Protocol for Improving Care by FAster risk-STratification through use of high sensitivity point-of-care troponin in patients presenting with possible acute coronary syndrome in the EmeRgency department (ICare-FASTER): a stepped-wedge cluster randomised quality improvement initiative.

INTRODUCTION: Clinical assessment in emergency departments (EDs) for possible acute myocardial infarction (AMI) requires at least one cardiac troponin (cTn) blood test. The turn-around time from blood draw to posting results in the clinical portal for central laboratory analysers is ~1-2 hours. New generation, high-sensitivity, point-of-care cardiac troponin I (POC-cTnI) assays use whole blood on a bedside (or near bedside) analyser that provides a rapid (8 min) result. This may expedite clinical decision-making and reduce length of stay. Our purpose is to determine if utilisation of a POC-cTnI testing reduces ED length of stay. We also aim to establish an optimised implementation process for the amended clinical pathway. METHODS AND ANALYSIS: This quality improvement initiative has a pragmatic multihospital stepped-wedge cross-sectional cluster randomised design. Consecutive patients presenting to the ED with symptoms suggestive of possible AMI and having a cTn test will be included. Clusters (comprising one or two hospitals each) will change from their usual-care pathway to an amended pathway using POC-cTnI-the 'intervention'. The dates of change will be randomised. Changes occur at 1 month intervals, with a minimum 2 month 'run-in' period. The intervention pathway will use a POC-cTnI measurement as an alternate to the laboratory-based cTn measurement. Clinical decision-making steps and logic will otherwise remain unchanged. The POC-cTnI is the Siemens (Erlangen Germany) Atellica VTLi high-sensitivity cTnI assay. The primary outcome is ED length of stay. The safety outcome is cardiac death or AMI within 30 days for patients discharged directly from the ED. ETHICS AND DISSEMINATION: Ethics approval has been granted by the New Zealand Southern Health and Disability Ethics Committee, reference 21/STH/9. Results will be published in a peer-reviewed journal. Lay and academic presentations will be made. Maori-specific results will be disseminated to Maori stakeholders. TRIAL REGISTRATION NUMBER: ACTRN12619001189112.

Determination of Clinically Acceptable Analytical Variation of Cardiac Troponin at Decision Thresholds.

BACKGROUND: Clinical decision-making for risk stratification for possible myocardial infarction (MI) uses high-sensitivity cardiac troponin (hs-cTn) thresholds that range from the limit of detection to several-fold higher than the upper reference limit (URL). To establish a minimum analytical variation standard, we can quantify the effect of variation on the population clinical measures of safety (sensitivity) and effectiveness [proportion below threshold, or positive predictive value (PPV)]. METHODS: From large datasets of patients investigated for possible MI with the Abbott hs-cTnI and Roche hs-cTnT assays, we synthesized datasets of 1 000 000 simulated patients. Troponin concentrations were randomly varied several times based on absolute deviations of 0.5 to 3 ng/L and relative changes of 2% to 20% around the low-risk threshold (5 ng/L) and URLs, respectively. RESULTS: For both assays at the low-risk thresholds, there were negligible differences in sensitivity (<0.3%) with increasing analytical variation. The proportion of patients characterized as low risk reduced by 30% to 29% (Roche) and 53% to 44% (Abbott). At the URL, increasing analytical variation also did not change sensitivity; the PPV fell by less than 3%. For risk stratification, increased delta thresholds (change between serial troponin concentrations) increased sensitivity at the cost of a decreased percentage of patients below the delta threshold, with the largest changes at the greatest analytical variation. CONCLUSIONS: At the low-risk threshold, analytical variation up to 3 ng/L minimally impacted the safety metric (sensitivity) but marginally reduced effectiveness. Similarly, at the URL even relative variation up to 25% minimally impacted safety metrics and effectiveness. Analytical variation for delta thresholds did not negatively impact sensitivity but decreased effectiveness.

Prognostic performance of soluble urokinase plasminogen activator receptor for heart failure or mortality in Western and Asian patients with acute breathlessness.

AIMS: The performance of circulating soluble urokinase plasminogen activator receptor (suPAR) for predicting the composite endpoint of subsequent heart failure (HF) hospitalisation and/or death at 1 year was assessed in (i) patients with undifferentiated breathlessness, and generalisability was compared in (ii) disparate Western versus Asian sub-cohorts, and in (iii) the sub-cohort adjudicated with HF. METHODS AND RESULTS: Patients with acute breathlessness were recruited from the emergency departments in New Zealand (NZ, n = 612) and Singapore (n = 483). suPAR measured in the presentation samples was higher in patients incurring the endpoint (n = 281) compared with survivors (5.2 ng/mL vs 3.1 ng/mL, P < 0.0001). The discriminative power of suPAR for endpoint prediction was c-statistic of 0.77 in the combined population, but was superior in Singapore than NZ (c-statistic: 0.83 vs 0.71, P < 0.0001). Although the highest suPAR tertile (>4.37 ng/mL) was associated with risks of >4-fold in NZ, >20-fold in Singapore, and ≥3-fold in HF for incurring the outcome, there was no interaction between country and suPAR levels after adjustment. Multivariable analysis indicated suPAR to be robust in predicting HF/death at 1-year [hazard ratio: 1.9 (95% CI:1.7 to 2.0) per SD increase] and improved risk discrimination for outcome prediction in HF (∆0.06) and for those with NT-proBNP >1000 pg/mL (∆0.02). CONCLUSION: suPAR is a strong independent predictor of HF and/or death at 1 year in acutely breathless patients, in both Asian and Western cohorts, and in HF. suPAR may improve stratification of acutely breathless patients, and in acute HF, for risk of later onset of heart failure or mortality.

Convalescent Growth Differentiation Factor-15 and Long-Term Outcomes after an Acute Coronary Syndrome.

BACKGROUND: Growth differentiation factor-15 (GDF-15) has been shown to be associated with adverse clinical outcomes in patients after an acute coronary syndrome when measured soon after an event. Although dynamic in the acute phase after myocardial injury, GDF-15 has been shown to remain stable during convalescence. In this study, we aimed to assess the value of GDF-15 as a long-term prognostic marker for clinical outcomes when measured in the convalescent phase following an acute coronary syndrome. METHODS: GDF-15 concentrations were measured in 1945 patients who were recruited between 2002 and 2009 to the Coronary Disease Cohort Study. For this analysis, follow-up was curtailed at 10 years and association of GDF-15 with all-cause death, cardiovascular death, recurrent myocardial infarction, and heart failure hospitalizations were assessed with multivariate Cox proportional hazard regression analysis. RESULTS: After 10 years of follow-up, there were 648 deaths (348 from cardiovascular causes), 500 admissions for myocardial infarction, and 436 for heart failure. Four-month convalescent GDF-15 demonstrated a robust independent association with all endpoints, which remained after adjustment for Global Registry of Acute Coronary Events score and other convalescent biomarkers. When compared to the lowest quartile of GDF-15 concentrations, those in the highest quartile had a 3-fold increased risk of all-cause death. CONCLUSIONS: Convalescent plasma GDF-15 is a strong and independent predictor of 10-year all-cause death, cardiovascular death, recurrent myocardial infarction, and heart failure admission following an acute coronary syndrome. AUSTRALIAN NEW ZEALAND CLINICAL TRIALS REGISTRY TRIAL ID: ACTRN12605000431628.

Failure rates of nonoperative management of low-grade splenic injuries with active extravasation: an Eastern Association for the Surgery of Trauma multicenter study.

Objectives: There is little evidence guiding the management of grade I-II traumatic splenic injuries with contrast blush (CB). We aimed to analyze the failure rate of nonoperative management (NOM) of grade I-II splenic injuries with CB in hemodynamically stable patients. Methods: A multicenter, retrospective cohort study examining all grade I-II splenic injuries with CB was performed at 21 institutions from January 1, 2014, to October 31, 2019. Patients >18 years old with grade I or II splenic injury due to blunt trauma with CB on CT were included. The primary outcome was the failure of NOM requiring angioembolization/operation. We determined the failure rate of NOM for grade I versus grade II splenic injuries. We then performed bivariate comparisons of patients who failed NOM with those who did not. Results: A total of 145 patients were included. Median Injury Severity Score was 17. The combined rate of failure for grade I-II injuries was 20.0%. There was no statistical difference in failure of NOM between grade I and II injuries with CB (18.2% vs 21.1%, p>0.05). Patients who failed NOM had an increased median hospital length of stay (p=0.024) and increased need for blood transfusion (p=0.004) and massive transfusion (p=0.030). Five patients (3.4%) died and 96 (66.2%) were discharged home, with no differences between those who failed and those who did not fail NOM (both p>0.05). Conclusion: NOM of grade I-II splenic injuries with CB fails in 20% of patients. Level of evidence: IV.

Derivation and Validation of Thresholds Using Synthetic Data Methods for Single-Test Screening of Emergency Department Patients with Possible Acute Myocardial Infarction Using a Point-of-Care Troponin Assay.

BACKGROUND: Single-sample (screening) rule-out of acute myocardial infarction (AMI) with troponin requires derivation of a single-test screening threshold. In data sets with small event numbers, the lowest one or two concentrations of myocardial infarction (MI) patients dictate the threshold. This is not optimal. We aimed to demonstrate a process incorporating both real and synthetic data for deriving such thresholds using a novel pre-production high-precision point-of-care assay. METHODS: cTnI concentrations were measured from thawed plasma using the Troponin I Next (TnI-Nx) assay (i-STAT; Abbott) in adults on arrival to the emergency department with symptoms suggestive of AMI. The primary outcome was an AMI or cardiac death within 30 days. We used internal-external validation with synthetic data production based on clinical and demographic data, plus the measured TnI-Nx concentration, to derive and validate decision thresholds for TnI-Nx. The target low-risk threshold was a sensitivity of 99% and a high-risk threshold specificity of >95%. RESULTS: In total, 1356 patients were included, of whom 191 (14.1%) had the primary outcome. A total of 500 synthetic data sets were constructed. The mean low-risk threshold was determined to be 5 ng/L. This categorized 38% (95% CI, 6%-68%) to low-risk with a sensitivity of 99.0% (95% CI, 98.6%-99.5%) and a negative predictive value of 99.4% (95% CI, 97.6%-99.8%). A similarly derived high-risk threshold of 25 ng/L had a specificity of 95.0% (95% CI, 94.8%-95.1%) and a positive predictive value of 74.8% (95% CI, 71.5%-78.0%). CONCLUSIONS: With the TnI-Nx assay, we successfully demonstrated an approach using synthetic data generation to derive low-risk thresholds for safe and effective screening.

Comparison of three creatinine-based equations to predict adverse outcome in a cardiovascular high-risk cohort: an investigation using the SPRINT research materials.

Background: Novel creatinine-based equations have recently been proposed but their predictive performance for cardiovascular outcomes in participants at high cardiovascular risk in comparison to the established CKD-EPI 2009 equation is unknown. Method: In 9361 participants from the United States included in the randomized controlled SPRINT trial, we calculated baseline estimated glomerular filtration rate (eGFR) using the CKD-EPI 2009, CKD-EPI 2021, and EKFC equations and compared their predictive value of cardiovascular events. The statistical metric used is the net reclassification improvement (NRI) presented separately for those with and those without events. Results: During a mean follow-up of 3.1 ± 0.9 years, the primary endpoint occurred in 559 participants (6.0%). When using the CKD-EPI 2009, the CKD-EPI 2021, and the EKFC equations, the prevalence of CKD (eGFR <60 ml/min/1.73 m2 or >60 ml/min/1.73 m2 with an ACR ≥30 mg/g) was 37% vs. 35.3% (P = 0.02) vs. 46.4% (P < 0.001), respectively. The corresponding mean eGFR was 72.5 ± 20.1 ml/min/1.73 m2 vs. 73.2 ± 19.4 ml/min/1.73 m2 (P < 0.001) vs. 64.6 ± 17.4 ml/min/1.73 m2 (P < 0.001). Neither reclassification according to the CKD-EPI 2021 equation [CKD-EPI 2021 vs. CKD-EPI 2009: NRIevents: -9.5% (95% confidence interval (CI) -13.0% to -5.9%); NRInonevents: 4.8% (95% CI 3.9% to 5.7%)], nor reclassification according to the EKFC equation allowed better prediction of cardiovascular events compared to the CKD-EPI 2009 equation (EKFC vs. CKD-EPI 2009: NRIevents: 31.2% (95% CI 27.5% to 35.0%); NRInonevents: -31.1% (95% CI -32.1% to -30.1%)). Conclusion: Substituting the CKD-EPI 2009 with the CKD-EPI 2021 or the EKFC equation for calculation of eGFR in participants with high cardiovascular risk without diabetes changed the prevalence of CKD but was not associated with improved risk prediction of cardiovascular events for both those with and without the event.

The Drug Burden Index and Level of Frailty as Determinants of Healthcare Costs in a Cohort of Older Frail Adults in New Zealand.

OBJECTIVES: Frailty is common in older people and is associated with increased use of healthcare services and ongoing use of multiple medications. This study provides insights into the healthcare cost structure of a frail group of older adults in Aotearoa, New Zealand. Furthermore, we investigated the relationship between participants' anticholinergic and sedative medication burden and their total healthcare costs to explore the viability of deprescribing interventions within this cohort. METHODS: Healthcare cost analysis was conducted using data collected during a randomized controlled trial within a frail, older cohort. The collected information included participant demographics, medications used, frailty, cost of service use of aged residential care and outpatient hospital services, hospital admissions, and dispensed medications. RESULTS: Data from 338 study participants recruited between 25 September 2018 and 30 October 2020 with a mean age of 80 years were analyzed. The total cost of healthcare per participant ranged from New Zealand $15 (US dollar $10) to New Zealand $270 681 (US dollar $175 943) over 6 months postrecruitment into the study. Four individuals accounted for 26% of this cohort's total healthcare cost. We found frailty to be associated with increased healthcare costs, whereas the drug burden was only associated with increased pharmaceutical costs, not overall healthcare costs. CONCLUSIONS: With no relationship found between a patient's anticholinergic and sedative medication burden and their total healthcare costs, more research is required to understand how and where to unlock healthcare cost savings within frail, older populations.

A globally synthesised and flagged bee occurrence dataset and cleaning workflow.

Species occurrence data are foundational for research, conservation, and science communication, but the limited availability and accessibility of reliable data represents a major obstacle, particularly for insects, which face mounting pressures. We present BeeBDC, a new R package, and a global bee occurrence dataset to address this issue. We combined >18.3 million bee occurrence records from multiple public repositories (GBIF, SCAN, iDigBio, USGS, ALA) and smaller datasets, then standardised, flagged, deduplicated, and cleaned the data using the reproducible BeeBDC R-workflow. Specifically, we harmonised species names (following established global taxonomy), country names, and collection dates and, we added record-level flags for a series of potential quality issues. These data are provided in two formats, "cleaned" and "flagged-but-uncleaned". The BeeBDC package with online documentation provides end users the ability to modify filtering parameters to address their research questions. By publishing reproducible R workflows and globally cleaned datasets, we can increase the accessibility and reliability of downstream analyses. This workflow can be implemented for other taxa to support research and conservation.

Social isolation and loneliness among older adults living in rural areas during the COVID-19 pandemic: a scoping review.

BACKGROUND: The causes and consequences of social isolation and loneliness of older people living in rural contexts during the COVID-19 pandemic were systematically reviewed to describe patterns, causes and consequences. METHODS: Using the Arksey and O'Malley (2005) scoping review method, searches were conducted between March and December 2022, 1013 articles were screened and 29 were identified for data extraction. RESULTS: Findings were summarized using thematic analysis separated into four major themes: prevalence of social isolation and loneliness; rural-only research; comparative urban-rural research; and technological and other interventions. Core factors for each of these themes describe the experiences of older people during the COVID-19 pandemic and related lockdowns. We observed that there are interrelationships and some contradictory findings among the themes. CONCLUSIONS: Social isolation and loneliness are associated with a wide variety of health problems and challenges, highlighting the need for further research. This scoping review systematically identified several important insights into existing knowledge from the experiences of older people living in rural areas during the COVID-19 pandemic, while pointing to pressing knowledge and policy gaps that can be addressed in future research.

Aprendizaje automático para la toma de decisiones en cardiología: una revisión narrativa para ayudar a recorrer el nuevo panorama / Machine learning for decision-making in cardiology: a narrative review to aid navigating the new landscape

El aprendizaje automático (machine learning) en cardiología es cada vez más frecuente en la literatura médica, pero los modelos de aprendizaje automático aún no han producido un cambio generalizado de la práctica clínica. En parte esto se debe a que el lenguaje utilizado para describir el aprendizaje automático procede de la informática y resulta menos familiar a los lectores de revistas clínicas. En esta revisión narrativa se proporcionan, en primer lugar, algunas orientaciones sobre cómo leer las revistas de aprendizaje automático y, a continuación, orientaciones adicionales para quienes se plantean iniciar un estudio utilizando el aprendizaje automático. Por último, se ilustra el estado actual de la técnica con breves resúmenes de 5 artículos que van desde un modelo de aprendizaje automático muy sencillo hasta otros muy sofisticados.(AU)

Machine learning in cardiology is becoming more commonplace in the medical literature; however, machine learning models have yet to result in a widespread change in practice. This is partly due to the language used to describe machine, which is derived from computer science and may be unfamiliar to readers of clinical journals. In this narrative review, we provide some guidance on how to read machine learning journals and additional guidance for investigators considering instigating a study using machine learning. Finally, we illustrate the current state of the art with brief summaries of 5 articles describing models that range from the very simple to the highly sophisticated.(AU)

Post Hoc Analyses of a Randomized Controlled Trial for the Effect of Pharmacist Deprescribing Intervention on the Anticholinergic Burden in Frail Community-Dwelling Older Adults.

OBJECTIVES: Anticholinergic burden is detrimental to cognitive health. Multiple studies found that a high anticholinergic burden is associated with an increased risk for dementia, changes to the brain structure, function, and cognitive decline. We performed a post hoc analysis of a randomized controlled deprescribing trial. We compared the effect of the intervention on baseline anticholinergic burden across the treatment and control groups and the time of recruitment before and after a lockdown due to the COVID pandemic with subgroup analyses by baseline frailty index. DESIGN: Randomized controlled trial. SETTINGS AND PARTICIPANTS: We analyzed data from a de-prescribing trial of older adults (>65 years) previously conducted in New Zealand that was focused on reducing the Drug Burden Index (DBI). METHODS: We used the anticholinergic cognitive burden (ACB) to quantify the impact of the intervention on reducing the anticholinergic burden. Participants not taking anticholinergics at the start of the trial were excluded. The primary outcome for this subgroup analysis was a change in ACB, measured with the gHedges statistic describing the difference in standard deviation units of this change between intervention and control. For this analysis, the trial participants were stratified into low, medium, and high frailty and timing into prior- and post-lockdown (public health measures for COVID-19). RESULTS: Among the 295 participants in this analysis, the median (IQR) age was 79 (74, 85), and 67% were women. For the primary outcome gHedges = -0.04 (95% CI -0.26 to 0.19) with a -0.23 mean reduction in ACB in the intervention arm and -0.19 in the control arm. Before lockdown gHedges = -0.38 (95% CI -0.84 to 0.04) and post-lockdown gHedges = 0.07 (95% CI -0.19 to 0.33). The mean change in ACB for each of the frailty strata was as follows: low frailty (-0.02; 95% CI -0.65 to 0.18); medium frailty (0.05; 95% CI -0.28 to 0.38); high frailty (0.08; 95% CI -0.40 to 0.56). CONCLUSIONS AND IMPLICATIONS: The study did not provide evidence for the effect of pharmacist deprescribing intervention on reducing the anticholinergic burden. However, this post hoc analysis examined the impact of COVID on the effectiveness of the intervention, and further research in this area may be warranted.

The role of implementation organizations in scaling evidence-based psychosocial interventions.

BACKGROUND: To bring evidence-based interventions (EBIs) to individuals with behavioral health needs, psychosocial interventions must be delivered at scale. Despite an increasing effort to implement effective treatments in communities, most individuals with mental health and behavioral problems do not receive EBIs. We posit that organizations that commercialize EBIs play an important role in disseminating EBIs, particularly in the USA. The behavioral health and implementation industry is growing, bringing the implementation field to an important inflection point: how to scale interventions to improve access while maintaining EBI effectiveness and minimizing inequities in access to psychosocial intervention. MAIN BODY: We offer a first-hand examination of five illustrative organizations specializing in EBI implementation: Beck Institute for Cognitive Behavioral Therapy; Incredible Years, Inc.; the PAXIS Institute; PracticeWise, LLC; and Triple P International. We use the Five Stages of Small Business Growth framework to organize themes. We discuss practical structures (e.g., corporate structures, intellectual property agreements, and business models) and considerations that arise when trying to scale EBIs including balancing fidelity and reach of the intervention. Business models consider who will pay for EBI implementation and allow organizations to scale EBIs. CONCLUSION: We propose research questions to guide scaling: understanding the level of fidelity needed to maintain efficacy, optimizing training outcomes, and researching business models to enable organizations to scale EBIs.

An integrated signature of extracellular matrix proteins and a diastolic function imaging parameter predicts post-MI long-term outcomes.

Background: Patients suffering from acute myocardial infarction (AMI) are at risk of secondary outcomes including major adverse cardiovascular events (MACE) and heart failure (HF). Comprehensive molecular phenotyping and cardiac imaging during the post-discharge time window may provide cues for risk stratification for the outcomes. Materials and methods: In a prospective AMI cohort in New Zealand (N = 464), we measured plasma proteins and lipids 30 days after hospital discharge and inferred a unified partial correlation network with echocardiographic variables and established clinical biomarkers (creatinine, c-reactive protein, cardiac troponin I and natriuretic peptides). Using a network-based data integration approach (iOmicsPASS+), we identified predictive signatures of long-term secondary outcomes based on plasma protein, lipid, imaging markers and clinical biomarkers and assessed the prognostic potential in an independent cohort from Singapore (N = 190). Results: The post-discharge levels of plasma proteins and lipids showed strong correlations within each molecular type, reflecting concerted homeostatic regulation after primary MI events. However, the two molecular types were largely independent with distinct correlation structures with established prognostic imaging parameters and clinical biomarkers. To deal with massively correlated predictive features, we used iOmicsPASS + to identify subnetwork signatures of 211 and 189 data features (nodes) predictive of MACE and HF events, respectively (160 overlapping). The predictive features were primarily imaging parameters, including left ventricular and atrial parameters, tissue Doppler parameters, and proteins involved in extracellular matrix (ECM) organization, cell differentiation, chemotaxis, and inflammation. The network signatures contained plasma protein pairs with area-under-the-curve (AUC) values up to 0.74 for HF prediction in the validation cohort, but the pair of NT-proBNP and fibulin-3 (EFEMP1) was the best predictor (AUC = 0.80). This suggests that there were a handful of plasma proteins with mechanistic and functional roles in predisposing patients to the secondary outcomes, although they may be weaker prognostic markers than natriuretic peptides individually. Among those, the diastolic function parameter (E/e' - an indicator of left ventricular filling pressure) and two ECM proteins, EFEMP1 and follistatin-like 3 (FSTL3) showed comparable performance to NT-proBNP and outperformed left ventricular measures as benchmark prognostic factors for post-MI HF. Conclusion: Post-discharge levels of E/e', EFEMP1 and FSTL3 are promising complementary markers of secondary adverse outcomes in AMI patients.

Personalized diagnosis in suspected myocardial infarction.

BACKGROUND: In suspected myocardial infarction (MI), guidelines recommend using high-sensitivity cardiac troponin (hs-cTn)-based approaches. These require fixed assay-specific thresholds and timepoints, without directly integrating clinical information. Using machine-learning techniques including hs-cTn and clinical routine variables, we aimed to build a digital tool to directly estimate the individual probability of MI, allowing for numerous hs-cTn assays. METHODS: In 2,575 patients presenting to the emergency department with suspected MI, two ensembles of machine-learning models using single or serial concentrations of six different hs-cTn assays were derived to estimate the individual MI probability (ARTEMIS model). Discriminative performance of the models was assessed using area under the receiver operating characteristic curve (AUC) and logLoss. Model performance was validated in an external cohort with 1688 patients and tested for global generalizability in 13 international cohorts with 23,411 patients. RESULTS: Eleven routinely available variables including age, sex, cardiovascular risk factors, electrocardiography, and hs-cTn were included in the ARTEMIS models. In the validation and generalization cohorts, excellent discriminative performance was confirmed, superior to hs-cTn only. For the serial hs-cTn measurement model, AUC ranged from 0.92 to 0.98. Good calibration was observed. Using a single hs-cTn measurement, the ARTEMIS model allowed direct rule-out of MI with very high and similar safety but up to tripled efficiency compared to the guideline-recommended strategy. CONCLUSION: We developed and validated diagnostic models to accurately estimate the individual probability of MI, which allow for variable hs-cTn use and flexible timing of resampling. Their digital application may provide rapid, safe and efficient personalized patient care. TRIAL REGISTRATION NUMBERS: Data of following cohorts were used for this project: BACC ( www. CLINICALTRIALS: gov ; NCT02355457), stenoCardia ( www. CLINICALTRIALS: gov ; NCT03227159), ADAPT-BSN ( www.australianclinicaltrials.gov.au ; ACTRN12611001069943), IMPACT ( www.australianclinicaltrials.gov.au , ACTRN12611000206921), ADAPT-RCT ( www.anzctr.org.au ; ANZCTR12610000766011), EDACS-RCT ( www.anzctr.org.au ; ANZCTR12613000745741); DROP-ACS ( https://www.umin.ac.jp , UMIN000030668); High-STEACS ( www. CLINICALTRIALS: gov ; NCT01852123), LUND ( www. CLINICALTRIALS: gov ; NCT05484544), RAPID-CPU ( www. CLINICALTRIALS: gov ; NCT03111862), ROMI ( www. CLINICALTRIALS: gov ; NCT01994577), SAMIE ( https://anzctr.org.au ; ACTRN12621000053820), SEIGE and SAFETY ( www. CLINICALTRIALS: gov ; NCT04772157), STOP-CP ( www. CLINICALTRIALS: gov ; NCT02984436), UTROPIA ( www. CLINICALTRIALS: gov ; NCT02060760).

COVID-19 experiences of social isolation and loneliness among older adults in Africa: a scoping review.

Objective: Social isolation and loneliness (SI/L) are considered critical public health issues. The primary objective of this scoping review is to document the experience of SI/L among older adults in Africa during the COVID-19 pandemic, given research gaps in this area. We identified the reasons for SI/L, the effects of SI/L, SI/L coping strategies, and research and policy gaps in SI/L experiences among older adults in Africa during COVID-19. Methods: Six databases (PubMed, Scopus, CINAHL, APA PsycINFO, Web of Science, and Ageline) were used to identify studies reporting the experiences of SI/L among older adults in Africa during the COVID-19 lockdown. We adopted the Joanna Briggs Institute (JBI) methodology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Results: Social isolation and loneliness due to COVID-19 in Africa affected older adults' mental, communal, spiritual, financial, and physical health. The use of technology was vital, as was the role of social networks within the family, community, religious groups, and government. Methodological challenges include the risk of selective survival bias, sampling biases, and limited inductive value due to context. Also, lack of large-scale mixed methods longitudinal studies to capture the experiences of older adults during COVID-19. There were essential policy gaps for African mental health support services, media programs, and community care service integration targeting older adults in the era of the COVID-19 lockdown. Discussion: Like in other countries, COVID-19 lockdown policies and the lockdown restrictions primarily caused the experience of SI/L among older adults in Africa. In African countries, they resulted in a severance of older adults from the cultural structure of care for older adults and their familial support systems. Weak government intervention, personal situations, challenges regarding technology, and detachment from daily activities, disproportionately affected older adults in Africa.