149 8 DELIRIUM | PART THREE The second aim of our study was to construct a prediction algorithm for delirium in advanced cancer patients, based on patient characteristics which can be quickly and easily assessed during an admission interview or with additional clinical or laboratory testing. We found that patients with an emergency admittance and with a metabolic imbalance have a clinically relevant high risk to develop delirium. In this group the incidence rate of delirium was 33 per 100 patients. The strength of this prediction algorithm is its simplicity and feasibility in clinical practice. Based on this algorithm, patients at high risk for delirium could be selected for targeted intervention strategies to prevent delirium. Future prediction model development and validation should follow methods including strategies to counter low sample size and expand case-mix and generalizability [73]. In these models, the potential predictive role for blood / urine-based or cerebrospinal fluid biomarkers, such as insulin-like growth factor-1, inflammatory markers, and neurofilament light could also be explored [74]. There are numerous screening tools validated for the assessment of delirium. A recent review by De et al [75] comprehensively identified 21 separate delirium screening tools used in hospital inpatients. However, few validation studies have been carried out in representative populations of people with advanced cancer or have included sufficient medical diagnostic detail to allow a determination of whether people with cancer were included in the study [76-81]. In chapter 6 the accuracy of the Delirium Observation Screening (DOS) scale [82] as a screening tool for delirium was tested against the Delirium Rating Scale Revised 98 (DRS-R-98) [83] in a multicenter, prospective, cohort study in 193 patients with advanced cancer. In the present study, sensitivity of the DOS was >99.9% (95% CI 95.8-100%); specificity was 99.5% (95% CI 95.5-99.96%); positive predictive value was 94.6% (95% CI 88.0-97.7%); and negative predictive value was >99.9% (95% CI 96.1100.0%). Our results indicate that the DOS, a 13-point screen for delirium designed to be completed by a nurse, is an accurate screening tool for delirium in patients with advanced cancer, that can be easily incorporated into clinical practice. In Chapter 7 we describe the results of a multicenter, phase III RCT that compared the efficacy and tolerability of olanzapine to haloperidol for the treatment of delirium in hospitalized patients with advanced cancer. Treatment with olanzapine did not result in improvement of delirium response rate or time to response compared to haloperidol. Treatment related adverse events leading to drug discontinuation were reported more frequently in the haloperidol arm than in the olanzapine arm; however, differences were not statistically significant. Olanzapine is a safe alternative to haloperidol in delirious cancer patients, and may be of particular interest in patients in whom haloperidol is contraindicated. Antipsychotic agents are the class of drugs most studied for delirium treatment. A 2018 Cochrane review of antipsychotic agents for the treatment of delirium in non-ICU patients reported findings of 9 trials involving 727 participants [84]. Data were generally of poor quality, with antipsychotic agents having no effect on delirium severity, symptom resolution or mortality. Data on duration, length of stay, discharge destination or QoL were lacking, and reporting of adverse effects was absent or poor. A 2020 Cochrane review of four trials testing drug treatment for delirium in terminally ill adults [85] found that the efficacy of drug therapy was unclear owing to the mostly low to very low quality of evidence in these studies. One of the included RCT’s was the first placebo-controlled
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