Delirium is associated with an increased morbidity and in-hospital mortality in cancer patients: Results from a prospective cohort study

Palliative prognostic scores for survival prediction of cancer patients: a systematic review and meta-analysis

BACKGROUND

The palliative prognostic score is the most widely validated prognostic tool for cancer survival prediction, with modified versions available. A systematic evaluation of palliative prognostic score tools is lacking. This systematic review and meta-analysis aimed to evaluate the performance and prognostic utility of palliative prognostic score, delirium-palliative prognostic score, and palliative prognostic score without clinician prediction in predicting 30-day survival of cancer patients and to compare their performance.

METHODS

Six databases were searched for peer-reviewed studies and grey literature published from inception to June 2, 2023. English studies must assess palliative prognostic score, delirium-palliative prognostic score, or palliative prognostic score without clinician-predicted survival for 30-day survival in adults aged 18 years and older with any stage or type of cancer. Outcomes were pooled using the random effects model or summarized narratively when meta-analysis was not possible.

RESULTS

A total of 39 studies (n = 10 617 patients) were included. Palliative prognostic score is an accurate prognostic tool (pooled area under the curve [AUC] = 0.82, 95% confidence interval [CI] = 0.79 to 0.84) and outperforms palliative prognostic score without clinician-predicted survival (pooled AUC = 0.74, 95% CI = 0.71 to 0.78), suggesting that the original palliative prognostic score should be preferred. The meta-analysis found palliative prognostic score and delirium-palliative prognostic score performance to be comparable. Most studies reported survival probabilities corresponding to the palliative prognostic score risk groups, and higher risk groups were statistically significantly associated with shorter survival.

CONCLUSIONS

Palliative prognostic score is a validated prognostic tool for cancer patients that can enhance clinicians' confidence and accuracy in predicting survival. Future studies should investigate if accuracy differs depending on clinician characteristics. Reporting of validation studies must be improved, as most studies were at high risk of bias, primarily because calibration was not assessed.

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.

Prognostic Implications of Delirium After Left Ventricular Assist Device Implantation: A Retrospective Study

BACKGROUND

In critically ill patients, delirium is a prognostic indicator of morbidity and mortality.

OBJECTIVE

This study investigates the impact of a delirium diagnosis on outcomes after left ventricular assist device (LVAD) implantation.

METHODS

This retrospective study included all adult patients who received LVADs at our institution between January 2016 and December 2020. We compared preimplantation characteristics between the two groups, with and without a diagnosis of delirium, and compared their outcomes, including 1-month, 6-month, and in-hospital mortality, as well as reintubation rate, length of stay, discharge disposition, and readmission rates.

RESULTS

In total, 361 patients (26.7% women and 75.8% African American) received durable LVADs. Ninety-four patients (26.1%) were diagnosed with delirium during the index admission. Preimplantation demographic characteristics, past medical and psychiatric conditions, Interagency Registry for Mechanically Assisted Circulatory Support Profile, and laboratory values did not differ between the two groups with and without a diagnosis of delirium; older age (59 vs 56; P = 0.03) was associated with delirium. Delirium diagnosis was associated with higher 1-month (P = 0.007), 6-month (P = 0.004), and in-hospital mortality (P < 0.001), unplanned reintubations (P < 0.001), and a lower likelihood of discharge home (P = 0.03). Total hospital and intensive care unit length of stay were higher in patients with a diagnosis of delirium, though these results were not statistically significant. Readmission to the hospital after index admission was quicker in patients with a diagnosis of delirium, but this result was not statistically significant.

CONCLUSIONS

In this study, a diagnosis of delirium during the LVAD implantation admission was associated with higher mortality, adverse postsurgical outcomes, and unfavorable discharge dispositions. Future prospective research is needed to validate the prognostic implications of delirium in both the short and long term. Additionally, there is a need to identify modifiable risk factors associated with delirium to promote early diagnosis and implement evidence-based management strategies to enhance outcomes within this population.

Delivering Palliative and Supportive Care for Older Adults with Cancer: Interactions between Palliative Medicine and Geriatrics

The world's population is aging rapidly, with projections indicating that by 2050 one in six people will be aged ≥65 years. As a result, the number of cancer cases in older people is expected to increase significantly. Palliative care is an essential component of cancer care with a direct impact on quality of life. However, older adults with cancer often suffer from multiple comorbidities, cognitive impairment, and frailty, posing unique challenges in the delivery of palliative care. The complex healthcare needs of older patients with cancer therefore require a comprehensive assessment, including a geriatric evaluation. Collaboration between geriatrics and palliative care can offer a solution to the challenges faced by older people with cancer, since this is a population with overlapping concerns for both disciplines. This review highlights the importance of palliative care for older adults with cancer and the benefits of a multidisciplinary approach. It also addresses the coordination of palliative care and geriatrics for specific symptom management and decision making.

Metabolic syndrome and the risk of postoperative delirium and postoperative cognitive dysfunction: a multi-centre cohort study

BACKGROUND

Metabolic syndrome and its components are risk factors for cognitive impairment, but their contribution to perioperative neurocognitive disorders is unknown. We examined their associations with the risk of postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) in older patients.

METHODS

In 765 male and female participants aged ≥65 years, we measured preoperative metabolic parameters and screened for POD for 7 days or until discharge. POCD was defined through comparison of cognitive change on six neuropsychological tests with non-surgical controls. Multiple logistic regression analyses examined the association of metabolic parameters with risk of POD and POCD with adjustment for age, sex, and surgery type.

RESULTS

A total of 149 patients (19.5% of 765) developed POD and 53 (10.1% of 520 attendees) had POCD at 3 months. Patients with metabolic syndrome were at 1.85-fold higher risk of POD (95% confidence interval [CI] 1.26-2.70). Each 1 mmol L^-1 higher high-density lipoprotein cholesterol (HDL-C) was associated with a 0.47-fold lower POD risk (95% CI 0.30-0.74). Each 1 kg m^-2 higher body mass index (BMI) was associated with a 1.09-fold higher POCD risk (95% CI 1.02- 1.16).

CONCLUSIONS

Older surgical patients with metabolic syndrome were at increased risk of POD. Only reduced HDL-C was significantly associated with POD. For POCD, a higher preoperative BMI was identified as a risk factor. These findings add to mounting evidence of a distinct epidemiology of POD and POCD. Screening programmes taking advantage of HDL-C and BMI measurements and of metabolic interventions in reducing perioperative neurocognitive disorders should be evaluated.

CLINICAL TRIAL REGISTRATION

NCT02265263.

Risk factors for delirium in advanced cancer patients: A systematic review and meta-analysis

PURPOSE

To systematically collect published research in order to identify and quantify risk factors for delirium in advanced cancer patients.

METHODS

The Cochrane Library, PubMed, Proquest, CINAHL, Web of Science, Ovid MEDLINE, Embase, Scopus, Chinese Wanfang Data, Chinese Periodical Full-text Database (VIP), Chinese Biomedical Literature Database (CBM), and Chinese National Knowledge Infrastructure (CNKI) were systematically searched for cohort or case-control studies reporting individual risk factors for delirium among advanced-stage cancer patients published prior to March 2022. The Newcastle-Ottawa Scale was used to assess the methodological quality of included studies. The pooled adjusted odds ratio (aOR) and its 95% confidence interval were calculated using the RevMan 5.4 software package.

RESULTS

A total of 15 studies with data from 3106 advanced cancer patients were included in our analysis. Nine studies were high-quality and six were of moderate quality. Pooled analyses revealed that 11 risk factors were statistically significant. High-intensity risk factors included sleep disturbance, infection, cachexia and the Palliative Prognostic Index; medium-intensity risk factors included male sex, renal failure, dehydration and drowsiness; low-intensity risk factors included age, total bilirubin and opioid use. Antibiotic use was found to have been a protective factor.

CONCLUSIONS

We identified 12 independent risk factors that were significantly associated with delirium in advanced cancer patients and provide an evidence-based foundation to implement appropriate preventive strategies.

Predisposing and precipitating risk factors for delirium in gastroenterology and hepatology: Subgroup analysis of 718 patients from a hospital-wide prospective cohort study

BACKGROUND AND AIMS

Delirium is the most common acute neuropsychiatric syndrome in hospitalized patients. Higher age and cognitive impairment are known predisposing risk factors in general hospital populations. However, the interrelation with precipitating gastrointestinal (GI) and hepato-pancreato-biliary (HPB) diseases remains to be determined.

PATIENTS AND METHODS

Prospective 1-year hospital-wide cohort study in 29'278 adults, subgroup analysis in 718 patients hospitalized with GI/HPB disease. Delirium based on routine admission screening and a DSM-5 based construct. Regression analyses used to evaluate clinical characteristics of delirious patients.

RESULTS

Delirium was detected in 24.8% (178/718). Age in delirious patients (median 62 years [IQR 21]) was not different to non-delirious (median 60 years [IQR 22]), p = 0.45). Dementia was the strongest predisposing factor for delirium (OR 66.16 [6.31-693.83], p < 0.001). Functional impairment, and at most, immobility increased odds for delirium (OR 7.78 [3.84-15.77], p < 0.001). Patients with delirium had higher in-hospital mortality rates (18%; OR 39.23 [11.85-129.93], p < 0.001). From GI and HPB conditions, cirrhosis predisposed to delirium (OR 2.11 [1.11-4.03], p = 0.023), while acute renal failure (OR 4.45 [1.61-12.26], p = 0.004) and liver disease (OR 2.22 [1.12-4.42], p = 0.023) were precipitators. Total costs were higher in patients with delirium (USD 30003 vs. 10977; p < 0.001).

CONCLUSION

Delirium in GI- and HPB-disease was not associated with higher age per se, but with cognitive and functional impairment. Delirium needs to be considered in younger adults with acute renal failure and/or liver disease. Clinicians should be aware about individual risk profiles, apply preventive and supportive strategies early, which may improve outcomes and lower costs.

Impact of delirium on the outcome of stroke: a prospective, observational, cohort study

Introduction

Delirium is an acute fluctuating disorder of attention and awareness, which often complicates the clinical course of several conditions, including acute stroke. The aim of the present study was to determine whether delirium occurrence impacts the outcome of patients with acute stroke.

Methods

The study design is single center, prospective, observational. We consecutively enrolled patients admitted to the stroke unit from April to October 2020. Inclusion criteria were age ≥ 18 years and diagnosis of acute stroke. Exclusion criteria were stroke mimics, coma, and terminal conditions. All patients were screened for delirium upon admission, within 72 h, and whenever symptoms suggesting delirium occurred by means of the Confusion Assessment Method for Intensive Care Unit and the Richmond Agitation Sedation Scale. Outcomes were evaluated with the 90-days modified Rankin Scale (mRS) by telephone interview.

Results

The final study cohort consisted of 103 patients (62 men; median age 75 years, interquartile range 63–81). Thirty-one patients (30%) developed delirium. In the multivariate ordinal logistic regression, patients with delirium had higher mRS scores at 3 months (DLR + : mRS = 4 (3–6); DLR–: mRS = 1 (1–3); adjusted odds ratio = 4.83; CI = 1.88–12.35; p = 0.006). Delirium was a risk factor for death (mRS = 6) in the univariate logistic regression (OR 4.5, CI = 1.44–14.07; p = 0.010), but not in the adjusted analysis (OR 3.45; CI = 0.66–17.95; p = 0.142). Survival time during 90-days follow-up was shorter in the delirium group (Log Rank χ² 3.89; p = 0.048).

Conclusion

Delirium negatively impacts the prognosis of patients with acute stroke. Patients with post-stroke delirium have a worse functional outcome and a shorter survival.

Early Diagnosis of Delirium in Palliative Care Patients Decreases Mortality and Necessity of Palliative Sedation: Results of a Prospective Observational Study

Introduction: Delirium in end-of-life patients is reported to be between 13% and 42% and up to 80% in the terminal phase. It is a serious clinical situation, often a cause of death due to the frequent ineffectiveness of treatments. This study aimed to assess whether and how much precocity of diagnosis, hitherto little considered, could affect the outcomes and prognosis of delirium in palliative care settings.

Methods: Patients consecutively admitted to a palliative care unit (PCU) between October 2018 and December 2019, cared for both in hospice and home programs, were analyzed. All patients were subjected to a careful procedure aimed at recognizing the onset of delirium. The first step was the detection of prodromal "sentinel" symptoms related to incoming delirium. PCU staff and family members/caregivers were trained to observe the patients and immediately identify the appearance of even one symptom. The final diagnosis was performed with the 4AT (4 A’s test). Patients were then included in the categories of "early" or "slow" diagnosis (cut-off: four hours) depending on the time between sentinel symptom observation and the final diagnosis of delirium.

Results: Among 503 admitted patients, 95 developed delirium. Confusion was the most frequent sentinel symptom (49.5%). The early diagnosis was more frequent in hospice than in home care (p-value<0.0001). Delirium was positively resolved in 43 patients, of which 25 with an early diagnosis (p-value=0.038). Time to resolution was shorter in the case of early diagnosis (7.1 vs. 13.7 hours in hospice patients; p-value=0.018). Palliative sedation was performed on 25 patients, but only 8 of them had an early diagnosis.

Conclusion: Time of diagnosis was important in determining the clinical outcomes of patients in charge of PCU who experienced delirium. The early diagnosis reduced both mortality and the necessity of palliative sedation.

Delirium in Older Adults: What a Surgeon Needs to Know

Delirium remains a challenging clinical problem in hospitalized older adults, especially for postoperative patients. This complication, with a high risk of postoperative mortality and an increased length of stay, frequently occurs in older adult patients. This brief narrative paper aims to review the recent literature regarding delirium and its most recent update. We also offer physicians a brief and essential clinical practice guide to managing this acute and common disease.

Prevalence of Anti-neural Autoantibodies in a Psychiatric Patient Cohort-Paradigmatic Application of Criteria for Autoimmune-Based Psychiatric Syndromes

BACKGROUND

Anti-neural autoantibodies associated with psychiatric syndromes is an increasing phenomenon in psychiatry. Our investigation aimed to assess the frequency and type of neural autoantibodies associated with distinct psychiatric syndromes in a mixed cohort of psychiatric patients.

METHODS

We recruited 167 patients retrospectively from the Department of Psychiatry and Psychotherapy, University Medical Center Göttingen for this study. Clinical features including the assessment of psychopathology via the Manual for Assessment and Documentation of Psychopathology in Psychiatry (AMDP), neurological examination, cerebrospinal fluid (CSF), magnetic resonance imaging (MRI) and electroencephalography (EEG) analysis were done in patients. Serum and or CSF anti- neural autoantibodies were measured in all patients for differential diagnostic reasons.

RESULTS

We divided patients in three different groups: (1) psychiatric patients with CSF and/or serum autoantibodies [PSYCH-AB+, n = 25 (14.9%)], (2) psychiatric patients with CSF autoantibodies [PSYCH-AB CSF+, n = 13 (7.8%)] and (3) those psychiatric patients without autoantibodies in serum and/or CSF [PSYCH-AB-, n = 131]. The prevalence of serum neural autoantibodies was 14.9% (PSYCH-AB+), whereas 7.2% had CSF autoantibodies (PSYCH-AB CSF+) in our psychiatric cohort. The most prevalent psychiatric diagnoses were neurocognitive disorders (61-67%) and mood disorders (25-36%) in the patients presenting neural autoantibodies (PSYCH-AB+ and PSYCH-AB CSF+). However, psychiatric diagnoses, neurological deficits, and laboratory results from CSF, EEG or MRI did not differ between the three groups. To evaluate the relevance of neural autoantibody findings, we applied recent criteria for possible, probable, or definitive autoimmune based psychiatric syndromes in an paradigmatic patient with delirium and in the PSYCH-AB+ cohort. Applying criteria for any autoimmune-based psychiatric syndromes, we detected a probable autoimmune-based psychiatric syndrome in 13 of 167 patients (7.8%) and a definitive autoimmune-based psychiatric syndrome in 11 of 167 patients (6.6%).

CONCLUSIONS

Neural autoantibodies were detected mainly in patients presenting neurocognitive and mood disorders in our psychiatric cohort. The phenotypical appearance of psychiatric syndromes in conjunction with neural autoantibodies did not differ from those without neural autoantibodies. More research is therefore warranted to optimize biomarker research to help clinicians differentiate patients with potential neural autoantibodies when a rapid clinical response is required as in delirium states.

Profiling Delirium Progression in Elderly Patients via Continuous-Time Markov Multi-State Transition Models

Poor recognition of delirium among hospitalized elderlies is a typical challenge for health care professionals. Considering methodological insufficiency for assessing time-varying diseases, a continuous-time Markov multi-state transition model (CTMMTM) was used to investigate delirium evolution in elderly patients. This is a longitudinal observational study performed in September 2016 in an Italian hospital. Change of delirium states was modeled according to the 4AT score. A Cox model (CM) and a CTMMTM were used for identifying factors affecting delirium onset both with a two-state and three-state model. In this study, 78 patients were enrolled and evaluated for 5 days. Both the CM and the CTMMTM show that urine catheter (UC), aging, drugs, and invasive devices (ID) are risk factors for delirium onset. The CTMMTM model shows that transition from no-delirium/cognitive impairment to delirium was associated with aging (HR = 1.14; 95%CI, 1.05, 1.23) and neuroleptics (HR = 4.3; 1.57, 11.77), dopaminergic drugs (HR = 3.89; 1.2, 12.6), UC (HR = 2.92; 1.09, 7.79) and ID (HR = 1.67; 103, 2.71). These results are confirmed by the multivariable model. Aging, ID, antibiotics, drugs affecting the central nervous system, and absence of moving ability are identified as the significant predictors of delirium. Additionally, it seems that modeling with CTMMTM may show associations that are not directly detectable with the traditional CM.