Background: Antimicrobial resistance surveillance is essential for empiric antibiotic prescribing, infection prevention and control policies and to drive novel antibiotic discovery. However, most existing surveillance systems are isolate-based without supporting patient-based clinical data, and not widely implemented especially in low- and middle-income countries (LMICs).Methods: A Clinically-Oriented Antimicrobial Resistance Surveillance Network (ACORN) II is a large-scale multicentre protocol which builds on the WHO Global Antimicrobial Resistance and Use Surveillance System to estimate syndromic and pathogen outcomes along with associated health economic costs. ACORN-healthcare associated infection (ACORN-HAI) is an extension study which focuses on healthcare-associated bloodstream infections and ventilator-associated pneumonia. Our main aim is to implement an efficient clinically-oriented antimicrobial resistance surveillance system, which can be incorporated as part of routine workflow in hospitals in LMICs. These surveillance systems include hospitalised patients of any age with clinically compatible acute community-acquired or healthcare-associated bacterial infection syndromes, and who were prescribed parenteral antibiotics. Diagnostic stewardship activities will be implemented to optimise microbiology culture specimen collection practices. Basic patient characteristics, clinician diagnosis, empiric treatment, infection severity and risk factors for HAI are recorded on enrolment and during 28-day follow-up. An R Shiny application can be used offline and online for merging clinical and microbiology data, and generating collated reports to inform local antibiotic stewardship and infection control policies.Discussion: ACORN II is a comprehensive antimicrobial resistance surveillance activity which advocates pragmatic implementation and prioritises improving local diagnostic and antibiotic prescribing practices through patient-centred data collection. These data can be rapidly communicated to local physicians and infection prevention and control teams. Relative ease of data collection promotes sustainability and maximises participation and scalability. With ACORN-HAI as an example, ACORN II has the capacity to accommodate extensions to investigate further specific questions of interest. Surveillance for the emergence and spread of antimicrobial resistance is a cornerstone of our response strategies. It is essential both at patient-level for informing empiric antibiotic prescribing and treatment guidelines, and at system-level for designing and evaluating treatment, infection prevention and control policies. Antimicrobial resistance surveillance involves systematic data collection, analysis and communication. The aim is to describe disease burden and epidemiology in a timely and unbiased manner that is relevant to the community and various stakeholders, including patients, physicians, researchers and policy makers.Antimicrobial resistance is declared as 1 of the top 10 global public health threats facing humanity. This is mainly because assembling comprehensive surveillance data which incorporate both clinical and microbiological records is challenging. Infection episodes are defined using clinical criteria which involves sourcing individual-level data from medical notes, microbiological reports and antibiotic prescription charts. Existing population-level surveillance databases, such as the European Centre for Disease Prevention and Control Surveillance Atlas, The Center For Disease Dynamics, Economics & Policy Resistance Map, and the World Health Organisation (WHO) Global Antimicrobial Resistance and Use Surveillance System (GLASS), are isolate-based and rely mainly on microbiology data alone.Despite a global emphasis on antimicrobial resistance in recent decades, effective surveillance networks and systems remain sparsedue to orwith a resistant bacteria. In addition, quantifying morbidity and mortality impacts of drug-resistant infections requires longitudinal follow-up and cannot be reliably determined from death certificates. Such high quality surveillance data is especially lacking in LMICs where antimicrobial resistance burden is the highest.These isolate-based data present geospatial and temporal trends of resistant organisms but are prone to biases introduced by local characteristics such as differential access to antibiotics in the community versus hospital settings, antibiotic prescription practices, availability of laboratory resources, and practices of microbiological culture collection and reporting. Particularly in low- and middle-income countries (LMICs), microbiology cultures may only be performed after multiple courses of empirical antibiotics have failed or in situations where diagnostics are affordable. Importantly, interpreting microbiology cultures without considering clinical syndromes makes it impossible to differentiate innocent ‘bystander’ colonising versus true pathogens, i.e. if a patient became ill or died. It aims to implement an efficient clinically-oriented antimicrobial resistance surveillance system, incorporated as part of routine workflow in hospitals in LMICs. In addition, ACORN II is designed to accommodate extension studies with specific focuses on infection syndrome, pathogens or patient population.A Clinically-Oriented Antimicrobial Resistance Surveillance Network (ACORN) is a large-scale multicentre study which captures essential data on patient clinical features, management, and outcomes. ACORN adds value to isolate-based antimicrobial resistance surveillance ), using patient rather than pathogen denominators, syndromic and pathogen outcomes, along with associated health economic costs. Specific target pathogens includedStreptococcus pneumoniae, Staphylococcus aureus, Salmonella spp., Klebsiella pneumoniae, Escherichia coli, andAcinetobacter baumannii. The current ACORN II study protocol refines the methodology ACORN I, and expands the scope to include all infection syndromes and bacterial pathogens. To illustrate the ability of ACORN design to accommodate extension studies, we describe the ACORN-healthcare associated infection (ACORN-HAI) extension study which focuses on healthcare-associated bloodstream infections and ventilator-associated pneumonia. Study procedures including site set up, diagnostic stewardship, data collection and rationale behind their design features are detailed below. An initial pilot phase (ACORN I) was completed in 2020, focusing on patients with clinically suspected meningitis, pneumonia, or sepsisAn overarching approval for ACORN II and ACORN-HAI was given by the Oxford Tropical Research Ethics Committee . Local ethics approvals are sought prior to enrolment. Written informed consent is required for participation in all study sites except the following, where anonymised data may be collected without individual consent: Angkor Hospital for Children in Siem Reap (Cambodia), Sarawak General Hospital , Queen Elizabeth II Hospital , Sabah Women and Children's Hospital , University Malaya Medical Centra , National Hospital of Tropical Diseases, Hanoi (Vietnam).The aim of ACORN II is to provide actionable data to local institutions, national and international surveillance systems, researchers and policy makers via an efficient clinically orientated antimicrobial resistance surveillance system (extended data 2). It is designed to be implemented alongside routine clinical care in hospitals, especially in LMIC settings. The data collected are fully compatible with WHO GLASS and are further expanded to enable classification of infection syndromes and outcomes. These data will be used to estimate syndromic and pathogen outcomes along with associated costs.Escherichia coli and methicillin resistantStaphylococcus aureus bloodstream infection using the WHO attributable mortality protocol); iv) determine the major indications for prescribing parenteral antibiotics by patient group, timing of prescription, and location; and, v) to determine the major empiric antibiotics used by clinical syndrome, place of acquisition, patient group, and location .The specific objectives are to i) implement and assess a hospital-based system for patient-centred surveillance of drug resistant infections; ii) characterise drug-resistant infections by clinical syndrome, place of acquisition, patient group, and location; iii) quantify burden of drug-resistant infections in terms of attributable mortality and excess length of hospital stay ; ii) set up infrastructure such as computers, data collection software and laboratory capacity; iii) form communication channels between the research investigators, clinicians and laboratory teams for collaborations and data sharing; and iv) familiarise themselves with local ethics and regulatory bodies.Participating sites will i) undergo basic research training such as “Good Clinical Practice (GCP)”  surveys:Laboratory assessmenthttps://tinyurl.com/ACORNlabsurvey), the Site Laboratory Assessment tool (extended data 3), document review and virtual audits. The assessment tools allow the assessors to gather information on the current capacity of site laboratories including what routine specimen processing the laboratory already carries out, the current level of quality management and quality control in the laboratory, safety procedures and specimen reporting. Following completion of the laboratory assessment continual laboratory quality monitoring is performed using the Site Laboratory Quality Monitoring tool (extended data 3).The purpose of laboratory assessment is to make a baseline evaluation of the laboratories that will be used for processing samples and submitting data for antimicrobial resistance (AMR) surveillance. Laboratory assessments will be performed using the Laboratory Pre-Assessment online survey . The endpoint of diagnostic stewardship is to ensure that the right patients have the right tests at the right time and that results are used to ensure that they receive the right treatment. Systematic testing of patients with suspected infection will result in data that can be used to inform local treatment guidelines as well as be used for AMR surveillance activities. It is important that ACORN II stewardship is aligned with existing specimen collection, processing, and feedback procedures. A diagnostic stewardship checklist is available to determine the extent of existing diagnostic stewardship activities / materials at the site. Negative answers to any of the questions should prompt development of that particular item / activity in the form of recommendations for standardised investigations for suspected infection, at least covering when to collect a blood culture and / or sterile site fluid culture. Examples of appropriate guidelines are included in the ACORN diagnostic stewardship guideline and can be adapted to the local situation. Monitoring of culture rates, by patient group, clinical location, clinical syndrome, via the ACORN project app described will permit assessment of diagnostic stewardship activity success.Clinician Knowledge, Attitudes and Practices surveyhttps://tinyurl.com/ACORNclinicianKAP). The survey gathers information on current knowledge, attitudes and practices around diagnostic microbiology and AMR surveillance. The data will be used primarily to inform and iterate site-specific surveillance training and implementation.The purpose of the clinician knowledge, attitudes and practices survey is to make a baseline assessment of the clinical staff working on ACORN II surveillance wards , in whom the decision to start intravenous antibiotic treatment has been made, and are willing to participate in the surveillance. This includesPatients transferred directly from another facility with an acute infection;Patients admitted to a non-surveillance ward initially but transferred to a surveillance ward within 48 hours of admission;Patients investigated and treated for suspected CI in the Emergency Department (Emergency Room / Accident and Emergency Department) with delayed transfer to the surveillance ward for any reasons, e.g. bed shortages, COVID-19 screening procedures, or other local operational challenges.For HI cases, all admitted patients in the surveillance wards will be reviewed on a single day per week via a point prevalence survey (PPS). The HI inclusion criteria include:Clinical suspicion of bacterial infection and prescription / commencement of a new intravenous antibiotic ; andOnset of infection syndrome on or after day 3 of admission (day 1 refers to the day of admission); andInfection syndrome was not active during the previous weekly review, i.e. onset at least one day following the most recent previous healthcare-associated infection point prevalence surveyAn overarching approval for ACORN II to enrol without the need for individual consent was given by the Oxford Tropical Research Ethics Committee . The requirement for informed consent will be catered to local ethics committees’ advice. In participating sites where the need for explicit informed consent can be waived for minimal/negligible risk studies, potential participants in participating wards will be given a patient information sheet with details about the study. At least one ACORN II information poster will be on display in each surveillance ward. Potential study participants will be asked to confirm verbally that they  have read the surveillance information material and agree to participate. Any patient who requests not to be included in the study will be recorded accordingly in the screening logbook. In sites where informed consent cannot be waived, a typical consent taking process will be in place.Further infection episodes may be identified during the weekly PPS. The final follow up is 28 days after the final infection episode. This can be done by telephone if the participant has been discharged. If the patient is readmitted before the 28 day follow up, the 28 day follow up should be completed as scheduled if no further infection episodes are diagnosed during the readmission.On enrolment, baseline clinical data will be extracted from the patient clinical records/electronic hospital information systems :DemographicsDate of admission and ward locationPrimary reason for admissionCo-morbidity status (modified Charlson comorbidity index)Healthcare exposure in the three months before current admissionThe following data will be collected about the infection episode on enrolment and when a new infection episode is detected in the weekly ward PPS:Ward detailsInfection syndromeClinical severity signs at symptom onsetqSOFA score for adults, ≥18 yearsSepsis six recognition features for children, <18 yearsGeneral WHO severity signs for neonates, <28 daysPresence of medical devices / surgical proceduresMicrobiology and antibiotic susceptibilityEmpiric antibiotic treatment prescribedE. coli orS. aureus BSI, additional clinical data are captured to fulfil the requirements of the WHO GLASS attributable mortality protocol. The Pitt BSI score is calculated and details of immunosuppression are collected, along with complete details of antibiotic treatment for the infection and likely source of infection.For patients with laboratory provenClinical data entry can be done either directly using the Open Data Kit (ODK) Collect app on an Android tablet or via paper case report forms (CRFs) with subsequent entry into the surveillance REDCap database using a laptop and web browser. All clinical data entered will be stored centrally in the ACORN REDCap database, where error checking and correction is performed regularly. All data stored in the central database are anonymised and securely stored.https://github.com/acornamr/acorn-dashboard) (https://moru.shinyapps.io/acornamr/). The application allows for real-time monitoring of surveillance and can be used for data visualisation and reporting. If it is not possible to access existing laboratory LIMS or WHONET data extracts, microbiology data can be directly entered into a WHONET database specifically for the project.Microbiology laboratory data, either extracted from an existing laboratory information management system (LIMS)/WHONET file or entered into WHONET software specifically for surveillance, is linked to clinical data using the ACORN project RShiny application (hboard) . This daACORN-HAI follows the ACORN II main study methodologies and procedures closely, with a specific focus on healthcare-associated bloodstream infection and ventilator-associated pneumonia (extended data 4 and 5). These two infection syndromes are chosen due to their association with drug resistant organisms and high mortality. In ACORN-HAI, healthcare-associated bloodstream infections encompass hospital-acquired bloodstream infections, i.e. any bloodstream infections taking place either after two days of admission or within 3 months of significant healthcare exposures, can be enrolled into the study.ACORN-HAI is complementary to ACORN II as it is designed to capture more cases of HAI and in more clinical detail for each enrolled case than ACORN II. ACORN II identifies HAI via weekly point prevalence surveys and daily ward screening (case-initiated), which will provide estimates for incidence and prevalence. However, the absolute number of HAI cases enrolled is likely to be lower than the actual incidence. ACORN-HAI aims to enrol all healthcare-associated bloodstream infection and ventilator-associated pneumonia in the participating wards through screening via positive blood or sputum cultures (isolate-initiated). Additional variables describing admission progression, antibiotic prescription and functional outcomes are collected with an addendum CRF .A participating site may participate in both or either ACORN II and ACORN-HAI study. In sites which implement both ACORN II and ACORN-HAI, only study participants who are enrolled into ACORN II will be enrolled into ACORN-HAI. In sites or wards which participate only in ACORN-HAI, similar clinical, microbiology and ward-level data as the ACORN II study will be collected such that the two datasets can be merged and analysed in a coherent manner .Enrolment criteria for ACORN-HAI are as follows:Bloodstream infectionInclusion criteria:1) Prescription/commencement of an intravenous antibiotic;Candida spp. pathogen(s) identified from blood specimen(s) either taken on or after day 3 of admission (Day 1 refers to the day of admission), or within 3 months of significant healthcare exposures; and 2) Growth of bacterial or3) Pathogen(s) in the blood specimen(s) satisfies either of the following:i.  bacterial orCandida spp. pathogen(s) identified from 1 or more blood specimens obtained by a culture; or1 or more non-common commensalii. ; andthe same common commensal bacterial pathogen identified from 2 or more blood specimens collected on separate occasions4) The same pathogen(s) in the blood specimen(s) was/were not present in the blood specimen(s) taken during the first 2 days of admission among those without significant healthcare exposures in the past 3 months.Exclusion criteria:Patient who has positive growth of organisms belonging to the following genera which are typically causes of community-associated infections and are rarely or are not known to be causes of healthcare-associated infections, or associated with severe immune suppression:a) Burkholderia pseudomallei,b) Brucella spp., including but not limited to,B. melitensis, B. abortus, B. suis, B. canis,c) Campylobacter, Salmonella, Shigella, Listeria, Vibrio and Yersinia.Ventilator-associated pneumonia1) Prescription/commencement of an intravenous antibiotic;; and 2) Clinical suspicion of ventilator-associated pnuemonia taken on or after day 3 of ventilation.3) Growth of bacterial pathogen(s) identified from respiratory specimen(s)Exclusion criteria:Patient who has positive growth of organisms belonging to the following genera which are typically causes of community-associated infections and / or are rarely or are not known to be causes of healthcare-associated infections:a) Streptococcus pneumoniae, Streptococcus suis, Haemophilus influenzae,b) Burkholderia pseudomallei,c) Coagulase-negative staphylococci,d) Talaromyces marneffei,e) Mycobacterium tuberculosis,f) M. mucogenicum, M. fortuitum, M. abscessus, M. chelonae, M. neoaurumRapidly growing Mycobacteria, including but not limited to,Acinetobacter spp.,Pseudomonas spp., Enterobacterales,Staphylococcus aureus,Enterococcus spp. and Candida (only from positive blood cultures), grown from routine clinical cultures will be collected from the microbiology laboratory and stored. The isolates will undergo whole genome sequencing at accredited laboratories.Bacterial isolates includingThere is no formal sample size calculation. This surveillance will enrol all eligible and consenting patients admitted to the surveillance wards during the surveillance period.All participating sites will develop an internal data monitoring plan to ensure adherence to ethics requirements, completeness and accuracy of data entry, and if applicable, isolate collection and storage. The local and overall data manager will conduct regular audits of the data entered and raise queries when necessary. Collated reports for monitoring progress and quality of data collected will be generated and presented to participating sites regularly.ACORN II site teams will interact with their local data in real time using the project app / dashboard. Via this dashboard, site investigators will have access to enrolment frequencies and patient demographics, follow-up on clinical outcome and day 28 status, weekly active HI point prevalence, microbiology summaries, and antimicrobial susceptibility and resistance patterns.The secondary outcomes are to: i) characterise multidrug resistant infections by clinical syndrome, place of acquisition , patient age group , sample type, and location , ii) quantify burden of multidrug-resistant infections in terms of attributable mortality and excess length of hospital stay, and iii) determine major parenteral antibiotic prescription indications by clinical syndrome, patient group , timing of prescription , and location . Analyses for these secondary outcomes will be performed after at least two years from study commencement (extended data 6).In general, data will be summarised in tables and graphs using descriptive statistics. For each selected key pathogen, the proportions of cases will be calculated, using the total number of participants from whom any pathogen was isolated as the denominator. Summaries will include the proportions of isolates resistant to key antibiotics, as defined by WHO GLASS or categorised as multidrug resistant, using standard definitions. Univariable and multivariable logistic regression models will be fitted to explore whether any clinical or microbiological variables are associated with the outcomes of resistance, mortality and discharged moribund. The unit of analysis will be admissions, with patient and site fitted as random effects.. At each site, observed crude case fatality rates  will be compared between cohorts: patients with multidrug resistant infection for selected pathogen-antimicrobial combination (cohort 1), and patient with non-multidrug resistant infection for selected pathogen-antimicrobial combination (cohort 2). Patients from cohort 1 and 2 will be matched 1:1 retrospectively. Ideally, matching will be by age category, admission ward, month of infection, and clinical syndrome at enrolment, and by the time from admission to infection. The effect of antibiotic resistance on vital status will be estimated using cause-specific Cox proportional hazards models, assessing the competing events of mortality and discharge alive, from the time of infection. Admissions, rather than patients, will be used as the unit of analysis and only the first relevant infection episode will be considered. A composite all-cause end- of-stay endpoint  will also be assessed which may be interpreted as an indication of the daily hazard of a patient’s admission ending. All models will include adjustment for the time between admission and infection. Proportional hazards assumptions will be checked using Schoenfeld residuals and visual inspection of log-log plots. Non-proportional hazards will be corrected using stratification.For attributable mortality analyses, the survival model approach outlined in the WHO GLASS protocol will be followed. Concordance (i.e. cultured isolate was susceptible) or discordance (i.e. cultured isolate was resistant) with microbiology test results will be determined. To assess the impact of initial treatment on mortality another set of Cox proportional hazards models will be run, but antibiotic resistance will be replaced by receipt of active initial therapy as the exposure of interest.Excess length of stay in days will be calculated using multistate models. The difference in expected length of stay will be estimated between the multidrug resistant and susceptible states. For empiric antibiotic analyses, drugs prescribed on the day of admission or symptom onset will be classified according to the WHO AWaRe criteria.To examine trends in infection rates over time, we will first use plots to visually inspect the trends to determine the most appropriate model/approach for analysis, while anticipating that random effects modelling with hospital clusters will be likely. Monthly incidence will be modeled  controlling for hospital characteristics including the country’s income status, type of hospital (tertiary versus community), and proportion of patients in specific age rangesIn the ACORN-HAI study, all clinical isolates will be processed for genomic sequencing. These genomic data will inform geospatial distribution of pathogen species and phenotypic resistance patterns and trends over the study period.Both ACORN II and ACORN-HAI are being implemented in study sites across Asia and Africa. ACORN II commenced in September 2021 in Asia and Africa and is actively enrolling in 19 hospitals. ACORN-HAI started enrolment in September 2022 and is fully implemented in 25 hospitals across Asia including Singapore, Malaysia, Vietnam, Thailand, Nepal, India, and Pakistan . ACORN II is a comprehensive antimicrobial resistance surveillance activity which advocates pragmatic implementation, and prioritises improving local diagnostic and antibiotic prescribing practices through patient-centred data collection and rapid feedback to physicians. It uses a systematic methodology to collect clinical, microbiology, and antibiotic use data. These data can be rapidly communicated to local physicians and infection prevention and control teams via an interactive web application to guide empiric antibiotic prescription and stewardship. Relative ease of data collection promotes sustainability and maximise participation and scalability.. In addition, prospectively collected data is less prone to information bias as relevant data are collected at baseline (without knowledge on outcomes) using standardised methods. Selection bias is minimised by implementing this surveillance tool in multiple antimicrobial resistance ‘hotspot’ regions to achieve a representative cohort of patients with multidrug-resistant infections.A prospective cohort design has strengths with regards to less risk of bias and confounding when compared to cross-sectional and retrospective study designs, albeit at the expense of higher cost. Important confounders in evaluating burden of drug resistant infections, e.g. attributable mortality, include comorbidities, initial disease severity at symptom onset, prior antibiotic exposure and time from admission to infectionACORN II has the potential to fill important gaps in our understanding of antimicrobial resistance epidemiology. Firstly, most existing antimicrobial resistance surveillance systems are passive, and based on routine antimicrobial susceptibility testing results generated by clinical microbiology laboratories alone. While such designs are useful to give broad pictures on the types of resistance and bacteria prevalent in a population, they cannot be used to relate the effect of resistance with patient outcomes. These proportions of resistance per pathogen antimicrobial combination lack relevant clinical metadata to be informative for local clinicians in their decision making or guideline development. Secondly, ACORN II focuses on LMICs where there is a mismatch in terms of antimicrobial resistance burden and surveillance capacity. ACORN II presents opportunities for participating sites to adopt and integrate a systematic surveillance programme into their routine workflows.Acinetobacter baumannii, carbapenem-resistantPseudomonas aeruginosa, carbapenem-resistant Enterobacterales and extended-spectrum beta-lactamase-producing Gram-negative bacteria. These antibiotic-resistant pathogens have been identified by the WHO as of the highest concern. This is because treatment options for carbapenem-resistant organisms are severely limited, associated with high toxicity, and often ineffective. ACORN-HAI, led by the ADVANCE-ID  network, will form the groundwork for subsequent interventional clinical trials targeting drug resistant infections. This reflects ADVANCE-ID’s main mission, which is to promote transnational research collaboration for a sustainable pipeline to conduct large-scale clinical trials in infectious diseases, particularly tackling antimicrobial resistance in LMICs where the need is the most urgent. With ACORN-HAI as an example, we foresee such extensions based on the ACORN II protocol in the future as hospital and research networks identify specific questions of interest.By focusing on severe healthcare-associated infections, ACORN-HAI builds on the main ACORN II protocol by collecting more detailed data on disease presentation, progression and treatment. The commonest pathogens causing healthcare-associated bloodstream infections and ventilator-associated pneumonia in LMICs are carbapenem-resistantWe acknowledge several potential limitations. Firstly, though ACORN is designed to be as efficient as possible such that it can be incorporated as part of routine workflow, additional staff and resources are required to maintain the surveillance system. This is a barrier to sustainability beyond the research funding period, and will necessitate further development and adaptation of enrolment and data collection procedures for sustainable scale-up going forward. Secondly, surveillance for antimicrobial resistance serves to inform interventions, guide treatment decisions, and identify emerging threats. Hence, ACORN may be less beneficial for sites that have limited familiarity and resources in infection prevention and control, as well as antibiotic stewardship. However, implementing an antimicrobial resistance surveillance system is an important first step to stimulate the initiation of such activities. Thirdly, differences in study implementation procedures across study sites, such as waiver of consent and choice of surveillance wards, may bias the demographics and disease severity of patients who enrol into the study. Lastly, identification of patients through clinical suspicion of infection syndromes, though pragmatic, may be sensitive to local physician practices and result in over- or under-estimation of certain infection syndromes such as ventilator-associated pneumonia.Implementation of a robust surveillance network for drug resistant infections is the necessary first step towards improved empirical antibiotic prescription, designing infection prevention and control policies, guiding resource allocation, and motivating novel treatment therapy clinical trials. A focus on severe drug resistant infections with the highest health and economic consequences will align the interests and expectations from various stakeholders including patients, clinicians, health ministries and pharmaceutical industry. Such surveillance data will engage all parties to direct a concerted effort to tackle the urgent issues in antimicrobial resistance.  In response to my prior feedback, the authors have provided thoughtful revisions to the manuscript. I appreciate their clarification on the evolution of the ACORN study, detailing the transition from ACORN I (pilot) to ACORN II, and further to the extension study, ACORN-HAI. Their emphasis on the synergy between ACORN II and ACORN-HAI, especially in the context of enrolling an increased number of patients with HAI and capturing in-depth clinical data, is well-articulated. Furthermore, the inclusion of key study limitations in the discussion adds depth and transparency to the paper. The improvements notably address the initial concerns and enhance the manuscript's overall clarity and contribution.Is the study design appropriate for the research question?PartlyIs the rationale for, and objectives of, the study clearly described?YesAre sufficient details of the methods provided to allow replication by others?PartlyAre the datasets clearly presented in a useable and accessible format?Not applicableReviewer Expertise:Microbiology,  AMR, infectious diseasesI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. I have thoroughly reviewed the current version provided by the authors, and I am pleased to note that the majority of my concerns have been effectively addressed. I extend my appreciation to the authors for their diligent efforts and commendable work. I wish them all the best moving forward.Is the study design appropriate for the research question?PartlyIs the rationale for, and objectives of, the study clearly described?PartlyAre sufficient details of the methods provided to allow replication by others?PartlyAre the datasets clearly presented in a useable and accessible format?Not applicableReviewer Expertise:Clinical Microbiology, Computational Microbiology,  Clinical Bioinformatics (Genomics)I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The article presents the protocol for ACORN II, a surveillance network aimed at monitoring antimicrobial resistance (AMR) in a clinically oriented manner. The study focuses on patients with severe bacterial infections in low- and middle-income countries (LMICs), where the burden of AMR is high and surveillance capacity is limited. The ACORN II surveillance network aims to provide a comprehensive picture of AMR, focusing on both community-acquired and healthcare-associated infections. The study includes patients with clinically compatible acute bacterial infection syndromes and collects data on patient characteristics, infection syndromes, microbiology, and antibiotic use. The data is then rapidly communicated to local physicians and infection prevention and control teams via an interactive web application to guide empiric antibiotic prescription and stewardship. The study also includes an extension, ACORN-HAI, which focuses on healthcare-associated bloodstream infection and ventilator-associated pneumonia due to their association with drug-resistant organisms and high mortality. The ACORN-HAI study follows similar methodologies and procedures as the main ACORN II study but collects more detailed data on disease presentation, progression, and treatment. Constructive Feedback: Clarify the Objectives/methods: The authors should provide a clearer link between the specific objectives of the study and the methods used to achieve them. This would help readers understand how the study design and data collection methods will help achieve the stated objectives. Clarify the Role of ACORN-HAI: The authors introduce ACORN-HAI as an extension of ACORN II, but it's not clear how these two studies are related. The authors should provide more information on the role of ACORN-HAI and how it fits into the overall ACORN II study. Limitations: Every study has limitations, and it's important for the authors to acknowledge these. For instance, are there potential biases in the study design? How generalizable are the findings likely to be, given that the study is being conducted in LMICs? Figures: The content of Figures 3A and 3B is not easily readable, which limits their usefulness in the context of the study. Overall, the article presents a valuable protocol for monitoring antimicrobial resistance in LMICs. With some improvements in clarity and detail, it could serve as a useful guide for other researchers in this field.Is the study design appropriate for the research question?PartlyIs the rationale for, and objectives of, the study clearly described?YesAre sufficient details of the methods provided to allow replication by others?PartlyAre the datasets clearly presented in a useable and accessible format?Not applicableReviewer Expertise:Microbiology,  AMR, infectious diseasesI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. I applaud the team for their brilliant work. The authors' aim to incorporate patient- or clinically-oriented AMR surveillance alongside isolate-based AMR surveillance is important because it acknowledges the multifaceted nature of AMR and can enable a comprehensive understanding of the problem. This approach can enhance the development of effective strategies to combat AMR and promote better patient outcomes through targeted interventions and improved antimicrobial stewardship. Upon evaluating this protocol, I have the following observations: The authors describe ACORN-II and ACORN-HAI in this protocol. In the title, they present this as an ACORN-II protocol. They describe a surveillance system that includes hospitalized patients… with clinically compatible acute community-acquired or healthcare associated bacterial infection syndromes… They also mention diagnostic activities that will be implemented to optimize culture specimen collection practices and later state that patient characteristics are already being recorded on enrolment and after 28 days of follow up... My concern with the authors’ approach is that they mix various elements in this protocol, making it somewhat unclear. Moreover, the fact that the protocol is already being implemented adds complexity to evaluating its suitability. I would have preferred the authors to follow a more systematic flow in their writing. They should have summarized ACORN-I since it presumably serves as the baseline for ACORN-II. Additionally, they should have described the advancements or updates in ACORN-II compared to ACORN-I, such as novel approaches, methodologies, or updated protocols etc. For instance, ACORN-I (the pilot study) focused on three infection syndromes, and it would be helpful to clarify which of these ACORN-II focuses on and why? Currently, the authors seem to present ACORN-I as GLASS, which is somewhat perplexing. Since they state that ACORN-II builds on the WHO-GLASS, to measure... I think they need to base ACORN-II more on the pilot study (ACORN-I) and less on GLASS. The authors briefly mention a surveillance system but fail to provide a comprehensive description of its components. Is it unclear whether this system encompasses ACORN in general, including both ACORN-I and ACORN-II, or if it specifically refers to ACORN-II and its extensions, such as ACORN-HAI? Although the authors introduce ACORN-HAI as an extension of ACORN-II, they should consider describing it fully in this protocol since it appears to be an independent study on its own. It would be helpful for the authors to clearly distinguish between ACORN-II and ACORN-HAI and explain the rationale behind the extension (ACORN-HAI). The authors mention ongoing activities while also stating that some activities are not yet underway (based on their writing tenses). This makes it challenging to provide feedback on the protocol if certain study activities are already underway. I would appreciate clarification from the authors to better understand what has been done, what is in progress, and what remains to be done. Regarding the study design versus the specific objectives, the authors state that ACORN-II is a prospective observational study . This implementation phase makes it difficult to provide comments on a protocol that is already being executed. However, I am concerned that the general design may not effectively achieve the specific objectives outlined by the authors (i to v). I recommend that the authors provide further clarification in this regard. Lastly, I suggest that the authors clearly define the methods that they intend to use to achieve each specific objective in a systematic manner. This would facilitate a more thorough evaluation and allow for feedback on the proposed approaches.Is the study design appropriate for the research question?PartlyIs the rationale for, and objectives of, the study clearly described?PartlyAre sufficient details of the methods provided to allow replication by others?PartlyAre the datasets clearly presented in a useable and accessible format?Not applicableReviewer Expertise:Clinical Microbiology, Computational Microbiology,  Clinical Bioinformatics (Genomics)I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.


Research in epigenetics has dramatically risen during the last decade to include aspects of environmental biology. However, many questions remain regarding the effects of environmental stressors on the epigenome, incorporating the particular role of epigenetic mechanisms in the adaptation and evolution of organisms in changing environments. Epigenetics is commonly defined as mitotically and/or meiotically heritable changes in gene function that occur without altering the underlying DNA sequence. It encompasses DNA (hydroxy)methylation, histone modifications, chromatin structure, and non-coding RNAs that may be inherited across generations under certain circumstances. Epigenetic mechanisms are perfect candidates to extend our understanding of the impact of environmental stressors on organisms and to explain the rapid phenomenon of adaptive evolution. Existing evidence shows that environmental cues can affect the epigenome and modify gene expression accordingly. These changes can then induce phenotypic modifications that are morphological, physiological, or behavioral at the organismal level. In this Special Issue focusing on environmental epigenetics, we provide an overview of influences to the epigenome that are driven by various environmental and evolutionary factors, with a particular focus on DNA methylation (DNAm). Five research groups have contributed insightful studies or reviews on (1) DNAm and demethylation events affected by the exposome; (2) DNAm as a potential biomarker to determine cardiometabolic risk early in life; (3) consequences of DNAm across multiple generations; (4) DNAm variation within natural animal populations; and (5) epigenetic mechanisms in genetically uniform organisms. Collectively, the articles from this Special Issue consistently support that environmental changes can induce long-lasting epigenetic effects within a given organism pertaining to individual risk for disease, or multi-generational impacts that ultimately impact evolution. Nature versus nurture argues that the fitness of an individual is primarily driven by either their genetic makeup or their environment. The concept of epigenetics greys the boundary on this debate by clarifying how environmental factors such as climate, nutrition, stress, and toxicants influence gene expression, and thus biological processes, without altering the underlying DNA sequence . DNA metPhenotypic variation is a key element in populations to permit adaptation and evolution to environmental changes. It has been acknowledged that phenotypic variation can arise from either genetic variation or phenotypic plasticity, wherein the same genome exhibits different expressions in response to environmental cues. However, emerging evidence suggests that epigenetic mechanisms can also underlie phenotypic plasticity . EpigeneThis Special Issue on environmental epigenetics includes a total of five articles, with two original studies and three insightful reviews. Collectively, the two original studies indicate how environmental exposure to chemicals influence DNAm-directed mechanisms and human health.PALB2) is a gene known to be disrupted in breast cancer and also contains C > T mutations. Courant and colleagues highlighted a proof of concept supporting that environmental exposures can guide DNA (de)methylation reactions to instigate C > T mutations. They utilized a mammary epithelial cell model (MCF10A) coupled with environmental exposures, including folate, Diuron, glyphosate, perfluorooctanoic acid (PFOA), iron, zinc, and ascorbic acid, to determine that exposomes can generate C > T mutations effecting DNA (de)methylation at PALB2: .In addition to epigenetic dysregulation, DNA damage and mutagenesis are extensively studied for their oncogenic effects. The deamination of 5-methyl-cytosine (5mC) to thymine (C > T) can occur actively or passively via DNA (de)methylation or hydrolytic deamination, respectively. It is also a hallmark mutational signature in human cancers, including breast cancer. Partner and localizer of BRCA2 (LINE-1) repeat element, as well as the maternally imprinted transcript H19, 11-hydroxysteroid dehydrogenase type 2 (11b-HSD-2) and peroxisome proliferator-activated receptor alpha (PPAR-⍺). The authors concluded that DNAm may serve as an indicator for early life risk for cardiometabolic abnormalities in specific genomic loci related to LINE-1 and 11b-HSD-2: .Epigenetic modifications such as DNAm may serve as biomarkers, which are often used for the early detection of diseases or as determinants of disease risk. These modifications can further be interrogated to assess disease-associated mechanisms for potential interventions. In previous reports, DNAm changes have been associated with cardiovascular disease, specifically during adulthood. However, it remains unclear how environmental perturbations can impact cardiometabolic diseases during the susceptible life stage of adolescence. To answer this question, Aljahdali and colleagues utilized early childhood and adolescent blood leukocyte DNAm as a determinant of cardiometabolic health from the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) birth cohort. They quantified DNAm changes at the long interspersed nuclear element-1 .In recent years, there has been a notable surge in research interest in the intraspecific spatial distribution of epigenetic marks, with a particular focus on DNAm. Within this Special Issue, Chapelle and Silvestre conduct a comprehensive review of DNAm variation observed in natural animal populations. They consider the hypothesis that epigenetic variation is independent from genetic variation and explore how DNAm dynamics contribute to the establishment of population structure. They present compelling arguments on the significant roles played by epigenetics in reshaping traditional evolutionary theories. By examining the relationship between genetics and epigenetics, their review enhances our understanding of the factors that influence evolutionary processes within natural animal populations. As a result, it paves the way for further investigations and advancements in the field of epigenetics, ultimately enriching our comprehension of the complexities of evolutionary biology: .To comprehensively assess the potential impact of epigenetics on adaptation and evolution, it is crucial to understand its relationship with genetic variation. To date, only a limited amount of empirical research has been dedicated to disentangling epigenetic from genetic variation. One promising approach to this challenge is the study of genetically uniform populations, which are more commonly found in asexually reproducing organisms. In this context, Vogt’s review within the current Special Issue emerges as a pivotal contribution and highlights the fundamental role played by epigenetic variation in the adaptive capacity of genetically uniform organisms across diverse environments. Vogt’s review draws from a wide array of examples spanning bacteria, protists, fungi, plants, and animal populations to illustrate how epigenetic mechanisms facilitate environmental adaptation in these organisms. Moreover, the author delves into the mechanistic explanations of both the general purpose genotype hypothesis and the genetic paradox of invasions, suggesting that epigenetic processes can contribute to these phenomena: .The stability of epigenetic marks across generations is a crucial aspect to comprehend the roles of epigenetics in the adaptation and evolution of populations. In this current Special Issue, Hanson and Liebl’s review comprehensively evaluates the implications of DNAm both within individual generations and in its transgenerational effects. One intriguing and relatively underexplored area of DNAm is its mutagenic potential and capacity to induce rapid evolutionary changes. The authors explore how DNAm can influence DNA mutation rates in a more directed and accelerated manner compared to classical stochastic genetic mutations alone. By shedding light on the mutagenic properties of DNAm, Hanson and Liebl’s review opens new avenues for understanding how epigenetic mechanisms can contribute to the rapid and directed evolution of populations: (Taken together, all five articles in this Special Issue emphasize the critical need to interrogate epigenetic mechanisms of intrinsic and extrinsic stimuli that predict the health of individuals later in life, as well as populations throughout evolution.


During the last 30 years, we have assisted to a great implementation in anticancer treatment with a subsequent increase of cancer survivors and decreased mortality. This has led to an ongoing interest about the possible therapy-related side-effects and their management to better guide patients therapy and surveillance in the chronic and long-term setting. As a consequence cardio-oncology was born, involving several different specialties, among which radiology plays a relevant role. Till the end of August 2022, when European Society of Cardiology (ESC) developed the first guidelines on cardio-oncology, no general indications existed to guide diagnosis and treatment of cancer therapy-related cardiovascular toxicity (CTR-CVT). They defined multimodality imaging role in primary and secondary prevention strategies, cancer treatment surveillance and early CTR-CVT identification and management.Cardiac computed tomography angiography (CCTA) has acquired a central role in coronary assessment, as far as coronary artery disease (CAD) exclusion is concerned; but on the side of this well-known application, it also started to be considered in left ventricular function evaluation, interstitial fibrosis quantification and cardiac perfusion studies.Cardiac magnetic resonance (CMR), instead, has been acknowledged as the gold standard alternative to trans-thoracic echocardiography (TTE) poor acoustic window in quantification of heart function and strain modifications, as well as pre- and post-contrast tissue characterization by means of T1-T2 mapping, early Gadolinium enhancement (EGE), late Gadolinium enhancement (LGE) and extracellular volume (ECV) evaluation.Our review is intended to provide a focus on the actual role of CMR and CCTA in the setting of a better understanding of cardiotoxicity and to draw some possible future directions of cardiac imaging in this field, starting from the recently published ESC guidelines. This has led to an ongoing interest about the possible therapy-related side effects and their management to better guide patients therapy and surveillance in the chronic and long-term setting. For this purpose, a new discipline was born, namely, cardio-oncology, involving several different specialties, among which radiology plays a relevant role in both cardiotoxicity identification and follow-up through multimodality imaging.During the last 30 years, we have assisted to a great implementation in anticancer treatment with a subsequent huge increase of cancer survivors. Nowadays, the consensus statement of the International Cardio-Oncology Society (IC-OS) has overcome these issues with a specific definition of cancer therapy-related cardiac dysfunction (CTRCD) as cardiomyopathy, heart failure (HF), myocarditis, vascular toxicity, hypertension (HTN), cardiac arrhythmias, and corrected QT interval prolongation induced by anticancer therapy. For CTR-CVT-induced pericardial and valvular heart diseases, definitions are the same used for the general population.Over the past decades, different definitions have been used to describe cancer therapy-related cardiovascular toxicity (CTR-CVT), generating confusion and misdiagnosis. Among them, the most common is the one given by the American Society of Echocardiography, describing cancer treatment-related cardiac dysfunction as a decrease in left ventricular ejection fraction (LVEF) of >10% to a value <53%.Till the end of August 2022, no international guidelines have existed to guide diagnosis and therapy of CTR-CVT, when the first European Society of Cardiology (ESC) guidelines on cardio-oncology were developed in collaboration with the European Hematology Association (EHA), the European Society for Therapeutic Radiology and Oncology (ESTRO) and IC-OS.As already stated, multimodality imaging plays a central role in primary and secondary prevention strategies, cancer treatment surveillance and early CTR-CVT identification and management. Our review will mainly focus on the actual role of cardiac magnetic resonance (CMR) and cardiac computed tomography angiography (CCTA) in the setting of a better understanding of cardiotoxicity predictive factors, early diagnosis, treatment, and follow-up. We will briefly analyze them from a clinical and pathophysiological point of view.The most common clinical manifestations of CRT-CVT are acute and chronic HF, myocardial ischemia, electrophysiological disturbances, HTN, and vessel thromboembolism. Cyclophosphamide and ifosfamide are thought to cause direct endothelial injury by means of extravasation of toxic metabolites leading to cardiomyocytes damage, interstitial damage, and hemorrhage. Bevacizumab has been demonstrated to induce HF via uncontrolled HTN and inhibition of Vascular Endothelial Growth Factor (VEGF), while Trastuzumab interferes with normal cardiomyocytes growth by inhibiting their epidermal growth factor receptor 2 (Erb2).HF is mostly induced by the administration of anthracyclines, cyclophosphamide, ifosfamide, bevacizumab, trastuzumab, and imatinib. From the pathophysiological point of view, free radical formation is recognized as the main mechanism of cardiotoxicity in anthracyclines treatment. From the pathophysiological point of view, coronary artery thrombosis, arteritis, and vasospasms are proposed as the main causative mechanisms of ischemia in antimetabolites and antimicrotubules therapy . Bevacizumab, instead, seems to cause myocardial ischemia through its anti-VEGF mechanism.Myocardial ischemia is commonly diagnosed in cancer patients, particularly in those treated with antimetabolites, antimicrotubules, and monoclonal antibody-based tyrosine kinase inhibitors. They can be caused by anti-cancer-induced fibrosis and radiation therapy, directly affecting the heart conduction system. Concerning bradycardia, paclitaxel is considered as the main causative agent among chemotherapeutic drugs via its direct effect on Purkinje fibers or extracardiac autonomic control. QT prolongation causative mechanisms remain unknown and are still quite rare, especially related to small molecule tyrosine kinase inhibitors.Bradycardia and QT prolongation are the main electrophysiological disturbances found in CRT-CVT. Studies have shown that it may be related to the VEGF inhibition, decreasing nitric oxide production in arterioles leading to increased vasoconstriction and peripheral vascular resistance.HTN is the most frequent comorbid condition reported in cancer patients. Cisplatin, for example, induces platelet activation and aggregation interfering with monocytes pro-coagulant activity and elevating von-Willebrand factor levels.Cancer patients commonly stay in a pro-thrombotic state, which is in some cases worsened by specific anticancer treatment, like alkylating agents and angiogenesis inhibitors, frequently resulting into vessels’ thromboembolism.Clinical manifestations for chemotherapeutic agents are reported in In the last decades, immunotherapy has become of common use in the treatment of various diseases, such as non-small cell lung cancer, renal cell carcinoma, and both Hodgkin and non-Hodgkin lymphoma. Checkpoint inhibitor immunotherapy  is immunomodulatory antibodies that are used to enhance the immune system. These agents have substantially improved the prognosis for patients with advanced malignancy.Despite clinical benefit, ICI are associated with a broad spectrum of side effects that are caused mainly by their activity on the immune system: these side effects are known as immune-related adverse events (IrAEs) with various manifestations including cardiotoxicity in up to 1% of patients.Its manifestation typically includes hypotension, arrhythmia, and left ventricular dysfunction, typically in the setting of cytokine release syndrome. Clinical features are extremely variable, in terms of disease onset and severity of manifestations, ranging from fatal myocarditis to transient myocardial edema. Median interval time from first treatment to disease presentation is typically 1–2 months, but cases of delayed onset have been reported up to one year.Of significant interest for the role of advanced imaging is the occurrence of acute ICI induced myocarditis, whose incidence is documented around 0.04–1.14% and which has been associated with a worsening of mortality.The underlying pathophysiology for ICI-associated myocarditis is not yet fully understood.One possible mechanism is that T cells could target an antigen shared by both the tumor and the heart. Preclinical studies in mouse models have demonstrated that PD-1 protects the heart against T-cell-mediated inflammation and that PD-1-deficient mice develop myocarditis. Similarly, CTLA-4-deficient mice also developed autoimmune myocarditis with infiltration of CD4+ and CD8+ T lymphocytes in the myocardium. Deletion of the CTLA-4 and PD-1 axis led to autoimmune myocarditis, which suggests that PD-1/PDL1 and CTLA-4 play important roles in limiting T-cell-mediated autoimmune myocarditis.Other mechanisms underlying ICI-mediated cardiotoxicity include pericardial inflammation by ICI-stimulated cytotoxic T-cells and myocardial ischemia. Indeed, ICI-associated inflammation may act on atherosclerotic coronary plaques and trigger fibrous cap rupture, leading to acute myocardial infarction. A second possible underlying mechanism of ICI-associated acute myocardial ischemia is coronary spasm causing ST elevation secondary to PD-1 inhibitor (pembrolizumab) treatment. The exact mechanism of coronary spasm remains unclear but may be related to systemic inflammatory response syndrome. A third explanation for ICI-related acute MI is the direct activation of T-cell-mediated coronary vasculitis in the absence of atherosclerosis. An indirect effect of ICIs on the coronary vasculature via a sudden release of large amounts of catecholamine from the adrenal glands or postganglionic sympathetic nerves in the heart, could result in catecholamine-mediated myocardial stunning (Tako-tsubo syndrome).Almost all cardiovascular adverse reactions, especially myocarditis, are firstly clinically suspected with cardiac enzymes and ECG alteration, but most of the evidence suggest CV imaging is requested to perform an accurate diagnosis. For this purpose, trans-thoracic echocardiography (TTE) and CMR are interchangeably indicated by 2022 ESC guidelines, but a specific and exclusive role of CMR could be postulated on the basis of modified Lake Louise Criteria accuracy., aiming at giving all the healthcare professionals the instruments to take care of oncologic patients before, during and after cancer treatment as far as the cardiovascular system is concerned. They represent a consensus on definition, diagnosis, treatment, and prevention of CTR-CVT. Guidelines include clear indications regarding the role and the appropriate use of different imaging modalities in various clinical scenarios, namely, cardiovascular toxicity risk stratification before anticancer therapy and cardiovascular surveillance during treatment  by means of coronary calcium score quantification and to detect intracardiac masses. Accurate imaging techniques are requested for this aim, like 3D echocardiography or CMR. Choice of favourite imaging technique depends on the local availability and expertise; no strict indication favors TTE over CMR at this point but the same imaging modality is recommended over the entire follow-up to allow a precise comparison. TTE is still recommended for left ventricular function and GLS assessment to detect cardiac dysfunction, but in case of poor image quality or non-diagnostic TTE, CMR has to be considered  like HTN, HF, acute coronary syndromes, arrhythmias, pulmonary HTN, and venous thromboembolism. Among them, HF with preserved ejection fraction is frequently associated with cardiac amyloidosis in MM but also to PI therapy; in this setting, a baseline CMR before treatment initiation followed by TTE every three cycles of PI is the commonly accepted surveillance protocol.A specific indication for CMR exists as far as cardiovascular surveillance for multiple myeloma (MM) targeted therapy is concerned. Proteasome inhibitors (PI) – bortezomib, carfilzomib, and ixazomib – have become a mainstay for MM treatment.CCTA is a widely available imaging technique for the evaluation of coronary anatomy and pathology, which can be exploited in the setting of secondary prevention, as far as CAD rule-out is concerned. Despite this knowledge, while clear recommendation exists to rule out CAD in case of first evidence of HF with reduced ejection fraction, no guidelines recommend a pre-treatment evaluation of CAD. Due to its high sensitivity in excluding CAD, CCTA is now widely used to exclude coronary stenosis in patients with exposure to cardiotoxic anticancer treatment and newly developed reduced LVEF. Non-contrast CT provides information about calcium burden within the coronary arteries as a reliable predictor of cardiovascular risk; it can be used to drive cardio-protective therapy before and during cytotoxic anticancer treatment like anthracyclines as proposed by the society of Cardiovascular Computed Tomography and the Society of Thoracic Radiology.Pre-existing CAD has been demonstrated to contribute to an increased risk of cardiovascular toxicity in patients undergoing anti-cancer treatment.An example of CAD rule-out by CCTA is shown in ) No strong evidences exist about CCTA role in cardiac function assessment and tissue characterization, where CMR is still superior. As far as tissue characterization is concerned, which is the main early markers of cardiotoxicity, future CCTA technology, like Photon Counting CT, may lead to CCTA as a competitive imaging modality in CTR-CVT early recognition and prevention. Radiation exposure issues still represent a major limitation to CCTA use, although new multidetector machine already maximally reduce dose.As already stated, CCTA value in Cardioncology field is nowadays confined to CAD rule-out in case of reduced LVEF, playing a limited role in all the other possible clinical scenarios, according to ESC 2022 guidelines. or more than 10% without other recognized causes.The spectrum of tissue abnormalities characterizing myocardial toxic effects of chemotherapy ranges from tissue edema and necrosis to the development of focal replacement and interstitial fibrosis. CMR has also been recognized as a significant prognostic tool in various clinical settings, which can be easily extended to the field of cardio-oncology.  and  while increase in T2 relaxation time is associated with acute myocardial edema, being a water-sensitive process. Generally speaking, cardiotoxic drug exposure has been associated with an increase in both native T1 and T2 relaxation time  and/or LGE is a hallmark of underlying pathologic processes like inflammation, edema, and myocardial fibrosis A limited tolerability of the exam does not allow to perform it in claustrophobic or patients unable to maintain apnea. Despite all the possible application of CMR in function assessment, tissue characterization and strain evaluation, still limited robust evidences exist about their changes in Cardiotoxicity, to allow for accurate diagnosis and prevention of CTR-CVT.Major CMR limitations derive from its costs and exam tolerability by patients, for which reason TTE still represents the first imaging modality of choice in Cardioncology according to ESC 2022 guidelines.Despite its exclusively approved role in CAD rule-out, literature evidence suggests possible CCTA use for left ventricular function evaluation, cardiac perfusion studies, and tissue characterization. and CMR quantification of late gadolinium enhancement was validated as a gold standard non-invasive method for the identification of focal myocardial fibrosis in the setting of ischemic and non-ischemic cardiomyopathy. In the last 20 years also CCTA has been used for the assessment of cardiac fibrosis by means of ECV quantification. The same imaging protocol as CMR has been adopted with a non-contrast low-dose ECG-gated CCTA acquisition followed by multiple imaging after contrast administration at a given time delay, usually around 10 min. A good correlation was found between CCTA findings and both CMR ECV and histological fibrosis also in cancer patients undergoing anthracycline chemotherapy regimens. CCTA-based ECV calculation has also some drawbacks, namely, the increased radiation dose delivered to the patients along with difficulty in image registration and segmentation. These aspects seem to be possibly overcome by the introduction of dual source energy CCT: reduced radiation dose through the elimination of pre-contrast images, improvement of image quality by beam hardening artefact reduction, and less wrong image registration.Multiple studies demonstrated extracellular volume (ECV) increase in patients treated with cardiotoxic chemotherapy compared to matched control population,From a functional perspective, subtle early tissue changes following chemotherapy may induce regional wall motion impairments that can be detected using advanced strain CMR-based imaging. This technique is based on the principle of tracking tissue voxel motion using standard steady-state free precession sequences.In the last years, left ventricular GLS merged as a more accurate index of left ventricular dysfunction, as demonstrated by the SUCCOR (Strain sUrveillance of Chemotherapy for improving Cardiovascular Outcomes) international multicenter prospective randomized controlled trial. Echocardiographic GLS-guided cardioprotective therapy better prevents chemotherapy-induced cardiotoxicity in comparison with LVEF in a high-risk population receiving cardiotoxic chemotherapy. A prospective study conducted by Drafts et al demonstrated an early subclinical deterioration in global circumferential strain (GCS) in patients receiving low-moderate dose of anthracyclines along with subclinical decline of LVEF, proving GCS as an early predictor of cardiotoxicity. An example of the utility of CMR longitudinal circumferential and radial strain as an early marker of cardio-toxicity is shown in  Their evaluation is recommended in all patients screened before cardiotoxic cancer treatment initiation to stratify CTR-CVT risk and to identify significant changes during treatment.CMR studies have proposed myocardial global longitudinal circumferential and radial strain to describe LV dysfunction, also in patients who have undergone anthracyclines and trastuzumab therapy.Among the different values of CMR strain, GLS and GCS are the most used because of less technical limitations with respect to global radial strain (GRS). Coherently, several studies have also demonstrated a significant decrease in left ventricular mass after anthracycline therapy. De Souza et al introduced a new method of measuring cardiomyocytes size with CMR, the intracellular water lifetime. It demonstrated a decrease in left ventricular mass due to cardiomyocytes atrophy in females treated with anthracyclines for breast cancer. Another study found an association between left ventricular mass decline, increased risk of cardiac events and HF symptoms more than LVEF decline.In the specific case of anthracyclines, decline in LVEF and deterioration of strain  values has been postulated to be associated with an atrophic remodeling of myocardium induced by DNA oxidative damage.Among all possible myocardial inflammation, ICI-related myocarditis represents a specific pathological entity from a clinical, radiological, and histopathological point of view. Differently from non-ICI myocarditis, less than 50% of cases present with a decreased ventricular function and relevant LGE.Most of patients developing ICI myocarditis show atypical patterns of LGE, without any association with pathology prognosis, meaning LGE approach is necessary but not sufficient for their evaluation.Quantitative parametric T1 and T2 mapping, as well ECV, seem to represent a real instrument to avoid underdiagnoses and overcome this inconsistency, in such a way to allow for a proper and prompt diagnosis of a pathology which brings a relevant mortality into the general population. demonstrate a better correlation between T1 and T2 mapping and both clinical and histological ICI-induced myocarditis characteristics. T1 mapping alterations are significantly correlated with symptom severity as well as to the histological specimen; even better and more significantly than T2 mapping, T1 mapping alteration seems to be correlated with the incidence of major cardiovascular adverse events.Recent studies, like the one of Paaladinesh et al,An example of ICI-induced myocarditis is shown in This opens to the possibility of designing specific CMR protocols, centered on quantitative parametric mapping, with a diagnostic and prognostic role from the pre-subclinical stages to the full-blown pathology. we propose a novel baseline risk assessment flowchart for patient’s candidate to potential cardiotoxic therapy, according to our Institution’s clinical routine (Despite the recently published ESC guidelines, on the basis of an accurate review of the new risk assessment tools from Cardioncology Study Group of the Heart Failure Association of the European Society of Cardiology in collaboration with the International Cardioncology Society, routine .A baseline Calcium Score quantification on a non-contrast ECG-gated chest CT performed during the cancer staging phase is strongly suggested according to the strong evidence correlation between coronary calcification and increased risk for a major adverse cardiac event.Regardless of these solid and consistent scientific bases, current CMR role is apparently limited to patients with poor acoustic TTE window, hypertrophic cardiomyopathy, and PI-induced MM, but its potential goes far beyond these applications. CMR should be considered as a first-line imaging at least in patients at high/very high risk to develop CTR-CVT, in order to be able to precisely estimate ventricular volumes and function but also to perform tissue characterization and identify subclinical changes by means of mapping, LGE sequences and ECV evaluation. Whenever CMR results normal, patients may be followed-up clinically and according to the standard guideline; in the case of any pathological abnormalities, following investigations should be performed with CMR for the sake of reproducibility and precision.While TTE is still recognized as the favourite imaging technique for baseline risk stratification, guidelines opened a new potential window for CMR application, but the pathway is still ongoing.ESC 2022 and all other possible applications for functional evaluation, GLS, LGE, and ECV quantification are not even mentioned for its poor availability and standardization. The main potential strength of the introduction of CCTA in cardiovascular toxicity risk assessment and evaluation during therapy is that oncologic patients routinely undergo computed tomography exams for cancer staging, monitoring, and follow-up. This would mean maximizing the usefulness of an exam which cannot be bypassed in oncologic patients and adapting it to a double use: the purely oncologic staging and follow-up but also the possible complete cardiac evaluation in specific subsets of the population at risk of CTR-CVT.CCTA use in this setting still remains limited to CAD exclusion in low-intermediate risk patients More evidences are needed about the possible clinical role of the above-mentioned imaging findings in guiding multifactorial therapeutic pathways and decision.Application of advanced cardiac imaging in oncology is an open field for research.


Smart wearable devices enable personalized at-home healthcare by unobtrusively collecting patient health data and facilitating the development of intelligent platforms to support patient care and management. The accurate analysis of data obtained from wearable devices is crucial for interpreting and contextualizing health data and facilitating the reliable diagnosis and management of critical and chronic diseases. The combination of edge computing and artificial intelligence has provided real-time, time-critical, and privacy-preserving data analysis solutions. However, based on the envisioned service, evaluating the additive value of edge intelligence to the overall architecture is essential before implementation. This article aims to comprehensively analyze the current state of the art on smart health infrastructures implementing wearable and AI technologies at the far edge to support patients with chronic heart failure (CHF). In particular, we highlight the contribution of edge intelligence in supporting the integration of wearable devices into IoT-aware technology infrastructures that provide services for patient diagnosis and management. We also offer an in-depth analysis of open challenges and provide potential solutions to facilitate the integration of wearable devices with edge AI solutions to provide innovative technological infrastructures and interactive services for patients and doctors. Heart failure affects approximately 26 million people worldwide and causes over 1 million hospitalizations in Europe and North America alone, causing a significant burden to healthcare systems ,4. SeverThis paper, therefore, provides an up-to-date comprehensive collation and analysis of existing edge AI solutions deployed in wearables for CHF-prevention or management-applications. With this literature analysis, we delineate the extent to which edge AI is implemented to improve the efficacy of technology applied to the CHF context. We also identify research gaps and provide recommendations to potentially improve the efficiency of the edge AI paradigm in wearable-based CHF prevention and management.The rest of the paper is organized as follows: CHF is increasingly becoming a social, health, and economic burden because of population growth, ageing, and the augmented prevalence of comorbidities. A technological approach to this disease may be essential to reduce this burden. Understanding the mechanics of CHF is crucial for developing relevant and effective technology intervention solutions to improve the primary and secondary prevention of cardiovascular events and heart failure re-actualization. Therefore, this section provides some medical facts about CHF, including how it is diagnosed and related to other cardiovascular diseases (CVDs).CHF is a complex clinical syndrome resulting from the structural modifications and/or functional limitations of the heart that determine a reduction in the cardiac output or augmentation of the pressure within the atrial and ventricular chambers at rest or during physical activities. The typical clinical signs and symptoms of heart failure are peripheral oedema, pulmonary crackles, dyspnea, and fatigue . In addiThe suspicion of CHF starts from evaluating clinical signs and symptoms of heart failure, which may vary according to the patient’s medical history of arterial hypertension, chronic coronary syndrome, diabetes mellitus, dyslipidemia, chronic kidney disease, and alcohol and smoking habits.Diagnostic tests include electrocardiogram, echocardiography, and dosage of blood chemistry parameters such as B-type natriuretic peptide (BNP) urea and electrolytes, creatinine, complete blood count, and liver and thyroid function . EchocarCHF is a complex and heterogeneous syndrome frequently associated with other cardiovascular diseases like coronary artery disease (CAD), the leading cause of CHF. CAD symptoms often overlap with those of heart failure; therefore, the onset of new signs and symptoms like dyspnea and fatigue may also indicate ischemic myocardial dysfunction .Rhythm conduction disturbances, like atrial fibrillation and CHF, frequently coexist due to common pathophysiological mechanisms such as structural heart abnormalities, volume, pressure overload, and alterations in neurohormonal system activation .Arterial hypertension can also be a significant risk factor for CHF if not adequately treated. Elevated blood pressure evokes an adverse remodeling of ventricular chambers and an augmentation of diastolic pressure that may lead to myocardial dysfunction .Moreover, other comorbidities such as iron deficiency, anaemia, sleep disorders, chronic obstructive pulmonary disease, chronic kidney renal failure, thyroid disorders, and liver failure can be clinically manifest with the same symptoms and signs of CHF without its instrumental criteria. However, these conditions may coexist with CHF, exacerbating and influencing the disease’s onset and course ,25.Advances in various technology sectors have turned technological innovations into trustworthy solutions providing reliable personalized care to augment burdened healthcare systems ,8. Thesen of CHF .Wearable, portable, or implantable devices can be adopted to measure the parameters of interest; however, owing to their unobtrusive and comfortable nature, wearable devices are the most popular contribution to the definition of the Internet of Medical Things (IoMT) ,27. SomeIdentifying risk conditions by predicting health trends and acute events, thus enabling early warning and the early administration of treatment solutions;Providing personalized risk stratification, targeted therapies, and treatment solutions;Analysing chronic disease trajectories and response to administered therapies to provide recommendations for therapy adjustments;Optimizing hospital administration and scheduling systems;Optimizing surgical procedures;Optimizing pharmacological interventions;Improving doctor and patient communication.AI and ML technologies also improve patient care and the overall efficiency of cardiovascular medicine. According to several sources, AI and ML contribute to reducing the burden on medical practitioners through algorithms that perform various tasks like ,11,28,35Due to the increased adoption of digital technologies in healthcare, various works have been published highlighting applied technologies; their impact on disease diagnosis, management, and treatment; and legislation implemented to regulate their use in clinical practices. Several studies on the diverse aspects of digital healthcare in cardiovascular medicine have been conducted to provide a picture of the current state of research, identify gaps, and future directions and potential solutions.For instance, Tarakji et al.  provide Miller in  reviewedPotential heart conditions are predominantly detected using ECG signals, which reliably provide helpful information on heart function and heart health ,28. As aAs mentioned earlier, in The studies summarized in this section reveal that the contributions of edge AI solutions in wearable architectures for CHF management and prevention are not adequately presented and highlighted. Therefore, this work collates and evaluates published research demonstrating architectures that leverage wearable devices and edge AI solutions for CHF management and diagnosis. The works selected for this discussion principally focus on the AI and ML applied on constrained devices at the extreme edge, i.e., the wearable devices themselves or smaller devices nearby. This study also aims to identify technology evolutions and provide a reference point for future developments, and it suggests potential research directions from a clinical and technology perspective.The method adopted in this study is a “systematic literature review” ,41,42, wWe first defined some research questions and expected goals as listed below to guide the search criteria and delineate the direction of the review. The questions are designed to guide the progression of the research. They facilitate extracting the most relevant information to highlight research gaps. The questions also provide insights into the critical components of the selected research field. This is the most crucial step of a systematic literature review.RQ1:What role does edge AI play in wearable healthcare architectures for CHF management?This question aims to determine the purpose for which edge AI is implemented in wearables used in CHF-prevention and -management architectures.RQ2:How is edge intelligence applied in wearable-based intelligent healthcare architectures supporting CHF diagnosis, prevention, and management?This question provides an overview of existing edge AI methods and techniques that have been thus far adopted for CHF management, diagnosis, and prevention frameworks.RQ3:How can edge AI contribute to developing interactive patient-centric solutions?This question demonstrates how users interact with intelligent systems containing wearables and edge AI technologies and the user services they offer.RQ4:How do wearables and edge AI technologies affect the role of medical practitioners in chronic heart failure patient treatment and management?This question highlights the clinical significance of wearable and edge AI technologies in technologies applied to the CHF context. This question also aims to determine if existing works provide solutions to empirically quantify the contribution of the implemented edge intelligence solutions to the overall CHF-prevention, -management, or -diagnosis technology solutions. The goal is to provide insight into how the effect of these innovative technologies on patients and medical practitioners is evaluated and quantified.To obtain the scientific sources used to perform this study, we selected multidisciplinary and domain-specific scholarly and scientific databases in line with the context of our research. We selected Scopus and Web of Science (WOS), the largest multidisciplinary scientific databases , as the Wearable sensing devices;Edge AI;Chronic heart failure.This study focuses on wearable sensors used in conjunction with edge AI in technology architectures applied to a chronic heart failure use case. Therefore, the principal elements of the study are:We, therefore, defined a query (Listing 1) that includes synonyms of the main keywords to guarantee an exhaustive search intercepting all the articles relevant to our study.Listing 1. Search Query.(Edge OR on-device OR distributed OR embedded OR constrained OR tiny ORFOG OR Mist) AND  )AND (``wear *’’ OR worn) AND( (``cardi*’’ OR ``heart*’’ OR ``heart failure’’ OR ``chronicheart failure’’) OR (``hospit*’’))As summarized in In total, 565 articles were retained for further analysis after the first duplicate elimination iteration. Due to minor punctuation differences in paper titles in different databases, some duplicates were not identified using the duplicate removal method used in the previous step. Another elimination method that considers punctuation differences was implemented, and 107 duplicate articles were discarded, leaving a total of 458 articles. The typical systematic review screening procedure first employs title and abstract screening to identify relevant sources ,46; therThis section highlights the main concepts related to the edge AI-powered wearable technologies used for CHF prevention, diagnosis, and management as presented in the articles selected for this study. The section provides a comprehensive discussion of articles obtained from the literature search described in Many publications emphasize the importance of wearable technologies to continuously monitor and manage patients diagnosed with cardiovascular diseases like CHF or to facilitate early detection and treatment. The literature we analyzed reflects that arrhythmia detection using ECG signals is the main focus of most wearable technologies implementing edge AI, while PPG signals are the second most popular sensor for arrhythmia detection in CHF-prevention frameworks. In some of the architectures presented, multiple types of arrhythmia are automatically identified and classified based on cardiac patterns known to represent their occurence. However, some applications are designed to perform the more basic binary task of detecting the presence or absence of specific types of arrhythmia.It is also estimated that about 50% of all deaths attributed to CHF are classified as sudden deaths, most of which are caused by ventricular arrhythmias . TherefoOne of the earlier arrhythmia-detection frameworks , along wSeveral arrhythmia detection applications, however, classify ECG signals into five classes, i.e., regular non-ectopic beats, supraventricular ectopic beats, VEBs, fusion beats, and unknown beats ,57,58,59Patients with reduced LVEF are sometimes equipped with wearable cardioverter defibrillators (WCDs) to detect arrhythmic events and provide an appropriate high-energy shock to restore normal sinus rhythm . WCDs arHuang et al.  instead Aside from monitoring arrhythmias, edge AI has also been applied to wearable-based BP measurements . In otheThe computational devices used in wearable electronics that measure useful biosignals and complementary signals have limited power, computing, and memory resources. Therefore, in most cases, the first step towards implementing AI algorithms on wearable devices or proximity-portable smart devices is designing large-scale end-to-end algorithms. The large-scale DL algorithms must then be transformed using model compression strategies to reduce the model size and obtain resource efficiency. In such cases, only offline inference is performed in wearable devices. Pruning, quantization, clustering, knowledge distillation, and low-rank approximation are typically used to compress models for constrained device implementation . The folSome edge AI solutions in the selected literature repurpose large neural network (NN) architectures designed for unconstrained computational devices ,53,61. MOther works define specialized model compression techniques to allow the implementation of NN in wearable devices. For instance, the authors of  explain In contrast to the algorithms developed for ECG signals, the work defined in  defined The authors of  combine Beyond identifying the presence or absence of abnormal heart rhythms, Ran et al.  define aIn addition to the quantization and pruning techniques, other methods used to improve resource efficiency and optimize AI models for constrained wearable devices include signal conversion ,51,64, aIncreasing the accuracy of complex-signal DL classification algorithms typically involves increasing the number of hidden layers in the NN architectures ,78. ThisAI configurations traditionally used exclusively for specific applications like the natural language processing (NLP) transformer architecture can be modified and adapted for biosignal analysis as depicted in . A fussiThe final algorithm modification technique employed to allow edge AI on wearable devices is presented in . This meOther AI algorithms are naturally suited for constrained device implementation due to their native resource efficiency configurations. The algorithms deployed in their original form presented in the literature analyzed in this work are summarized in Whereas preprocessing techniques like discrete wavelet transform (DWT) or similar R peak detection algorithms ,50,64,80The authors of  additionIn contrast to most of the work discussed in this section, the authors of  defined Other research focused on developing neural architecture search (NAS) methods to obtain DNN architectures suitable for processing biosignals in wearable devices. One example is the work in , where aAs mentioned earlier in this section, model-compression and device-optimization techniques can be performed through open-source tools and frameworks. These tools facilitate the automated generation of hardware-specific optimizations for efficient on-device inference. Some tools adopted in the works analyzed here are presented in From the articles, we analyzed in this review, the MIT-BIH  arrhythmThe following table summarizes the databases used in the works analyzed in this study. The table shows that the implementation of edge AI is inherently skewed towards ECG analysis, as depicted by the recurrence of the MIT-BIH in most entries. As mentioned in Creating datasets on ill patients is a time-consuming endeavour, as described in , and is Efficient user experience is crucial to the success and acceptance of any activity of daily life or wearable technology. As such, communication with stakeholders is paramount to assessing efficiency and impact. However, most of the works discussed here focus on developing devices and algorithms and do not mention the service delivery and interaction with patients or medical practitioners.Devices that are easy to use.Instruments that facilitate efficient communication between doctor and patient, i.e., easy-to-read data visualization instruments.Frameworks with combined biosensors and patient medical history.The work presented in  providesIn , system Interactions between patients and physicians are barely mentioned in the wearable-based architectures discussed in the study. Investigating extensive future developments focusing on the impact of combining the power of wearables and AI on patient–physician interactions would significantly contribute to obtaining a more substantial answer to RQ3 and RQ4.This section summarizes the primary outcomes and lessons learned from this review by highlighting the central answers to the established research questions. In addition, it sheds some light on remaining open issues and discusses the potential research opportunities emerging from the review.The burden placed on healthcare infrastructures due to the cyclic and frequent hospitalization of CHF patients has increased significantly over the last few years ,25. WithObtaining a complete assessment of CHF patients that compares to clinical practices requires monitoring multiple parameters, including respiration and oxygen saturation, which provide doctors with information about fatigue that is an important factor for determining patients’ New York Heart Association (NHYA) class. The NYHA class allows doctors to determine the severity of CHF in an individual as well as the individual’s risk of death . AdoptinBased on the observations made in The discussion in Although the application of technology to prevent CHF and manage diagnosed patients is being tackled from many fronts, practical demonstrators and real-life testing remains challenging. In most cases, the development of edge AI in wearable devices is only mentioned as a future development, creating a significant divide between conceptualization and implementation. Therefore, a potential research direction would be performing dedicated pilot studies including patients, clinicians, and technology experts to propose solutions acceptable to all stakeholders.Another important lesson from this study regards collaboration among technology innovators and medical professionals. Most of the works we analyzed only provide the technological point of view. As a result, most of these discussed solutions are not adopted by clinicians. Collaboration among technology experts and clinicians can facilitate research that produces fascinating outcomes and practical solutions that both communities could appreciate.However, the absence of collaboration is also evident among stakeholders of different domains and among experts in the same field. We noticed a divide among the works focused on the hardware and the software components. The software experts, in fact, mainly concentrate on developing dedicated frameworks with complete pipelines and interconnection through dedicated APIs. In contrast, hardware experts usually focus on hardware optimization, performance evaluation, etc. This division, unfortunately, sometimes leads to incomplete products and influences the users’ acceptance of innovative solutions. Therefore, future research directions should account for collaboration, not only among stakeholders operating in different domains but also among experts of the same area operating on different aspects .Finally, based on our observations, we believe a comprehensive solution that leverages edge intelligence, IoT wearable sensors and user interfaces to provide a digital twin with two intercommunicating platforms could be an efficient CHF management solution. The two components of the digital twin would include (i.) a patient-centred digital twin based on virtual assistance solutions to support the patient and receive helpful feedback to improve predictions and treatment plans, and (ii.) a physician-centred digital twin solution to support medical professionals in their daily activities and help them to enhance the patients’ overall well-being, thus improving the efficacy of care and patient management. Such a solution must integrate edge AI technologies in wearables to provide distributed data elaboration and reduce the IoT data storage dilemma. However, cloud computing is still essential to developing complete infrastructures due to available technologies and scalability requirements.Various innovative technologies have been proposed and applied to provide technology solutions contributing to CHF diagnosis, management, and prediction, reducing mortality and improving patient care. However, most available solutions leverage the cloud computing paradigm that is unfortunately affected by communication latency, privacy issues, etc. Despite the existence of several distributed computing scenarios, cloud computing still has an integral role in many application solutions. However, as IoT devices increasingly produce a large number of data, the need for applications implementing computing solutions based on reliable distributed intelligence hastens. We hypothesize that implementing AI in wearable devices can significantly contribute to the widespread use of decentralized computing. Fortunately, the adoption of wearable-based edge intelligence for CHF diagnosis management and prediction has been the subject of many research endeavours, as demonstrated in this study. However, to the best of our knowledge, the literature lacks a comprehensive analysis evaluating research about wearable solutions implementing edge AI to prevent and manage CHF.Therefore, this study presented a systematic literature review discussing 41 articles related to edge AI techniques applied to wearable-based chronic heart-failure-prevention and -management infrastructures. A staggering 80% of the articles were published within the last two years, demonstrating growing interest in the subject.Approximately 46% of the total number of articles analyzed in this survey describe the use of varied optimization techniques to enable the implementation of AI algorithms on resource-constrained wearable devices. In total, 68% of those articles highlight the application of model compression techniques on large-scale algorithms. The remaining 32% describe algorithms modified for constrained devices through signal conversion or applying domain knowledge-based modifications to the algorithms. Finally, only about 14% of the analyzed articles presented algorithms deployed without changes. Due to these observations, it can be concluded that efficient optimization techniques are essential for deploying AI algorithms in wearable devices. Only four articles we analyzed adopted tools to perform hardware-specific optimizations on predefined models automatically. Therefore, further investigation is required to determine why. The MIT-BIH dataset is used in over 50% of the articles that provided information about the datasets used. Therefore, also, in this case, further investigation is required to determine the reason for this considering the availability of datasets containing heart failure patient physiological parameters.Several existing open issues, as discussed in In conclusion, artificial intelligence is only at the beginning of its application in the medical field, and its potential is barely perceived. The strength of wearable technologies in medicine lies in their low impact on the patient’s daily routine, which significantly improves healthcare. Equipping patients with intelligent wearable technologies also gives them a central role to play in their clinical assessment and makes them active in the therapeutic process, thus improving their chances of positive outcomes.