Predicting incident delirium diagnoses using data from primary-care electronic health records

Development and validation of a nomogram to predict postoperative delirium in older patients after major abdominal surgery: a retrospective case-control study

BACKGROUND

Postoperative delirium is a common complication in older patients, with poor long-term outcomes. This study aimed to investigate risk factors and develop a predictive model for postoperative delirium in older patients after major abdominal surgery.

METHODS

This study retrospectively recruited 7577 patients aged ≥ 65 years who underwent major abdominal surgery between January 2014 and December 2018 in a single hospital in Beijing, China. Patients were divided into a training cohort (n = 5303) and a validation cohort (n = 2224) for univariate and multivariate logistic regression analyses and to build a nomogram. Data were collected for 43 perioperative variables, including demographics, medical history, preoperative laboratory results, imaging, and anesthesia information.

RESULTS

Age, chronic obstructive pulmonary disease, white blood cell count, glucose, total protein, creatinine, emergency surgery, and anesthesia time were associated with postoperative delirium in multivariate analysis. We developed a nomogram based on the above 8 variables. The nomogram achieved areas under the curve of 0.731 and 0.735 for the training and validation cohorts, respectively. The discriminatory ability of the nomogram was further assessed by dividing the cases into three risk groups (low-risk, nomogram score < 175; medium-risk, nomogram score 175~199; high-risk, nomogram score > 199; P < 0.001). Decision curve analysis revealed that the nomogram provided a good net clinical benefit.

CONCLUSIONS

We developed a nomogram that could predict postoperative delirium with high accuracy and stability in older patients after major abdominal surgery.

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.

Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records

COVID-19 has a range of complications, from no symptoms to severe pneumonia. It can also affect multiple organs including the nervous system. COVID-19 affects the brain, leading to neurological symptoms such as delirium. Delirium, a sudden change in consciousness, can increase the risk of death and prolong the hospital stay. However, research on delirium prediction in patients with COVID-19 is insufficient. This study aimed to identify new risk factors that could predict the onset of delirium in patients with COVID-19 using machine learning (ML) applied to nursing records. This retrospective cohort study used natural language processing and ML to develop a model for classifying the nursing records of patients with delirium. We extracted the features of each word from the model and grouped similar words. To evaluate the usefulness of word groups in predicting the occurrence of delirium in patients with COVID-19, we analyzed the temporal changes in the frequency of occurrence of these word groups before and after the onset of delirium. Moreover, the sensitivity, specificity, and odds ratios were calculated. We identified (1) elimination-related behaviors and conditions and (2) abnormal patient behavior and conditions as risk factors for delirium. Group 1 had the highest sensitivity (0.603), whereas group 2 had the highest specificity and odds ratio (0.938 and 6.903, respectively). These results suggest that these parameters may be useful in predicting delirium in these patients. The risk factors for COVID-19-associated delirium identified in this study were more specific but less sensitive than the ICDSC (Intensive Care Delirium Screening Checklist) and CAM-ICU (Confusion Assessment Method for the Intensive Care Unit). However, they are superior to the ICDSC and CAM-ICU because they can predict delirium without medical staff and at no cost.

Increased α-2,6 sialic acid on microglia in amyloid pathology is resistant to oseltamivir

Terminal sialic acid residues are present on most glycoproteins and glycolipids, but levels of sialylation are known to change in the brain throughout the lifespan as well as during disease. Sialic acids are important for numerous cellular processes including cell adhesion, neurodevelopment, and immune regulation as well as pathogen invasion into host cells. Neuraminidase enzymes, also known as sialidases, are responsible for removal of terminal sialic acids in a process known as desialylation. Neuraminidase 1 (Neu1) cleaves the α-2,6 bond of terminal sialic acids. Aging individuals with dementia are often treated with the antiviral medication oseltamivir, which is associated with induction of adverse neuropsychiatric side effects; this drug inhibits both viral and mammalian Neu1. The present study tested whether a clinically relevant antiviral dosing regimen of oseltamivir would disrupt behavior in the 5XFAD mouse model of Alzheimer's disease amyloid pathology or wild-type littermates. While oseltamivir treatment did not impact mouse behavior or modify amyloid plaque size or morphology, a novel spatial distribution of α-2,6 sialic acid residues was discovered in 5XFAD mice that was not present in wild-type littermates. Further analyses revealed that α-2,6 sialic acid residues were not localized the amyloid plaques but instead localized to plaque-associated microglia. Notably, treatment with oseltamivir did not alter α-2,6 sialic acid distribution on plaque-associated microglia in 5XFAD mice which may be due to downregulation of Neu1 transcript levels in 5XFAD mice. Overall, this study suggests that plaque-associated microglia are highly sialylated and are resistant to change with oseltamivir, thus interfering with microglia immune recognition of and response to amyloid pathology.

Incidence and associations of hospital delirium diagnoses in 85,979 people with severe mental illness: A data linkage study

INTRODUCTION

Delirium is an acute neuro-psychiatric disturbance precipitated by a range of physical stressors, with high morbidity and mortality. Little is known about its relationship with severe mental illness (SMI).

METHODS

We conducted a retrospective cohort study using linked data analyses of the UK Clinical Practice Research Datalink (CPRD) and Hospital Episodes Statistics (HES) databases. We ascertained yearly hospital delirium incidence from 2000 to 2017 and used logistic regression to identify associations with delirium diagnosis in a population with SMI.

RESULTS

The cohort included 249,047 people with SMI with median follow-up time in CPRD of 6.4 years. A total of 85,979 patients were eligible for linkage to HES. Delirium incidence increased from 0.04 (95% CI 0.02-0.07) delirium associated admissions per 100 person-years in 2000 to 1.05 (95% CI 0.93-1.17) per 100 person-years in 2017, increasing most notably from 2010 onwards. Delirium was associated with older age at study entry (OR 1.05 per year, 95% CI 1.05-1.06), SMI diagnosis of bipolar affective disorder (OR 1.66, 95% CI 1.44-1.93) or other psychosis (OR 1.56, 95% CI 1.35-1.80) relative to schizophrenia, and more physical comorbidities (OR 1.08 per additional comorbidity of the Charlson Comorbidity Index, 95% CI 1.02-1.14). Patients with delirium received more antipsychotic medication during follow-up (1-2 antipsychotics OR 1.65, 95% CI 1.44-1.90; >2 antipsychotics OR 2.49, 95% CI 2.12-2.92).

CONCLUSIONS

The incidence of recorded delirium diagnoses in people with SMI has increased in recent years. Older people prescribed more antipsychotics and with more comorbidities have a higher incidence. Linked electronic health records are feasible for exploring hospital diagnoses such as delirium in SMI.

Clinical Risk Group as a predictor of mortality in delirious older adults in the emergency department

BACKGROUND

In older people, chronicity is associated with delirium, which in turn increases the risk of developing poor clinical outcomes like nursing home admission and death. The aim is to determine whether chronicity, as assessed by Clinical Risk Groups (CRG), is an independent predictor of mortality in older adults with delirium seen in the emergency department (ED).

METHODS

Prospective study with 18-month follow-up. Included patients were aged 65 years or older, admitted from 1 January to 31 December 2020, and diagnosed and coded for delirium in the ED of a secondary hospital. Patients were followed for 18 months. A survival analysis was performed using the Kaplan-Meier method and a multivariate Cox proportional hazards model.

RESULTS

The study included 125 patients (56 % men, mean age 81.2 years, standard deviation [SD] 7.5). At baseline, level 0 chronicity was present in 4.7 % of the patients; level 1, 23.4 %; level 2, 32.8 %; and level 3, 39.1 %. By study end, 29.68 % (n = 38) had died. Mean survival in the total sample was 176.6 (standard error 25.8) days. Level 3 chronicity was associated with a significantly higher risk of mortality (hazard ratio 3.41, 95 % confidence interval 1.31-8.96).

CONCLUSIONS

Level 3 chronicity, as assessed by Clinical Risk Groups, is an independent predictor of mortality in older ED patients with delirium. Delirium leads to an increased level of chronicity over the following months.

The degree of frailty as a translational measure of health in aging

Frailty is a multiply determined, age-related state of increased risk for adverse health outcomes. We review how the degree of frailty conditions the development of late-life diseases and modifies their expression. The risks for frailty range from subcellular damage to social determinants. These risks are often synergistic-circumstances that favor damage also make repair less likely. We explore how age-related damage and decline in repair result in cellular and molecular deficits that scale up to tissue, organ and system levels, where they are jointly expressed as frailty. The degree of frailty can help to explain the distinction between carrying damage and expressing its usual clinical manifestations. Studying people-and animals-who live with frailty, including them in clinical trials and measuring the impact of the degree of frailty are ways to better understand the diseases of old age and to establish best practices for the care of older adults.

Delirium: A Marker of Vulnerability in Older People

Delirium is an acute neuropsychiatric syndrome and one of the most common presenting symptoms of acute medical illnesses in older people. Delirium can be triggered by a single cause, but in most cases, it is multifactorial as it depends on the interaction between predisposing and precipitating factors. Delirium is highly prevalent in older patients across various settings of care and correlates with an increased risk of adverse clinical outcomes. Several pathophysiological mechanisms may contribute to its onset, including neurotransmitter imbalance, neuroinflammation, altered brain metabolism, and impaired neuronal network connectivity. Several screening and diagnostic tools for delirium exist, but they are unfortunately underutilized. Additionally, the diagnosis of delirium superimposed on dementia poses a formidable challenge – especially if dementia is severe. Non-pharmacological approaches for the prevention and multidomain interventions for the treatment of delirium are recommended, given that there is currently no robust evidence of drugs that can prevent or resolve delirium. This article aims to review the current understanding about delirium in older people. To achieve this goal, we will describe the epidemiology and outcomes of the syndrome, the pathophysiological mechanisms that are supposed to be involved, the most commonly used tools for screening and diagnosis, and prevention strategies and treatments recommended. This review is intended as a brief guide for clinicians in hospital wards to improve their knowledge and practice. At the end of the article, we propose an approach to improve the quality of care provided to older patients throughout a systematic detection of delirium.

Delirium in COVID-19: epidemiology and clinical correlations in a large group of patients admitted to an academic hospital

BACKGROUND

Delirium incidence and clinical correlates in coronavirus disease-19 (COVID-19) pneumonia are still poorly investigated.

AIM

To describe the epidemiology of delirium in patients hospitalized for suspect COVID-19 pneumonia during the pandemic peak in an academic hospital of Northern Italy, identify its clinical correlations and evaluate the association with mortality.

METHODS

The clinical records of 852 patients admitted for suspect COVID-19 pneumonia, defined as respiratory symptoms or fever or certain history of contact with COVID-19 patients, plus chest CT imaging compatible with alveolar-interstitial pneumonia, were retrospectively analyzed. Delirium was defined after careful revision of daily clinical reports in accordance with the Confusion Assessment Method criteria. Data on age, clinical presentation, comorbidities, drugs, baseline lab tests and outcome were collected. The factors associated with delirium, and the association of delirium with mortality, were evaluated through binary logistic regression models.

RESULTS

Ninety-four patients (11%) developed delirium during stay. They were older (median age 82, interquartile range, IQR 78-89, vs 75, IQR 63-84, p < 0.001), had more neuropsychiatric comorbidities and worse respiratory exchanges at baseline. At multivariate models, delirium was independently and positively associated with age [odds ratio (OR) 1.093, 95% confidence interval (CI) 1.046-1.143, p < 0.001], use of antipsychotic drugs (OR 4.529, 95% CI 1.204-17.027, p = 0.025), serum urea and lactate-dehydrogenase at admission. Despite a higher mortality in patients with delirium (57% vs 30%), this association was not independent of age and respiratory parameters.

CONCLUSIONS

Delirium represents a common complication of COVID-19 and a marker of severe disease course, especially in older patients with neuropsychiatric comorbidity.