18 Chapter 3 describes the results of a systematic review of randomized controlled trials (RCT’s) with antiemetics for the prevention of delayed CINV in cancer patients treated with MEC. In Chapter 4, a multicenter, randomized, phase III, non-inferiority study to assess the efficacy and tolerability of dexamethasone-sparing strategies for the prevention of delayed CINV, and specifically nausea, after MEC is described. Part Three: Delirium Delirium is a severe neuropsychiatric syndrome characterized by the acute onset of deficits in attention and other aspects of cognition. Patients often have altered arousal, from reduced responsiveness at a near-coma level to hypervigilance and severe agitation. They may also experience highly distressing symptoms of psychosis, including delusions and hallucinations, and altered mood. Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) diagnostic criteria for delirium [71] are as follows: disturbance of consciousness (ie, reduced clarity of awareness of the environment) occurs, with reduced ability to focus, sustain, or shift attention. The features of delirium tend to fluctuate in presence and severity. Delirium is associated with considerable distress in patients and caregivers [72, 73]. The incidence of delirium in advanced cancer patients has been reported as varying greatly [74, 75]. During hospitalization, 16.5% [76] to 18% [77] of patients with cancer or a haematological malignancy admitted to oncology or internal medicine units developed delirium. Up to 88% of patients develop delirium in the last hours to weeks of life [78]. This variation depends on the study population, the delirium definition and method of assessment used and staff training, as well as delirium subtype (hyperactive, hypoactive, or mixed) and methods used for subtype classification. The risk of delirium is determined by predisposing risk factors (i.e. the background characteristics of patients) and precipitating risk factors (i.e. acute insults, injury or drugs) (Figure 3). Typically, more than one precipitating factor is present in patients [79, 80]. Studies in oncology settings have not documented specific socio-demographic and disease-related predictive factors for delirium. As a consequence, the number of patients with delirium in these studies has often been insufficient to precisely determine associated risk factors. An accurate and timely predictionmodel for deliriumwould facilitate early implementation of prevention measures based on individual risk profiles. However, existing delirium prediction models use different methods of delirium identification and different risk factors for model calibration, and do not have adequate predictive capabilities [81-82]. In chapter 5, we evaluate the incidence of delirium and its risk factors in hospitalized patients with advanced cancer in a retrospective cohort study. In this chapter, a prediction algorithm that we have developed to help identify patients at high risk of delirium is described.
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