsychiatric syndrome with adverse outcomes, which is common but often undiagnosed in terminally ill people. The 4 'A's test or 4AT (www.the4AT.com), a brief delirium detection tool, is widely used in general settings, but validation studies in terminally ill people are lacking. AIM: To determine the diagnostic accuracy of the 4AT in detecting delirium in terminally ill people, who are hospice inpatients. DESIGN: A diagnostic test accuracy study in which participants underwent the 4AT and a reference standard based on the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders. The reference standard was informed by Delirium Rating Scale Revised-98 and tests assessing arousal and attention. Assessments were conducted in random order by pairs of independent raters, blinded to the results of the other assessment. SETTING/PARTICIPANTS: Two hospice inpatient units in Scotland, UK. Participants were 148 hospice inpatients aged ⩾18 years. RESULTS: A total of 137 participants completed both assessments. Three participants had an indeterminate reference standard diagnosis and were excluded, yielding a final sample of 134. Mean age was 70.3 (SD = 10.6) years. About 33% (44/134) had reference standard delirium. The 4AT had a sensitivity of 89% (95% CI 79%-98%) and a specificity of 94% (95% CI 90%-99%). The area under the receiver operating characteristic curve was 0.97 (95% CI 0.94-1). CONCLUSION: The results of this validation study support use of the 4AT as a delirium detection tool in hospice inpatients, and add to the literature evaluating methods of delirium detection in palliative care settings. TRIAL REGISTRY: ISCRTN 97417474.

Introducing a machine learning algorithm for delirium prediction-the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead).

INTRODUCTION: Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14-56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop a machine learning (ML) model for POD prediction in older patients, in close cooperation with the PAWEL (patient safety, cost-effectiveness and quality of life in elective surgery) project. METHODS: The model was trained on the PAWEL study's dataset of 878 patients (no intervention, age ≥ 70, 209 with POD). Presence of POD was determined by the Confusion Assessment Method and a chart review. We selected 15 features based on domain knowledge, ethical considerations and a recursive feature elimination. A logistic regression and a linear support vector machine (SVM) were trained, and evaluated using receiver operator characteristics (ROC). RESULTS: The selected features were American Society of Anesthesiologists score, multimorbidity, cut-to-suture time, estimated glomerular filtration rate, polypharmacy, use of cardio-pulmonary bypass, the Montreal cognitive assessment subscores 'memory', 'orientation' and 'verbal fluency', pre-existing dementia, clinical frailty scale, age, recent falls, post-operative isolation and pre-operative benzodiazepines. The linear SVM performed best, with an ROC area under the curve of 0.82 [95% CI 0.78-0.85] in the training set, 0.81 [95% CI 0.71-0.88] in the test set and 0.76 [95% CI 0.71-0.79] in a cross-centre validation. CONCLUSION: We present a clinically useful and explainable ML model for POD prediction. The model will be deployed in the Supporting SURgery with GEriatric Co-Management and AI project.

Development of an individualized model for predicting postoperative delirium in elderly patients with hepatocellular carcinoma.

Postoperative delirium (POD) is a common complication in older patients with hepatocellular carcinoma (HCC) that adversely impacts clinical outcomes. We aimed to evaluate the risk factors for POD and to construct a predictive nomogram. Data for a total of 1481 older patients (training set: n=1109; validation set: n=372) who received liver resection for HCC were retrospectively retrieved from two prospective databases. The receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance. The rate of POD was 13.3% (148/1109) in the training set and 16.4% (61/372) in the validation set. Multivariate analysis of the training set revealed that factors including age, history of cerebrovascular disease, American Society of Anesthesiologists (ASA) classification, albumin level, and surgical approach had significant effects on POD. The area under the ROC curves (AUC) for the nomogram, incorporating the aforementioned predictors, was 0.798 (95% CI 0.752-0.843) and 0.808 (95% CI 0.754-0.861) for the training and validation sets, respectively. The calibration curves of both sets showed a degree of agreement between the nomogram and the actual probability. DCA demonstrated that the newly established nomogram was highly effective for clinical decision-making. We developed and validated a nomogram with high sensitivity to assist clinicians in estimating the individual risk of POD in older patients with HCC.

Development and validation of a new drug-focused predictive risk score for postoperative delirium in orthopaedic and trauma surgery patients.

BACKGROUND: Postoperative delirium (POD) is the most common complication following surgery in elderly patients. During pharmacist-led medication reconciliation (PhMR), a predictive risk score considering delirium risk-increasing drugs and other available risk factors could help to identify risk patients. METHODS: Orthopaedic and trauma surgery patients aged ≥ 18 years with PhMR were included in a retrospective observational single-centre study 03/2022-10/2022. The study cohort was randomly split into a development and a validation cohort (6:4 ratio). POD was assessed through the 4 A's test (4AT), delirium diagnosis, and chart review. Potential risk factors available at PhMR were tested via univariable analysis. Significant variables were added to a multivariable logistic regression model. Based on the regression coefficients, a risk score for POD including delirium risk-increasing drugs (DRD score) was established. RESULTS: POD occurred in 42/328 (12.8%) and 30/218 (13.8%) patients in the development and validation cohorts, respectively. Of the seven evaluated risk factors, four were ultimately tested in a multivariable logistic regression model. The final DRD score included age (66-75 years, 2 points; > 75 years, 3 points), renal impairment (eGFR < 60 ml/min/1.73m2, 1 point), anticholinergic burden (ACB-score ≥ 3, 1 point), and delirium risk-increasing drugs (n ≥ 2; 2 points). Patients with ≥ 4 points were classified as having a high risk for POD. The areas under the receiver operating characteristic curve of the risk score model were 0.89 and 0.81 for the development and the validation cohorts, respectively. CONCLUSION: The DRD score is a predictive risk score assessable during PhMR and can identify patients at risk for POD. Specific preventive measures concerning drug therapy safety and non-pharmacological actions should be implemented for identified risk patients.

The role of heart rate variability in predicting delirium: A systematic review and meta-analysis.

Brain and heart interact through multiple ways. Heart rate variability, a non-invasive measurement is studied extensively as a predicting model for various health conditions including subarachnoid hemorrhage, cancer, and diabetes. There is limited evidence to predict delirium, an acute fluctuating disorder of brain dysfunction, as it poses a significant challenge in the intensive care unit (ICU) and post-operative setting. In this systematic review of 9 articles, heart rate variability indices were used to investigate the occurrence of post-operative and ICU delirium. This systematic review and meta-analysis reveal evidence of a strong predilection between postoperative and intensive care unit delirium and alterations in the heart rate variability, measured by mean differences for standard deviation of NN-intervals. Other heart rate variability indices [root mean squares of successive differences, low-frequency (LF), high-frequency (HF), and LF:HF ratio] showed lack of or very weak association. A non-invasive tool of brain and heart interaction may refine diagnostic predictions for acute brain dysfunctions like delirium in such population and would be an important step in delirium research.

Association between pediatric postoperative delirium and regional cerebral oxygen saturation: a prospective observational study.

BACKGROUND: Postoperative delirium (POD) represents a prevalent and noteworthy complication in the context of pediatric surgical interventions. In recent times, a hypothesis has emerged positing that cerebral ischemia and regional cerebral oxygen desaturation might serve as potential catalysts in the pathogenesis of POD. The primary aim of this study was to methodically examine the potential relationship between POD and regional cerebral oxygen saturation (rSO2) and to assess the predictive and evaluative utility of rSO2 in the context of POD. METHODS: This prospective observational study was conducted at the Children's Hospital, Zhejiang University School of Medicine, Zhejiang, China, spanning the period from November 2020 to March 2021. The research cohort comprised children undergoing surgical procedures within this clinical setting. To measure rSO2 dynamics, cerebral near-infrared spectroscopy (NIRS) was used to monitor rSO2 levels both before and after surgery. In addition, POD was assessed in the paediatric patients according to the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) criteria. The analysis of the association between the rSO2 index and the incidence of POD was carried out through the application of either the independent samples t-test or the nonparametric rank-sum test. To ascertain the threshold value of the adjusted rSO2 index for predictive and evaluative purposes regarding POD in the pediatric population, the Receiver Operating Characteristics (ROC) curve was employed. RESULTS: A total of 211 cases were included in this study, of which 61 (28.9%) developed POD. Participants suffering delirium had lower preoperative rSO2mean, lower preoperative rSO2min, and lower postoperative rSO2min, higher ∆rSO2mean, higher amount of ∆rSO2mean, lower ∆rSO2min (P < 0.05). Preoperative rSO2mean (AUC = 0.716, 95%CI 0.642-0.790), ∆rSO2mean (AUC = 0.694, 95%CI 0.614-0.774), amount of ∆rSO2mean (AUC = 0.649, 95%CI 0.564-0.734), preoperative rSO2min (AUC = 0.702, 96%CI 0.628-0.777), postoperative rSO2min (AUC = 0.717, 95%CI 0.647-0.787), and ∆rSO2min (AUC = 0.714, 95%CI 0.638-0.790) performed well in sensitivity and specificity, and the best threshold were 62.05%, 1.27%, 2.41%, 55.68%, 57.36%, 1.29%. CONCLUSIONS: There is a close relationship between pediatric POD and rSO2. rSO2 could be used as an effective predictor of pediatric POD. It might be helpful to measure rSO2 with NIRS for early recognizing POD and making it possible for early intervention.

Characterizing medical patients with delirium: A cohort study comparing ICD-10 codes and a validated chart review method.

BACKGROUND: Delirium is a major cause of preventable mortality and morbidity in hospitalized adults, but accurately determining rates of delirium remains a challenge. OBJECTIVE: To characterize and compare medical inpatients identified as having delirium using two common methods, administrative data and retrospective chart review. METHODS: We conducted a retrospective study of 3881 randomly selected internal medicine hospital admissions from six acute care hospitals in Toronto and Mississauga, Ontario, Canada. Delirium status was determined using ICD-10-CA codes from hospital administrative data and through a previously validated chart review method. Baseline sociodemographic and clinical characteristics, processes of care and outcomes were compared across those without delirium in hospital and those with delirium as determined by administrative data and chart review. RESULTS: Delirium was identified in 6.3% of admissions by ICD-10-CA codes compared to 25.7% by chart review. Using chart review as the reference standard, ICD-10-CA codes for delirium had sensitivity 24.1% (95%CI: 21.5-26.8%), specificity 99.8% (95%CI: 99.5-99.9%), positive predictive value 97.6% (95%CI: 94.6-98.9%), and negative predictive value 79.2% (95%CI: 78.6-79.7%). Age over 80, male gender, and Charlson comorbidity index greater than 2 were associated with misclassification of delirium. Inpatient mortality and median costs of care were greater in patients determined to have delirium by ICD-10-CA codes (5.8% greater mortality, 95% CI: 2.0-9.5 and $6824 greater cost, 95%CI: 4713-9264) and by chart review (11.9% greater mortality, 95%CI: 9.5-14.2% and $4967 greater cost, 95%CI: 4415-5701), compared to patients without delirium. CONCLUSIONS: Administrative data are specific but highly insensitive, missing most cases of delirium in hospital. Mortality and costs of care were greater for both the delirium cases that were detected and missed by administrative data. Better methods of routinely measuring delirium in hospital are needed.

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

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

Co-design and mixed methods evaluation of an interdisciplinary digital resource for undergraduate health profession students to improve the prevention, recognition, and management of delirium in Ireland: a study protocol.

BACKGROUND: Delirium is a common symptom of acute illness which is potentially avoidable with early recognition and intervention. Despite being a growing concern globally, delirium remains underdiagnosed and poorly reported, with limited understanding of effective delirium education for undergraduate health profession students. Digital resources could be an effective approach to improving professional knowledge of delirium, but studies utilising these with more than one profession are limited, and no evidence-based, interdisciplinary, digital delirium education resources are reported. This study aims to co-design and evaluate a digital resource for undergraduate health profession students across the island of Ireland to improve their ability to prevent, recognise, and manage delirium alongside interdisciplinary colleagues. METHODS: Utilising a logic model, three workstreams have been identified. Workstream 1 will comprise three phases: (1) a systematic review identifying the format, methods, and content of existing digital delirium education interventions for health profession students, and their effect on knowledge, self-efficacy, and behavioural change; (2) focus groups with health profession students to determine awareness and experiences of delirium care; and (3) a Delphi survey informed by findings from the systematic review, focus groups, and input from the research team and expert reference group to identify resource priorities. Workstream 2 will involve the co-design of the digital resource through workshops (n = 4) with key stakeholders, including health profession students, professionals, and individuals with lived experience of delirium. Lastly, Workstream 3 will involve a mixed methods evaluation of the digital resource. Outcomes include changes to delirium knowledge and self-efficacy towards delirium care, and health profession students experience of using the resource. DISCUSSION: Given the dearth of interdisciplinary educational resources on delirium for health profession students, a co-designed, interprofessional, digital education resource will be well-positioned to shape undergraduate delirium education. This research may enhance delirium education and the self-efficacy of future health professionals in providing delirium care, thereby improving practice and patients' experiences and outcomes. TRIAL REGISTRATION: Not applicable.

New horizons in undergraduate geriatric medicine education.

Current projections show that between 2000 and 2050, increasing proportions of older individuals will be cared for by a smaller number of healthcare workers, which will exacerbate the existing challenges faced by those who support this patient demographic. This review of a collection of Age and Ageing papers on the topic in the past 10 years explores (1) what best practice geriatrics education is and (2) how careers in geriatrics could be made more appealing to improve recruitment and retention. Based on these deeper understandings, we consider, as clinician educators, how to close the gap both pragmatically and theoretically. We point out paradigm shifting solutions that include innovations at the Undergraduate level, use of simulation, incorporation of learner and patient perspectives, upskilling professionals outside of Geriatrics and integration of practice across disciplines through Interprofessional Learning. We also identify an education research methodological gap. Specifically, there is an abundance of simple descriptive or justification studies but few clarification education studies; the latter are essential to develop fresh insights into how Undergraduate students can learn more effectively to meet the needs of the global ageing challenge. A case of improving understanding in delirium education is presented as an illustrative example of a new approach to exploring at greater depth education and outlines suggested directions for the future.

Sleep and circadian biomarkers of postoperative delirium (SLEEP-POD): protocol for a prospective and observational cohort study.

INTRODUCTION: Surgical patients over 70 experience postoperative delirium (POD) complications in up to 50% of procedures. Sleep/circadian disruption has emerged as a potential risk factor for POD in epidemiological studies. This protocol presents a single-site, prospective observational study designed to examine the relationship between sleep/circadian regulation and POD and how this association could be moderated or mediated by Alzheimer's disease (AD) pathology and genetic risk for AD. METHODS AND ANALYSIS: Study staff members will screen for eligible patients (age ≥70) seeking joint replacement or spinal surgery at Massachusetts General Hospital (MGH). At the inclusion visit, patients will be asked a series of questionnaires related to sleep and cognition, conduct a four-lead ECG recording and be fitted for an actigraphy watch to wear for 7 days before surgery. Blood samples will be collected preoperatively and postoperatively and will be used to gather information about AD variant genes (APOE-ε4) and AD-related pathology (total and phosphorylated tau). Confusion Assessment Method-Scale and Montreal Cognitive Assessment will be completed twice daily for 3 days after surgery. Seven-day actigraphy assessments and Patient-Reported Outcomes Measurement Information System questionnaires will be performed 1, 3 and 12 months after surgery. Relevant patient clinical data will be monitored and recorded throughout the study. ETHICS AND DISSEMINATION: This study is approved by the IRB at MGH, Boston, and it is registered with the US National Institutes of Health on ClinicalTrials.gov (NCT06052397). Plans for dissemination include conference presentations at a variety of scientific institutions. Results from this study are intended to be published in peer-reviewed journals. Relevant updates will be made available on ClinicalTrials.gov. TRIAL REGISTRATION NUMBER: NCT06052397.

Incidence of confusional syndrome (delirium) in a Latin American university hospital.

BACKGROUND: Little is known about the incidence of delirium and its subtypes in patients admitted to different departments of university hospitals in Latin America. OBJECTIVE: To determine the incidence of delirium and the frequency of its subtypes, as well as its associated factors, in patients admitted to different departments of a university hospital in Bogotá, Colombia. METHODS: A cohort of patients over 18 years of age admitted to the internal medicine (IM), geriatrics (GU), general surgery (GSU), orthopaedics (OU) and intensive care unit (ICU) services of a university hospital was followed up between January and June 2018. To detect the presence of delirium, we used the CAM (Confusion Assessment Method) and the CAM-ICU if the patient had decreased communication skills. The delirium subtype was characterised using the RASS (Richmond Agitation and Sedation Scale). Patients were assessed on their admission date and then every two days until discharged from the hospital. Those in whom delirium was identified were referred for specialised intra-institutional interdisciplinary management. RESULTS: A total of 531 patients admitted during the period were assessed. The overall incidence of delirium was 12% (95% CI, 0.3-14.8). They represented 31.8% of patients in the GU, 15.6% in the ICU, 8.7% in IM, 5.1% in the OU, and 3.9% in the GSU. The most frequent clinical display was the mixed subtype, at 60.9%, followed by the normoactive subtype (34.4%) and the hypoactive subtype (4.7%). The factors most associated with delirium were age (adjusted RR = 1.07; 95% CI, 1.05-1.09), the presence of four or more comorbidities (adjusted RR = 2.04; 95% CI, 1.31-3.20), and being a patient in the ICU (adjusted RR = 2.02; 95% CI, 1.22-3.35). CONCLUSIONS: The incidence of delirium is heterogeneous in the different departments of the university hospital. The highest incidence occurred in patients that were admitted to the GU. The mixed subtype was the most frequent one, and the main associated factors were age, the presence of four or more comorbidities, and being an ICU patient.

Metabolomic Analysis in Saliva and Different Brain Regions of Older Mice with Postoperative Delirium Behaviors.

Objective: Postoperative delirium (POD) has become a critical challenge with severe consequences and increased incidences as the global population ages. However, the underlying mechanism is yet unknown. Our study aimed to explore the changes in metabolites in three specific brain regions and saliva of older mice with postoperative delirium behavior and to identify potential non-invasive biomarkers. Methods: Eighteen-month-old male C57/BL6 mice were randomly assigned to the anesthesia/surgery or control group. Behavioral tests were conducted 24 h before surgery and 6, 9, and 24 h after surgery. Complement C3 (C3) and S100 calcium-binding protein B protein (S100beta) levels were measured in the hippocampus, and a metabolomics analysis was performed on saliva, hippocampus, cortex, and amygdala samples. Results: In total, 43, 33, 38, and 14 differential metabolites were detected in the saliva, hippocampus, cortex, and amygdala, respectively. "Pyruvate" "alpha-linolenic acid" and "2-oleoyl-1-palmitoy-sn-glycero-3-phosphocholine" are enriched in one common pathway and may be potential non-invasive biomarkers for POD. Common changes were observed in the three brain regions, with the upregulation of 1-methylhistidine and downregulation of D-glutamine. Conclusion: Dysfunctions in energy metabolism, oxidative stress, and neurotransmitter dysregulation are implicated in the development of POD. The identification of changes in the level of salivary metabolite biomarkers could aid in the development of noninvasive diagnostic methods for POD.

Validity and reliability of the Turkish version of the Informant Assessment of Geriatric Delirium Scale.

Background and purpose:

Delirium is a common complication developing in el­der­ly patients. Therefore, it is important to diagnose delirium earlier. Family caregivers play an active role in early diagnosis of de­lirium and build a bridge between health pro­fessionals and patients. The purpose of this research was to achieve the validity and reliability of the Turkish version of the Informant Assessment of Geriatric Delirium Scale (I-AGeD).

This is a methodological study. The sample comprised 125 caregivers ac­cepting to participate in the study and offering care to older patients with hip fracture aged ≥60 years. Data were gathered preoperatively and on postoperative days 0, 1 and 2. After achieving the linguistic and content validity of the scale, the known-groups comparison was used to achieve its construct validity. The ROC curve analysis was made to determine the sensitivity and specificity of the scale. Item-total correlations, item analysis based on the difference between the upper 27% and lower 27%, Kuder–Richardson 20 (KR-20) coefficient and parallel forms reliability with the NEECHAM Confusion Scale were adapted to assess discriminant indices of the items in the I-AGeD.

The item-total correlation coeffi­cients of the scale ranged from 0.54 to 0.89 and KR-20 coefficient ranged from 0.09 to 0.91 depending on the measurement times. According to the ROC curve analysis, the sensitivity and specificity of the scale were ≥ 91% and ≥ 96% respectively. The parallel forms reliability analysis showed a highly significant, strong negative relation at each measurement between the I-AGeD and the NEECHAM Confusion Scale. 

The I-AGeD is valid and reliable to diagnose delirium in older Turkish patients in perioperative processes.

Delirium screening in the emergency department: evaluation and intervention.

BACKGROUND: Between 8-17% of older adults, and up to 40% of those arriving from nursing homes, present with delirium upon admission to the Emergency Department (ED). However, this condition often remains undiagnosed by ED medical staff. We investigated the prevalence of delirium among patients aged 65 and older admitted to the ED and assessed the impact of a prospective study aimed at increasing awareness. METHODS: The study was structured into four phases: a "pre-intervention period" (T0); an "awareness period" (T1), during which information about delirium and its diagnosis was disseminated to ED staff; a "screening period" (T2), in which dedicated evaluators screened ED patients aged 65 and older; and a "post-intervention period" (T3), following the departure of the evaluators. Delirium screening was conducted using the Brief Confusion Assessment Method (bCAM) questionnaire. RESULTS: During the T0 and T1 periods, the rate of delirium diagnosed by ED staff was below 1%. The evaluators identified a delirium rate of 14.9% among the screened older adults during the T2 period, whereas the rate among those assessed by ED staff was between 1.6% and 1.9%. Following the evaluators' departure in the T3 period, the rate of delirium diagnosis decreased to 0.89%. CONCLUSIONS: This study underscores that a significant majority of older adult delirium cases remain undetected by ED staff. Despite efforts to increase awareness, the rate of diagnosis did not significantly improve. While the presence of dedicated delirium evaluators slightly increased the diagnosis rate among patients assessed by ED staff, this rate reverted to pre-intervention levels after the evaluators left. These findings emphasize the necessity of implementing mandatory delirium screening during ED triage and throughout the patient's stay.

Understanding the association between pain and delirium in older hospital inpatients: systematic review and meta-analysis.

OBJECTIVE: Delirium and pain are common in older adults admitted to hospital. The relationship between these is unclear, but clinically important. We aimed to systematically review the association between pain (at rest, movement, pain severity) and delirium in this population. METHODS: PubMed, EMBASE, CINAHL, PsycINFO, Cochrane and Web of Science were searched (January 1982-November 2022) for Medical Subject Heading terms and synonyms ('Pain', 'Analgesic', 'Delirium'). Study eligibility: (1) validated pain measure as exposure, (2) validated delirium tool as an outcome; participant eligibility: (1) medical or surgical (planned/unplanned) inpatients, (2) admission length ≥ 48 h and (3) median cohort age over 65 years. Study quality was assessed with the Newcastle Ottawa Scale. We collected/calculated odds ratios (ORs) for categorical data and standard mean differences (SMDs) for continuous data and conducted multi-level random-intercepts meta-regression models. This review was prospectively registered with PROSPERO [18/5/2020] (CRD42020181346). RESULTS: Thirty studies were selected: 14 reported categorical data; 16 reported continuous data. Delirium prevalence ranged from 2.2 to 55%. In the multi-level analysis, pain at rest (OR 2.14; 95% confidence interval [CI] 1.39-3.30), movement (OR 1.30; 95% CI 0.66-2.56), pain categorised as 'severe' (OR 3.42; 95% CI 2.09-5.59) and increased pain severity when measured continuously (SMD 0.33; 95% CI 0.08-0.59) were associated with an increased delirium risk. There was substantial heterogeneity in both categorical (I2 = 0%-77%) and continuous analyses (I2 = 85%). CONCLUSION: An increase in pain was associated with a higher risk of developing delirium. Adequate pain management with appropriate analgesia may reduce incidence and severity of delirium.