Personalising intravenous to oral antibiotic switch decision making through fair interpretable machine learning.

Antimicrobial resistance (AMR) and healthcare associated infections pose a significant threat globally. One key prevention strategy is to follow antimicrobial stewardship practices, in particular, to maximise targeted oral therapy and reduce the use of indwelling vascular devices for intravenous (IV) administration. Appreciating when an individual patient can switch from IV to oral antibiotic treatment is often non-trivial and not standardised. To tackle this problem we created a machine learning model to predict when a patient could switch based on routinely collected clinical parameters. 10,362 unique intensive care unit stays were extracted and two informative feature sets identified. Our best model achieved a mean AUROC of 0.80 (SD 0.01) on the hold-out set while not being biased to individuals protected characteristics. Interpretability methodologies were employed to create clinically useful visual explanations. In summary, our model provides individualised, fair, and interpretable predictions for when a patient could switch from IV-to-oral antibiotic treatment. Prospectively evaluation of safety and efficacy is needed before such technology can be applied clinically.

A low-volume LC/MS method for highly sensitive monitoring of phenoxymethylpenicillin, benzylpenicillin, and probenecid in human serum.

Background: The optimization of antimicrobial dosing plays a crucial role in improving the likelihood of achieving therapeutic success while reducing the risks associated with toxicity and antimicrobial resistance. Probenecid has shown significant potential in enhancing the serum exposure of phenoxymethylpenicillin, thereby allowing for lower doses of phenoxymethylpenicillin to achieve similar pharmacokinetic/pharmacodynamic (PK/PD) targets. We developed a triple quadrupole liquid chromatography mass spectrometry (TQ LC/MS) analysis of, phenoxymethylpenicillin, benzylpenicillin and probenecid using benzylpenicillin-d7 and probenecid-d14 as IS in single low-volumes of human serum, with improved limit of quantification to support therapeutic drug monitoring. Methods: Sample clean-up was performed by protein precipitation using acetonitrile. Reverse phase chromatography was performed using TQ LC/MS. The mobile phase consisted of 55% methanol in water + 0.1% formic acid, with a flow rate of 0.4 mL min-1. Antibiotic stability was assessed at different temperatures. Results: Chromatographic separation was achieved within 2 minutes, allowing simultaneous measurement of phenoxymethylpenicillin, benzylpenicillin and probenecid in a single 15 µL blood sample. Validation indicated linearity over the range 0.0015-10 mg L-1, with accuracy of 96-102% and a LLOQ of 0.01 mg L-1. All drugs demonstrated good stability under different storage conditions. Conclusion: The developed method is simple, rapid, accurate and clinically applicable for the quantification of phenoxymethylpenicillin, benzylpenicillin and probenecid in tandem.

Mentorship as an overlooked dimension of research capacity strengthening: how to embed value-driven practices in global health.

Mentorship in global health remains an overlooked dimension of research partnerships. Commitment to effective mentorship models requires value-driven approaches. This includes having an understanding of (1) what mentorship means across different cultural and hierarchical boundaries in the health research environment, and (2) addressing entrenched power asymmetries across different aspects including funding, leadership, data and outputs, and capacity strengthening. Existing guidance towards equity and sustainability fails to inform how to navigate complex relationships which hinder effective mentorship models. We focus this perspective piece on human capacity strengthening in research partnerships through mentorship. Using a case study of a research partnership, we describe the lessons learnt and the challenges faced in the mentor mentee relationship while maintaining an effective and sustainable partnership. Human capacity strengthening must research projects and collaborations, and recognise local leadership and ownership. To be transformative and effective, practices need to be driven by common values across research teams.

Integrated analysis of patient networks and plasmid genomes reveals a regional, multi-species outbreak of carbapenemase-producing Enterobacterales carrying both blaIMP and mcr-9 genes.

BACKGROUND: Carbapenemase-producing Enterobacterales (CPE) are challenging in healthcare, with resistance to multiple classes of antibiotics. This study describes the emergence of IMP-encoding CPE amongst diverse Enterobacterales species between 2016 and 2019 across a London regional network. METHODS: We performed a network analysis of patient pathways, using electronic health records, to identify contacts between IMP-encoding CPE positive patients. Genomes of IMP-encoding CPE isolates were overlayed with patient contacts to imply potential transmission events. RESULTS: Genomic analysis of 84 Enterobacterales isolates revealed diverse species (predominantly Klebsiella spp, Enterobacter spp, E. coli); 86% (72/84) harboured an IncHI2 plasmid carrying blaIMP and colistin resistance gene mcr-9 (68/72). Phylogenetic analysis of IncHI2 plasmids identified three lineages showing significant association with patient contacts and movements between four hospital sites and across medical specialities, which was missed on initial investigations. CONCLUSIONS: Combined, our patient network and plasmid analyses demonstrate an interspecies, plasmid-mediated outbreak of blaIMPCPE, which remained unidentified during standard investigations. With DNA sequencing and multi-modal data incorporation, the outbreak investigation approach proposed here provides a framework for real-time identification of key factors causing pathogen spread. Plasmid-level outbreak analysis reveals that resistance spread may be wider than suspected, allowing more interventions to stop transmission within hospital networks.

Establishing the core elements of a frailty at the front door model of care using a modified real-time Delphi technique.

BACKGROUND: Innovations in models of care for older adults living with frailty presenting to the emergency department (ED) have become a key priority for clinicians, researchers and policymakers due to the deleterious outcomes older adults experience due to prolonged exposure to such an environment. This study aimed to develop a set of expert consensus-based statements underpinning operational design, outcome measurement and evaluation of a Frailty at the Front Door (FFD) model of care for older adults within an Irish context. METHODS: A modified real-time Delphi method was used. Facilitation of World Café focus groups with an expert panel of 86 members and seperate advisory groups with a Public and Patient Involvement panel of older adults and members of the Irish Association of Emergency Medicine generated a series of statements on the core elements of the FFD model of care. Statements were analysed thematically and incorporated into a real-time Delphi survey, which was emailed to members of the expert panel. Members were asked to rank 70 statements across nine domains using a 9-point Likert scale. Consensus criteria were defined a priori and guided by previous research using 9-point rating scales. RESULTS: Fifty members responded to the survey representing an overall response rate of 58%. Following analyses of the survey responses, the research team reviewed statements for content overlap and refined a final list of statements across the following domains: aims and objectives of the FFD model of care; target population; screening and assessment; interventions; technology; integration of care; evaluation and metrics; and research. CONCLUSION: Development of a consensus derived FFD model of care represents an important step in generating national standards, implementation of a service model as intended and enhances opportunities for scientific impact. Future research should focus on the development of a core outcome set for studies involving older adults in the ED.

A Physiotherapy-Led Transition to Home Intervention for Older Adults Following Emergency Department Discharge: A Pilot Feasibility Randomised Controlled Trial (ED PLUS).

Background: Older adults frequently attend the emergency department (ED) and experience high rates of subsequent adverse outcomes including functional decline, ED re-presentation and unplanned hospital admission. The development of effective interventions to prevent such outcomes is a key priority for research and service provision. Our aim was to evaluate the feasibility of a physiotherapy-led integrated care intervention for older adults discharged from the ED (ED PLUS). Patients and Methods: Older adults presenting to the ED of a university teaching hospital with undifferentiated medical complaints and discharged within 72 hours were computer randomised in a ratio of 1:1:1 to deliver usual care, Comprehensive Geriatric Assessment (CGA) in the ED, or ED PLUS. ED PLUS is an evidence-based and stakeholder-informed intervention to bridge the care transition between the ED and community by initiating a CGA in the ED and implementing a six-week, multi-component, self-management programme in the patient's home. Feasibility and acceptability were assessed quantitatively and qualitatively. All clinical and process outcomes were assessed by a research nurse blinded to group allocation. Data analyses were primarily descriptive. Results: Twenty-nine participants were recruited indicating a 67% recruitment rate. At 6 months, there was 100% retention in the usual care group, 88% in the CGA group and 90% in the ED PLUS group. ED PLUS participants expressed positive feedback, and there was a trend towards improved function and quality of life and less ED revisits and unscheduled hospitalisations in the ED PLUS group. Conclusion: ED PLUS bridges the transition of care between the index visit to the ED and the community and is feasible using systematic recruitment strategies. Despite recruitment challenges in the context of COVID-19, the intervention was successfully delivered and well received by participants. There was a lower incidence of functional decline and improved quality of life in the ED PLUS group. Trial Registration: The trial was registered in Clinical Trials Protocols and Results System as of 21st July 2021, with registration number NCT04983602.

Smart-Plexer: a breakthrough workflow for hybrid development of multiplex PCR assays.

Developing multiplex PCR assays requires extensive experimental testing, the number of which exponentially increases by the number of multiplexed targets. Dedicated efforts must be devoted to the design of optimal multiplex assays ensuring specific and sensitive identification of multiple analytes in a single well reaction. Inspired by data-driven approaches, we reinvent the process of developing and designing multiplex assays using a hybrid, simple workflow, named Smart-Plexer, which couples empirical testing of singleplex assays and computer simulation to develop optimised multiplex combinations. The Smart-Plexer analyses kinetic inter-target distances between amplification curves to generate optimal multiplex PCR primer sets for accurate multi-pathogen identification. In this study, the Smart-Plexer method is applied and evaluated for seven respiratory infection target detection using an optimised multiplexed PCR assay. Single-channel multiplex assays, together with the recently published data-driven methodology, Amplification Curve Analysis (ACA), were demonstrated to be capable of classifying the presence of desired targets in a single test for seven common respiratory infection pathogens.

Analytical and diagnostic performance characteristics of reverse-transcriptase loop-mediated isothermal amplification assays for dengue virus serotypes 1-4: A scoping review to inform potential use in portable molecular diagnostic devices.

Dengue is a mosquito-borne disease caused by dengue virus (DENV) serotypes 1-4 which affects 100-400 million adults and children each year. Reverse-transcriptase (RT) quantitative polymerase chain reaction (qPCR) assays are the current gold-standard in diagnosis and serotyping of infections, but their use in low-middle income countries (LMICs) has been limited by laboratory infrastructure requirements. Loop-mediated isothermal amplification (LAMP) assays do not require thermocycling equipment and therefore could potentially be deployed outside laboratories and/or miniaturised. This scoping literature review aimed to describe the analytical and diagnostic performance characteristics of previously developed serotype-specific dengue RT-LAMP assays and evaluate potential for use in portable molecular diagnostic devices. A literature search in Medline was conducted. Studies were included if they were listed before 4th May 2022 (no prior time limit set) and described the development of any serotype-specific DENV RT-LAMP assay ('original assays') or described the further evaluation, adaption or implementation of these assays. Technical features, analytical and diagnostic performance characteristics were collected for each assay. Eight original assays were identified. These were heterogenous in design and reporting. Assays' lower limit of detection (LLOD) and linear range of quantification were comparable to RT-qPCR (with lowest reported values 2.2x101 and 1.98x102 copies/ml, respectively, for studies which quantified target RNA copies) and analytical specificity was high. When evaluated, diagnostic performance was also high, though reference diagnostic criteria varied widely, prohibiting comparison between assays. Fourteen studies using previously described assays were identified, including those where reagents were lyophilised or 'printed' into microfluidic channels and where several novel detection methods were used. Serotype-specific DENV RT-LAMP assays are high-performing and have potential to be used in portable molecular diagnostic devices if they can be integrated with sample extraction and detection methods. Standardised reporting of assay validation and diagnostic accuracy studies would be beneficial.

Applying artificial intelligence and digital health technologies, Viet Nam.

Problem: Direct application of digital health technologies from high-income settings to low- and middle-income countries may be inappropriate due to challenges around data availability, implementation and regulation. Hence different approaches are needed. Approach: Within the Viet Nam ICU Translational Applications Laboratory project, since 2018 we have been developing a wearable device for individual patient monitoring and a clinical assessment tool to improve dengue disease management. Working closely with local staff at the Hospital for Tropical Diseases, Ho Chi Minh City, we developed and tested a prototype of the wearable device. We obtained perspectives on design and use of the sensor from patients. To develop the assessment tool, we used existing research data sets, mapped workflows and clinical priorities, interviewed stakeholders and held workshops with hospital staff. Local setting: In Viet Nam, a lower middle-income country, the health-care system is in the nascent stage of implementing digital health technologies. Relevant changes: Based on patient feedback, we are altering the design of the wearable sensor to increase comfort. We built the user interface of the assessment tool based on the core functionalities selected by workshop attendees. The interface was subsequently tested for usability in an iterative manner by the clinical staff members. Lessons learnt: The development and implementation of digital health technologies need an interoperable and appropriate plan for data management including collection, sharing and integration. Engagements and implementation studies should be conceptualized and conducted alongside the digital health technology development. The priorities of end-users, and understanding context and regulatory landscape are crucial for success.

A survey of patient and public perceptions and awareness of SARS-CoV-2-related risks among participants in India and South Africa.

A cross-sectional survey among participants in India and South Africa to explore perceptions and awareness of SARS-CoV-2-related risks. Main outcome measures-proportion of participants aware of SARS-CoV-2, and their perception of infection risks as it related to their views and perceptions on vaccination, i.e., using COVID-19 vaccine uptake as proxy for awareness level. Self-administered questionnaires were used to collect data via web- and paper-based surveys over three months. Pearson's Chi-squared test assessed relationships between variables; a p-value less than 0.05 was considered significant. There were 844 respondents (India: n = 660, South Africa: n = 184; response rate 87.6%), with a 61.1% vs 38.3% female to male ratio. Post-high-school or university education was the lowest qualification reported by most respondents in India (77.3%) and South Africa (79.3%). Sources of pandemic information were usually media and journal publications (73.2%), social media (64.6%), family and friends (47.7%) and government websites (46.2%). Most respondents correctly identified infection prevention measures (such as physical distancing, mask use), with 90.0% reporting improved hand hygiene practices since the pandemic. Hesitancy or refusal to accept the SARS-CoV-2 vaccine was reported among 17.9% and 50.9% of respondents in India and South Africa, respectively; reasons cited included rushed vaccine development and the futility of vaccines for what respondents considered a self-limiting flu-like illness. In South Africa, vaccine acceptance was associated with improved hand hygiene practices since the pandemic and flu vaccination in the preceding year. No relationship was noted between awareness and practice of infection prevention measures (such as hand hygiene) and socio-demographic factors such as employment status or availability of amenities. Pandemic response and infection prevention and control measures through vaccination campaigns should consider robust public engagement and contextually-fit communication strategies with multimodal, participatory online and offline initiatives to address public concerns, specifically towards vaccines developed for this pandemic and general vaccine hesitancy.

Impact of the COVID-19 pandemic on antimicrobial stewardship support for general practices in England: a qualitative interview study.

BACKGROUND: In England, clinical commissioning group (CCG; now replaced by Integrated Care Systems [ICSs]) and primary care network (PCN) professionals support primary care prescribers to optimise antimicrobial stewardship (AMS). AIM: To explore views and experiences of CCG and PCN staff in supporting AMS, and the impact of COVID-19 on this support. DESIGN & SETTING: Qualitative interview study in primary care in England. METHOD: Semi-structured interviews with staff from CCG and PCNs responsible for AMS were conducted at two timepoints via telephone. These were audio-recorded, transcribed, and analysed thematically. RESULTS: Twenty-seven interviews were conducted with 14 participants (nine CCG, five PCN) in December 2020-January 2021 and February-May 2021. The study found that AMS support was (1) deprioritised in order to keep general practice operational and deliver COVID-19 vaccines; (2) disrupted as social distancing made it harder to build relationships, conduct routine AMS activities, and challenge prescribing decisions; and (3) adapted, with opportunities identified for greater use of technology and changing patient and public perceptions of viruses and self-care. It was also found that resources to support AMS were valued if they were both novel, to counter AMS 'fatigue', and sufficiently familiar to fit with existing and/or future AMS. CONCLUSION: AMS needs to be reprioritised in general practice in the post-pandemic era and within the new ICSs in England. This should include interventions and strategies that combine novel elements with already familiar strategies to refresh prescribers' motivation and opportunities for AMS. Behaviour change interventions should be aimed at improving the culture and processes for how PCN pharmacists voice concerns about AMS to prescribers in general practice and take advantage of the changed patient and public perceptions of viruses and self-care.

A Multi-Site, Multi-Wavelength PPG Platform for Continuous Non-Invasive Health Monitoring in Hospital Settings.

This article presents a novel PPG acquisition platform capable of synchronous multi-wavelength signal acquisition from two measurement locations with up to 4 independent wavelengths from each in parallel. The platform is fully configurable and operates at 1ksps, accommodating a wide variety of transmitters and detectors to serve as both a research tool for experimentation and a clinical tool for disease monitoring. The sensing probes presented in this work acquire 4 PPG channels from the wrist and 4 PPG channels from the fingertip, with wavelengths such that surrogates for pulse wave velocity and haematocrit can be extracted. For conventional PPG sensing, we have achieved the mean error of 4.08 ± 3.72 bpm for heart-rate and a mean error of 1.54 ± 1.04% for SpO 2 measurement, with the latter lying within the FDA limits for commercial pulse oximeters. We have further evaluated over 700 individual peak-to-peak time differences between wrist and fingertip signals, achieving a normalized weighted average PWV of 5.80 ± 1.58 m/s, matching with values of PWV found for this age group in literature. Lastly, we introduced and computed a haematocrit ratio ( Rhct) between the deep IR and deep red wavelength from the fingertip sensor, finding a significant difference between male and female values (median of 1.9 and 2.93 respectively) pointing to devices sensitivity to Hct.

Towards optimizing carbapenem selection in stewardship strategies: a prospective propensity score-matched study of ertapenem versus class 2 carbapenems for empirical treatment of third-generation cephalosporin-resistant Enterobacterales bacteraemia.

BACKGROUND: Third-generation cephalosporin-resistant Enterobacterales (3GCRE) are increasing in prevalence, leading to greater carbapenem consumption. Selecting ertapenem has been proposed as a strategy to reduce carbapenem resistance development. However, there are limited data for the efficacy of empirical ertapenem for 3GCRE bacteraemia. OBJECTIVES: To compare the efficacy of empirical ertapenem and class 2 carbapenems for the treatment of 3GCRE bacteraemia. METHODS: A prospective non-inferiority observational cohort study was performed from May 2019 to December 2021. Adult patients with monomicrobial 3GCRE bacteraemia receiving carbapenems within 24 h were included at two hospitals in Thailand. Propensity scores were used to control for confounding, and sensitivity analyses were performed in several subgroups. The primary outcome was 30 day mortality. This study is registered with clinicaltrials.gov (NCT03925402). RESULTS: Empirical carbapenems were prescribed in 427/1032 (41%) patients with 3GCRE bacteraemia, of whom 221 received ertapenem and 206 received class 2 carbapenems. One-to-one propensity score matching resulted in 94 pairs. Escherichia coli was identified in 151 (80%) of cases. All patients had underlying comorbidities. Septic shock and respiratory failure were the presenting syndromes in 46 (24%) and 33 (18%) patients, respectively. The overall 30 day mortality rate was 26/188 (13.8%). Ertapenem was non-inferior to class 2 carbapenems in 30 day mortality (12.8% versus 14.9%; mean difference -0.02; 95% CI: -0.12 to 0.08). Sensitivity analyses were consistent regardless of aetiological pathogens, septic shock, source of infection, nosocomial acquisition, lactate levels or albumin levels. CONCLUSIONS: Ertapenem may be of comparable efficacy to class 2 carbapenems in the empirical treatment of 3GCRE bacteraemia.

Can precision antibiotic prescribing help prevent the spread of carbapenem-resistant organisms in the hospital setting?

The emergence of carbapenem-resistant organisms (CROs) is a significant global threat. Reduction of carbapenem consumption can decrease CROs. In the global endemic era of ESBL-producing bacteria, carbapenems are considered the treatment of choice, leading to challenge in limiting carbapenem use. This review describes the role of precision prescribing for prevention of CROs. This involves improving antibiotic selection, dosing and shortening duration. The effect of different antibiotics, dosing and duration on CRO development are explored. Available options for precision prescribing, gaps in the scientific evidence, and areas for future research are also presented.

Deep Domain Adaptation Enhances Amplification Curve Analysis for Single-Channel Multiplexing in Real-Time PCR.

Data-driven approaches for molecular diagnostics are emerging as an alternative to perform an accurate and inexpensive multi-pathogen detection. A novel technique called Amplification Curve Analysis (ACA) has been recently developed by coupling machine learning and real-time Polymerase Chain Reaction (qPCR) to enable the simultaneous detection of multiple targets in a single reaction well. However, target classification purely relying on the amplification curve shapes faces several challenges, such as distribution discrepancies between different data sources (i.e., training vs testing). Optimisation of computational models is required to achieve higher performance of ACA classification in multiplex qPCR through the reduction of those discrepancies. Here, we proposed a novel transformer-based conditional domain adversarial network (T-CDAN) to eliminate data distribution differences between the source domain (synthetic DNA data) and the target domain (clinical isolate data). The labelled training data from the source domain and unlabelled testing data from the target domain are fed into the T-CDAN, which learns both domains' information simultaneously. After mapping the inputs into a domain-irrelevant space, T-CDAN removes the feature distribution differences and provides a clearer decision boundary for the classifier, resulting in a more accurate pathogen identification. Evaluation of 198 clinical isolates containing three types of carbapenem-resistant genes (blaNDM, blaIMP and blaOXA-48) illustrates a curve-level accuracy of 93.1% and a sample-level accuracy of 97.0% using T-CDAN, showing an accuracy improvement of 20.9% and 4.9% respectively. This research emphasises the importance of deep domain adaptation to enable high-level multiplexing in a single qPCR reaction, providing a solid approach to extend qPCR instruments' capabilities in real-world clinical applications.

Learning meaningful latent space representations for patient risk stratification: Model development and validation for dengue and other acute febrile illness.

Background: Increased data availability has prompted the creation of clinical decision support systems. These systems utilise clinical information to enhance health care provision, both to predict the likelihood of specific clinical outcomes or evaluate the risk of further complications. However, their adoption remains low due to concerns regarding the quality of recommendations, and a lack of clarity on how results are best obtained and presented. Methods: We used autoencoders capable of reducing the dimensionality of complex datasets in order to produce a 2D representation denoted as latent space to support understanding of complex clinical data. In this output, meaningful representations of individual patient profiles are spatially mapped in an unsupervised manner according to their input clinical parameters. This technique was then applied to a large real-world clinical dataset of over 12,000 patients with an illness compatible with dengue infection in Ho Chi Minh City, Vietnam between 1999 and 2021. Dengue is a systemic viral disease which exerts significant health and economic burden worldwide, and up to 5% of hospitalised patients develop life-threatening complications. Results: The latent space produced by the selected autoencoder aligns with established clinical characteristics exhibited by patients with dengue infection, as well as features of disease progression. Similar clinical phenotypes are represented close to each other in the latent space and clustered according to outcomes broadly described by the World Health Organisation dengue guidelines. Balancing distance metrics and density metrics produced results covering most of the latent space, and improved visualisation whilst preserving utility, with similar patients grouped closer together. In this case, this balance is achieved by using the sigmoid activation function and one hidden layer with three neurons, in addition to the latent dimension layer, which produces the output (Pearson, 0.840; Spearman, 0.830; Procrustes, 0.301; GMM 0.321). Conclusion: This study demonstrates that when adequately configured, autoencoders can produce two-dimensional representations of a complex dataset that conserve the distance relationship between points. The output visualisation groups patients with clinically relevant features closely together and inherently supports user interpretability. Work is underway to incorporate these findings into an electronic clinical decision support system to guide individual patient management.

Continuous Measurement of Lactate Concentration in Human Subjects through Direct Electron Transfer from Enzymes to Microneedle Electrodes.

Microneedle lactate sensors may be used to continuously measure lactate concentration in the interstitial fluid in a minimally invasive and pain-free manner. First- and second-generation enzymatic sensors produce a redox-active product that is electrochemically sensed at the electrode surface. Direct electron transfer enzymes produce electrons directly as the product of enzymatic action; in this study, a direct electron transfer enzyme specific to lactate has been immobilized onto a microneedle surface to create lactate-sensing devices that function at low applied voltages (0.2 V). These devices have been validated in a small study of human volunteers; lactate concentrations were raised and lowered through physical exercise and subsequent rest. Lactazyme microneedle devices show good agreement with concurrently obtained and analyzed serum lactate levels.

Next-generation molecular diagnostics: Leveraging digital technologies to enhance multiplexing in real-time PCR.

Real-time polymerase chain reaction (qPCR) enables accurate detection and quantification of nucleic acids and has become a fundamental tool in biological sciences, bioengineering and medicine. By combining multiple primer sets in one reaction, it is possible to detect several DNA or RNA targets simultaneously, a process called multiplex PCR (mPCR) which is key to attaining optimal throughput, cost-effectiveness and efficiency in molecular diagnostics, particularly in infectious diseases. Multiple solutions have been devised to increase multiplexing in qPCR, including single-well techniques, using target-specific fluorescent oligonucleotide probes, and spatial multiplexing, where segregation of the sample enables parallel amplification of multiple targets. However, these solutions are mostly limited to three or four targets, or highly sophisticated and expensive instrumentation. There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for a next generation of diagnostic tools which could accommodate high throughput in an affordable manner. To this end, the use of machine learning (ML) algorithms (data-driven solutions) has recently emerged to leverage information contained in amplification and melting curves (AC and MC, respectively) - two of the most standard bio-signals emitted during qPCR - for accurate classification of multiple nucleic acid targets in a single reaction. Therefore, this review aims to demonstrate and illustrate that data-driven solutions can be successfully coupled with state-of-the-art and common qPCR platforms using a variety of amplification chemistries to enhance multiplexing in qPCR. Further, because both ACs and MCs can be predicted from sequence data using thermodynamic databases, it has also become possible to use computer simulation to rationalize and optimize the design of mPCR assays where target detection is supported by data-driven technologies. Thus, this review also discusses recent work converging towards the development of an end-to-end framework where knowledge-based and data-driven software solutions are integrated to streamline assay design, and increase the accuracy of target detection and quantification in the multiplex setting. We envision that concerted efforts by academic and industry scientists will help advance these technologies, to a point where they become mature and robust enough to bring about major improvements in the detection of nucleic acids across many fields.