The prevalence of delirium in acute medical inpatients is high, with estimates ranging from 10% to 31%. [1] Short-and long-term complications of delirium include increased mortality and length of hospitalisation, post-discharge institutionalisation, and long-term functional and cognitive decline. [2] This is a considerable healthcare burden: in developed countries the cost of delirium is equal to that of falls and diabetes mellitus. [3] A number of risk factors for delirium have been identified, including predisposing factors such as dementia and advancing age and acute precipitating factors such as drugs, infections and metabolic abnormalities. [2] Protective factors include a higher level of education, a marker of cognitive reserve. [4] Unfortunately, the data on delirium outcomes and risk factors in general medical inpatients are derived almost exclusively from geriatric populations in developed countries, a very different population to acute medical admissions in developing country settings with a high HIV/tuberculosis (TB) burden. [5] Furthermore, the few studies from developing country settings such as sub-Saharan Africa (SSA) have either been conducted in medical patients aged >60 years or in specialised populations, such as psychiatric and intensive care settings. [6][7][8] In developed countries such as the USA, studies were done among HIV-infected populations before universal access to combination antiretroviral therapy (ART). [9][10][11][12][13][14] HIV targets the central nervous system (CNS) with resultant neurocognitive impairment (NCI), a well-described predisposing risk factor for delirium. Acute and opportunistic infections (OIs), also known risk factors for delirium, occur more commonly with advancing immunosuppression. It is therefore unsurprising that studies have shown high prevalence rates of delirium (3.7 -57%) in HIV-infected populations. [6,[11][12][13] Delirium in HIV is often concomitant with NCI, in 8 -22% of cases. [15] Combination ART both prevents and improves NCI and decreases the incidences of acute and OIs. Widespread access to ART may therefore mitigate delirium risk. It is unclear whether HIV infection remains an independent risk factor for delirium in acute medical admissions in endemic HIV settings with universal ART programmes. [9] Furthermore, in developing country settings such as South Africa (SA), with high This open-access article is distributed under Creative Commons licence CC-BY-NC 4.0.

This open-access article is distributed under Creative Commons licence CC-BY-NC 4.0.

BackgroundTimely identification of people who are at risk of dying is an important first component of end-of-life care. Clinicians often fail to identify such patients, thus trigger tools have been developed to assist in this process. We aimed to evaluate the performance of a identification tool (based on the Gold Standards Framework Prognostic Indicator Guidance) to predict death at 12 months in a population of hospitalised patients in South Africa.MethodsPatients admitted to the acute medical services in two public hospitals in Cape Town, South Africa were enrolled in a prospective observational study. Demographic data were collected from patients and patient notes. Patients were assessed within two days of admission by two trained clinicians who were not the primary care givers, using the identification tool. Outcome mortality data were obtained from patient folders, the hospital electronic patient management system and the Western Cape Provincial death registry which links a unique patient identification number with national death certificate records and system wide electronic records.Results822 patients (median age of 52 years), admitted with a variety of medical conditions were assessed during their admission. 22% of the cohort were HIV-infected. 218 patients were identified using the screening tool as being in the last year of their lives. Mortality in this group was 56% at 12 months, compared with 7% for those not meeting any criteria. The specific indicator component of the tool performed best in predicting death in both HIV-infected and HIV-uninfected patients, with a sensitivity of 74% (68–81%), specificity of 85% (83–88%), a positive predictive value of 56% (49–63%) and a negative predictive value of 93% (91–95%). The hazard ratio of 12-month mortality for those identified vs not was 11.52 (7.87–16.9, p < 0.001).ConclusionsThe identification tool is suitable for use in hospitals in low-middle income country setting that have both a high communicable and non-communicable disease burden amongst young patients, the majority under age 60.

Objectives In high-income countries, up to 25% of inpatients have a self-reported penicillin allergy (PA). After testing, 95% of these self-reported PAs are incorrect. These incorrectly labelled PAs increase the use of broad-spectrum antibiotics, and drive bacterial resistance. The epidemiology of PA in low- and middle-income countries is unknown. We aimed to describe the epidemiology and delabelling outcomes of self-reported PA in South African (SA) inpatients. Methods We conducted point prevalence surveys between April 2019 and June 2021 at seven hospitals in Cape Town, South Africa. A team trained in the PEN-FAST allergy decision tool conducted in-person interviews, and reviewed patient notes to identify and risk stratify inpatients with a self-reported PA. These patients were referred to the Groote Schuur Hospital (GSH) allergy clinic for delabelling. Results A total of 1486 hospital inpatients were surveyed and 3.2% (n = 48) carried a PA label. Importantly, 64.6% (n = 31) were classified by PEN-FAST as low risk for true penicillin hypersensitivity. Overall, 25% of the self-reported PAs received a β-lactam antibiotic in hospital and were directly delabelled. Delabelling attrition was very high, with 6.3% (3/48) of the self-reported PAs attending the GSH allergy clinic, and only one patient proceeding to a negative oral penicillin challenge. Conclusions Inpatient self-reported PA was lower in South Africa hospitals compared with other upper-middle-income countries, and the majority of patients carried a low-risk PA label. Linkage for delabelling with the allergy clinic was very poor, and thus strategies to improve access and delivery of delabelling remains an urgent public health issue.

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Background. Up to a quarter of inpatients in high-income countries (HICs) self-report beta-lactam allergy (BLA), which if incorrect,increases the use of alternative antibiotics, worsening individual health outcomes and driving bacterial resistance. In HICs, up to 95% ofself-reported BLAs are incorrect. The epidemiology of BLA in low- and middle-income African countries is unknown.Objectives. To describe the epidemiology and de-labelling outcomes of self-reported BLA in hospitalised South African (SA) patients.Methods. Point-prevalence surveys were conducted at seven hospitals (adult, paediatric, government and privately funded, district andtertiary level) in Cape Town, SA, between April 2019 and June 2021. Ward prescription records and in-person interviews were conductedto identify and risk-stratify BLA patients using the validated PEN-FAST tool. De-labelling was attempted at the tertiary allergy clinic atGroote Schuur Hospital.Results. A total of 1 486 hospital inpatients were surveyed (1 166 adults and 320 children). Only 48 patients (3.2%) self-reported a BLA,with a higher rate in private than in government-funded hospitals (6.3% v. 2.8%; p=0.014). Using the PEN-FAST tool, only 10.4% (n=5/48)of self-reported BLA patients were classified as high risk for true penicillin hypersensitivity. Antibiotics were prescribed to 70.8% (n=34/48)of self-reported BLA patients, with 64.7% (n=22/34) receiving a beta-lactam. Despite three attempts to contact patients for de-labelling atthe allergy clinic, only 3/36 underwent in vivo testing, with no positive results, and 1 patient proceeded to a negative oral challenge.Conclusion. Unlike HICs, self-reported BLA is low among inpatients in SA. The majority of those who self-reported BLA were low risk fortype 1 hypersensitivity, but outpatient de-labelling efforts were largely unsuccessful.

Background: Timely identification of people who are at risk of dying is an important first component of end-of-life care. Clinicians often fail to identify such patients, thus trigger tools have been developed to assist in this process. We aimed to evaluate the performance of a identification tool (based on the Gold Standards FrameworkPrognostic Indicator Guidance) to predict death at 12 months in a population of hospitalised patients in South AfricaMethods: Patients admitted to the acute medical services in two public hospitals in Cape Town, South Africa were enrolled in a prospectiveobservational study. Demographic data were collected from patients and patient notes. Patients were assessed within two days of admission by two trained clinicians who were not the primary care givers, using the identification tool. Outcome mortality data were obtained from patient folders, the hospital electronic patient management system and the Western Cape Provincial death registry which links a unique patient identification number with national death certificate records and system wide electronic recordsResults: 822 patients (median age of 52 years), admitted with a variety of medical conditions were assessed during their admission. 22% of the cohort were HIV-infected. 218 patients were identified using the screening tool as being in the last year of their lives. Mortality in this group was 56% at 12 months, compared with 7% for those not meeting any criteria. The specific indicator component of the tool performed best in predicting death in both HIV-infected and HIV-uninfected patients, with a sensitivity of 74% (68-81%), specificity of 85% (83-88%), a positive predictive value of 56% (49-63%) and a negative predictive value of 93% (91-95%). The hazard ratio of 12-month mortality for those identified vs not was 11.52 (7.87 – 16.9; p < 0.001).Conclusions:The identification tool is suitable for use in hospitals in low-middle income country setting that have both a high communicable and non-communicable disease burden amongst young patients, the majority under age 60.

Background: Timely identification of people who are at risk of dying is an important first component of end-of-life care. Clinicians often fail to identify such patients, thus trigger tools have been developed to assist in this process. We aimed to evaluate the performance of a identification tool (based on the Gold Standards FrameworkPrognostic Indicator Guidance) to predict death at 12 months in a population of hospitalised patients in South AfricaMethods: Patients admitted to the acute medical services in two public hospitals in Cape Town, South Africa were enrolled in a prospectiveobservational study. Demographic data were collected from patients and patient notes. Patients were assessed within two days of admission by two trained clinicians who were not the primary care givers, using the identification tool. Outcome mortality data were obtained from patient folders, the hospital electronic patient management system and the Western Cape Provincial death registry which links a unique patient identification number with national death certificate records and system wide electronic recordsResults: 822 patients (median age of 52 years), admitted with a variety of medical conditions were assessed during their admission. 22% of the cohort were HIV-infected. 218 patients were identified using the screening tool as being in the last year of their lives. Mortality in this group was 56% at 12 months, compared with 7% for those not meeting any criteria. The specific indicator component of the tool performed best in predicting death in both HIV-infected and HIV-uninfected patients, with a sensitivity of 74% (68-81%), specificity of 85% (83-88%), a positive predictive value of 56% (49-63%) and a negative predictive value of 93% (91-95%). The hazard ratio of 12-month mortality for those identified vs not was 11.52 (7.87 – 16.9; p < 0.001).Conclusions:The identification tool is suitable for use in hospitals in low-middle income country setting that have both a high communicable and non-communicable disease burden amongst young patients, the majority under age 60.