Electronic clinical decision support algorithms incorporating point-of-care diagnostic tests in low-resource settings: a target product profile

Co-creation process of an intervention to implement a multiparameter point-of-care testing device in a primary healthcare setting for non-communicable diseases in Peru

BACKGROUND

Point-of-care testing (POCT) devices are diagnostic tools that can provide quick and accurate results within minutes, making them suitable for diagnosing non-communicable diseases (NCDs). However, these devices are not widely implemented in healthcare systems and for this reason is relevant to understand the implementation process.

AIM

To describe the process and define a strategy to implement a multiparameter POCT device for diagnosing and managing NCDs in one region of Peru.

METHODS

A descriptive and non-experimental study, using the participatory methodologies of co-creation process. It was conducted in one region of Peru (Tumbes) to design an intervention for implementing a multiparameter POCT device. Two co-creation sessions were conducted involving five groups: community members, primary healthcare workers, these groups in both rural and urban settings, and regional decision-makers. These sessions included activities to understand patient journeys in receiving care for NCDs, identify facilitators and barriers to POCT devices usage, and define an implementation strategy for POCT devices in both rural and urban settings of Tumbes. The research team analysed the data and summarized key topics for discussion after each session.

RESULTS

A total of 78 participants were enrolled across the five groups. Among community members: 22.2% had only diabetes, 24.1% had only hypertension, and 18.5% had both diagnoses. In the patient journey, community members mentioned that it took at least three days to receive a diagnosis and treatment for an NCD. Most of the participants agreed that the POCT devices would be beneficial for their communities, but they also identified some concerns. The strategy for POCT devices implementation included healthcare workers training, POCT devices must be placed in the laboratory area and must be able to perform tests for glucose, glycated haemoglobin, cholesterol, and creatinine. Advertising about POCT devices should be displayed at the healthcare centres and the municipality using billboards and flyers.

CONCLUSIONS

The co-creation process was useful to develop strategies for the implementation of multiparameter POCT devices for NCDs, involving the participation of different groups of stakeholders guided by moderators in both, rural and urban, settings in Peru.

Target Product Profile for a Machine Learning-Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study

BACKGROUND

Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation.

OBJECTIVE

This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England.

METHODS

This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence's Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from "definitely exclude" to "definitely include," and suggest edits. The document will be iterated between rounds based on participants' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote.

RESULTS

Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024.

CONCLUSIONS

The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50568.

Development and Implementation of Digital Diagnostic Algorithms for Neonatal Units in Zimbabwe and Malawi: Development and Usability Study

BACKGROUND

Despite an increase in hospital-based deliveries, neonatal mortality remains high in low-resource settings. Due to limited laboratory diagnostics, there is significant reliance on clinical findings to inform diagnoses. Accurate, evidence-based identification and management of neonatal conditions could improve outcomes by standardizing care. This could be achieved through digital clinical decision support (CDS) tools. Neotree is a digital, quality improvement platform that incorporates CDS, aiming to improve neonatal care in low-resource health care facilities. Before this study, first-phase CDS development included developing and implementing neonatal resuscitation algorithms, creating initial versions of CDS to address a range of neonatal conditions, and a Delphi study to review key algorithms.

OBJECTIVE

This second-phase study aims to codevelop and implement neonatal digital CDS algorithms in Malawi and Zimbabwe.

METHODS

Overall, 11 diagnosis-specific web-based workshops with Zimbabwean, Malawian, and UK neonatal experts were conducted (August 2021 to April 2022) encompassing the following: (1) review of available evidence, (2) review of country-specific guidelines (Essential Medicines List and Standard Treatment Guidelinesfor Zimbabwe and Care of the Infant and Newborn, Malawi), and (3) identification of uncertainties within the literature for future studies. After agreement of clinical content, the algorithms were programmed into a test script, tested with the respective hospital's health care professionals (HCPs), and refined according to their feedback. Once finalized, the algorithms were programmed into the Neotree software and implemented at the tertiary-level implementation sites: Sally Mugabe Central Hospital in Zimbabwe and Kamuzu Central Hospital in Malawi, in December 2021 and May 2022, respectively. In Zimbabwe, usability was evaluated through 2 usability workshops and usability questionnaires: Post-Study System Usability Questionnaire (PSSUQ) and System Usability Scale (SUS).

RESULTS

Overall, 11 evidence-based diagnostic and management algorithms were tailored to local resource availability. These refined algorithms were then integrated into Neotree. Where national management guidelines differed, country-specific guidelines were created. In total, 9 HCPs attended the usability workshops and completed the SUS, among whom 8 (89%) completed the PSSUQ. Both usability scores (SUS mean score 75.8 out of 100 [higher score is better]; PSSUQ overall score 2.28 out of 7 [lower score is better]) demonstrated high usability of the CDS function but highlighted issues around technical complexity, which continue to be addressed iteratively.

CONCLUSIONS

This study describes the successful development and implementation of the only known neonatal CDS system, incorporated within a bedside data capture system with the ability to deliver up-to-date management guidelines, tailored to local resource availability. This study highlighted the importance of collaborative participatory design. Further implementation evaluation is planned to guide and inform the development of health system and program strategies to support newborn HCPs, with the ultimate goal of reducing preventable neonatal morbidity and mortality in low-resource settings.

Expert consensus on novel medicines to prevent preterm birth and manage preterm labour: Target product profiles

OBJECTIVE

To develop target product profiles (TPPs) for new medicines for preterm birth prevention and preterm labour management that address the real-world need of women and healthcare providers, informed by views and agreement amongst globally diverse stakeholders.

DESIGN

Mixed methods.

SETTING

Global (with a focus on low- and middle-income countries, LMICs).

SAMPLE

Global stakeholders with diverse expertise in preterm labour/birth and drug development.

METHODS

Following an initial literature review, diverse stakeholders were invited to participate in an online international survey and in-depth interviews. The level of stakeholder agreement with TPPs was assessed, and findings from interviews were synthesised to inform the final TPPs.

MAIN OUTCOMES MEASURES

Level of stakeholder agreement on the minimum and preferred requirements for preterm labour/birth medicines.

RESULTS

We performed 21 interviews. Interview participants demonstrated strong agreement on room temperature stability, no additional drug-specific clinical monitoring, and affordability in LMICs being the minimal acceptable requirements. Points of discussion were raised around the target population. Survey respondents included clinicians, researchers, funding agency staff, international public organisation staff, programme implementers, policymakers, representatives of consumer advocacy organisations and other relevant stakeholders from maternal health systems. Survey results indicated strong agreement amongst stakeholders, with only one variable in each TPP not reaching consensus (i.e. 25% disagree or strongly disagree).

CONCLUSIONS

There is strong consensus within the preterm labour/birth community on the characteristics that new medicines for preterm birth prevention and preterm labour management must achieve. These TPPs provide necessary guidance to evaluate new candidates and their potential for implementation in a range of settings.

A digital health algorithm to guide antibiotic prescription in pediatric outpatient care: a cluster randomized controlled trial

Excessive antibiotic use and antimicrobial resistance are major global public health threats. We developed ePOCT+, a digital clinical decision support algorithm in combination with C-reactive protein test, hemoglobin test, pulse oximeter and mentorship, to guide health-care providers in managing acutely sick children under 15 years old. To evaluate the impact of ePOCT+ compared to usual care, we conducted a cluster randomized controlled trial in Tanzanian primary care facilities. Over 11 months, 23,593 consultations were included from 20 ePOCT+ health facilities and 20,713 from 20 usual care facilities. The use of ePOCT+ in intervention facilities resulted in a reduction in the coprimary outcome of antibiotic prescription compared to usual care (23.2% versus 70.1%, adjusted difference -46.4%, 95% confidence interval (CI) -57.6 to -35.2). The coprimary outcome of day 7 clinical failure was noninferior in ePOCT+ facilities compared to usual care facilities (adjusted relative risk 0.97, 95% CI 0.85 to 1.10). There was no difference in the secondary safety outcomes of death and nonreferred secondary hospitalizations by day 7. Using ePOCT+ could help address the urgent problem of antimicrobial resistance by safely reducing antibiotic prescribing. Clinicaltrials.gov Identifier: NCT05144763.

Patient Stratification for Antibiotic Prescriptions Based on the Bound-Free Phase Detection Immunoassay of C-Reactive Protein in Serum Samples

C-reactive protein is a well-studied host response biomarker, whose diagnostic performance depends on its accurate classification into concentration zones defined by clinical scenario-specific cutoff values. We validated a newly developed, bead-based, bound-free phase detection immunoassay (BFPD-IA) versus a commercial CE-IVD enzyme-linked immunosorbent assay (ELISA) kit and a commercial CE-IVD immunoturbidimetric assay (ITA) kit. The latter was performed on a fully automated DPC Konelab 60i clinical analyzer used in routine diagnosis. We classified 53 samples into concentration zones derived from four different sets of cutoff values that are related to antibiotic prescription scenarios in the case of respiratory tract infections. The agreements between the methods were ELISA/ITA at 87.7%, ELISA/BFPD-IA at 87.3%, and ITA/-BFPD-IA at 93.9%, reaching 98-99% in all cases when considering the calculated relative combined uncertainty of the single measurement of each sample. In a subgroup of 37 samples, which were analyzed for absolute concentration quantification, the scatter plot slopes' correlations were as follows: ELISA/ITA 1.15, R2 = 0.97; BFPD-IA/ELISA 1.12, R2 = 0.95; BFPD-IA/ITA 0.95, R2 = 0.93. These very good performances and the agreement between BFPD-IA and ITA (routine diagnostic), combined with BFPD-IA's functional advantages over ITA (and ELISA)-such as quick time to result (~20 min), reduced consumed reagents (only one assay buffer and no washing), few and easy steps, and compatibility with nucleic-acid-amplification instruments-render it a potential approach for a reliable, cost-efficient, evidence-based point-of-care diagnostic test for guiding antibiotic prescriptions.

Digitalizing Clinical Guidelines: Experiences in the Development of Clinical Decision Support Algorithms for Management of Childhood Illness in Resource-Constrained Settings

Clinical decision support systems (CDSSs) can strengthen the quality of integrated management of childhood illness (IMCI) in resource-constrained settings. Several IMCI-related CDSSs have been developed and implemented in recent years. Yet, despite having a shared starting point, the IMCI-related CDSSs are markedly varied due to the need for interpretation when translating narrative guidelines into decision logic combined with considerations of context and design choices. Between October 2019 and April 2021, we conducted a comparative analysis of 4 IMCI-related CDSSs. The extent of adaptations to IMCI varied, but common themes emerged. Scope was extended to cover a broader range of conditions. Content was added or modified to enhance precision, align with new evidence, and support rational resource use. Structure was modified to increase efficiency, improve usability, and prioritize care for severely ill children. The multistakeholder development processes involved syntheses of recommendations from existing guidelines and literature; creation and validation of clinical algorithms; and iterative development, implementation, and evaluation. The common themes surrounding adaptations of IMCI guidance highlight the complexities of digitalizing evidence-based recommendations and reinforce the rationale for leveraging standards for CDSS development, such as the World Health Organization's SMART Guidelines. Implementation through multistakeholder dialogue is critical to ensure CDSSs can effectively and equitably improve quality of care for children in resource-constrained settings.

Research on digital tool in cognitive assessment: a bibliometric analysis

Objective

The number of research into new cognitive assessment tools has increased rapidly in recent years, sparking great interest among professionals. However, there is still little literature revealing the current status and future trends of digital technology use in cognitive assessment. The aim of this study was to summarize the development of digital cognitive assessment tools through the bibliometric method.

Methods

We carried out a comprehensive search in the Web of Science Core Collection to identify relevant papers published in English between January 1, 2003, and April 3, 2023. We used the subjects such as "digital," "computer," and "cognitive," and finally 13,244 related publications were collected. Then we conducted the bibliometric analysis by Bibliometrix" R-package, VOSviewer and CiteSpace software, revealing the prominent countries, authors, institutions, and journals.

Results

11,045 articles and 2,199 reviews were included in our analyzes. The number of annual publications in this field was rising rapidly. The results showed that the most productive countries, authors and institutions were primarily located in economically developed regions, especially the North American, European, and Australian countries. Research cooperation tended to occur in these areas as well. The application of digital technology in cognitive assessment appealed to growing attention during the outbreak of the COVID-19 epidemic.

Conclusion

Digital technology uses have had a great impact on cognitive assessment and health care. There have been substantial papers published in these areas in recent years. The findings of the study indicate the great potential of digital technology in cognitive assessment.

New medicines for spontaneous preterm birth prevention and preterm labour management: landscape analysis of the medicine development pipeline

BACKGROUND

There are few medicines in clinical use for managing preterm labor or preventing spontaneous preterm birth from occurring. We previously developed two target product profiles (TPPs) for medicines to prevent spontaneous preterm birth and manage preterm labor. The objectives of this study were to 1) analyse the research and development pipeline of medicines for preterm birth and 2) compare these medicines to target product profiles for spontaneous preterm birth to identify the most promising candidates.

METHODS

Adis Insight, Pharmaprojects, WHO international clinical trials registry platform (ICTRP), PubMed and grant databases were searched to identify candidate medicines (including drugs, dietary supplements and biologics) and populate the Accelerating Innovations for Mothers (AIM) database. This database was screened for all candidates that have been investigated for preterm birth. Candidates in clinical development were ranked against criteria from TPPs, and classified as high, medium or low potential. Preclinical candidates were categorised by product type, archetype and medicine subclass.

RESULTS

The AIM database identified 178 candidates. Of the 71 candidates in clinical development, ten were deemed high potential (Prevention: Omega-3 fatty acid, aspirin, vaginal progesterone, oral progesterone, L-arginine, and selenium; Treatment: nicorandil, isosorbide dinitrate, nicardipine and celecoxib) and seven were medium potential (Prevention: pravastatin and lactoferrin; Treatment: glyceryl trinitrate, retosiban, relcovaptan, human chorionic gonadotropin and Bryophyllum pinnatum extract). 107 candidates were in preclinical development.

CONCLUSIONS

This analysis provides a drug-agnostic approach to assessing the potential of candidate medicines for spontaneous preterm birth. Research should be prioritised for high-potential candidates that are most likely to meet the real world needs of women, babies, and health care professionals.

Modular Clinical Decision Support Networks (MoDN)-Updatable, interpretable, and portable predictions for evolving clinical environments

Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with probabilistic guidance. However, many CDSS deploy static, generic rule-based logic, resulting in inequitably distributed accuracy and inconsistent performance in evolving clinical environments. Data-driven models could resolve this issue by updating predictions according to the data collected. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often imperfectly interoperable (IIO) or unshareable. We propose Modular Clinical Decision Support Networks (MoDN) which allow flexible, privacy-preserving learning across IIO datasets, as well as being robust to the systematic missingness common to CDSS-derived data, while providing interpretable, continuous predictive feedback to the clinician. MoDN is a novel decision tree composed of feature-specific neural network modules that can be combined in any number or combination to make any number or combination of diagnostic predictions, updatable at each step of a consultation. The model is validated on a real-world CDSS-derived dataset, comprising 3,192 paediatric outpatients in Tanzania. MoDN significantly outperforms 'monolithic' baseline models (which take all features at once at the end of a consultation) with a mean macro F1 score across all diagnoses of 0.749 vs 0.651 for logistic regression and 0.620 for multilayer perceptron (p < 0.001). To test collaborative learning between IIO datasets, we create subsets with various percentages of feature overlap and port a MoDN model trained on one subset to another. Even with only 60% common features, fine-tuning a MoDN model on the new dataset or just making a composite model with MoDN modules matched the ideal scenario of sharing data in a perfectly interoperable setting. MoDN integrates into consultation logic by providing interpretable continuous feedback on the predictive potential of each question in a CDSS questionnaire. The modular design allows it to compartmentalise training updates to specific features and collaboratively learn between IIO datasets without sharing any data.

ePOCT+ and the medAL-suite: Development of an electronic clinical decision support algorithm and digital platform for pediatric outpatients in low- and middle-income countries

Electronic clinical decision support algorithms (CDSAs) have been developed to address high childhood mortality and inappropriate antibiotic prescription by helping clinicians adhere to guidelines. Previously identified challenges of CDSAs include their limited scope, usability, and outdated clinical content. To address these challenges we developed ePOCT+, a CDSA for the care of pediatric outpatients in low- and middle-income settings, and the medical algorithm suite (medAL-suite), a software for the creation and execution of CDSAs. Following the principles of digital development, we aim to describe the process and lessons learnt from the development of ePOCT+ and the medAL-suite. In particular, this work outlines the systematic integrative development process in the design and implementation of these tools required to meet the needs of clinicians to improve uptake and quality of care. We considered the feasibility, acceptability and reliability of clinical signs and symptoms, as well as the diagnostic and prognostic performance of predictors. To assure clinical validity, and appropriateness for the country of implementation the algorithm underwent numerous reviews by clinical experts and health authorities from the implementing countries. The digitalization process involved the creation of medAL-creator, a digital platform which allows clinicians without IT programming skills to easily create the algorithms, and medAL-reader the mobile health (mHealth) application used by clinicians during the consultation. Extensive feasibility tests were done with feedback from end-users of multiple countries to improve the clinical algorithm and medAL-reader software. We hope that the development framework used for developing ePOCT+ will help support the development of other CDSAs, and that the open-source medAL-suite will enable others to easily and independently implement them. Further clinical validation studies are underway in Tanzania, Rwanda, Kenya, Senegal, and India.

Systematic evaluation of the pre-eclampsia drugs, dietary supplements and biologicals pipeline using target product profiles

BACKGROUND

The Accelerating Innovation for Mothers (AIM) project established a database of candidate medicines in research and development (R&amp;D) between 2000 and 2021 for five pregnancy-related conditions, including pre-eclampsia. In parallel, we published target product profiles (TPPs) that describe optimal characteristics of medicines for use in preventing/treating pre-eclampsia. The study objective was to use systematic double screening and extraction to identify all candidate medicines being investigated for pre-eclampsia prevention/treatment and rank their potential based on the TPPs.

METHODS

Adis Insight, Pharmaprojects, WHO international clinical trials registry platform (ICTRP), PubMed and grant databases were searched (Jan-May 2021). The AIM database was screened for all candidates being investigated for pre-eclampsia. Candidates in clinical development were evaluated against nine prespecified criteria from TPPs identified as key for wide-scale implementation, and classified as high, medium or low potential based on matching to the TPPs. Preclinical candidates were categorised by product type, archetype and medicine subclass.

RESULTS

The AIM database identified 153 candidates for pre-eclampsia. Of the 87 candidates in clinical development, seven were classified as high potential (prevention: esomeprazole, L-arginine, chloroquine, vitamin D and metformin; treatment: sulfasalazine and metformin) and eight as medium potential (prevention: probiotic lactobacilli, dalteparin, selenium and omega-3 fatty acid; treatment: sulforaphane, pravastatin, rosuvastatin and vitamin B3). Sixty-six candidates were in preclinical development, the most common being amino acid/peptides, siRNA-based medicines and polyphenols.

CONCLUSIONS

This is a novel, evidence-informed approach to identifying promising candidates for pre-eclampsia prevention and treatment - a vital step in stimulating R&amp;D of new medicines for pre-eclampsia suitable for real-world implementation.

Multi-, Inter-, and Transdisciplinarity within the Public Health Workforce: A Scoping Review to Assess Definitions and Applications of Concepts

In light of the current public health challenges, calls for more inter- and transdisciplinarity in the public health workforce are increasing, particularly to respond to complex and intersecting health challenges, such as those presented by the climate crisis, emerging infectious diseases, or military conflict. Although widely used, it is unclear how the concepts of multi-, inter-, and transdisciplinarity are applied with respect to the public health workforce. We conducted a scoping review and qualitative content analysis to provide an overview of how the concepts of multi-, inter-, and transdisciplinarity are defined and applied in the academic literature about the public health workforce. Of the 1957 records identified, 324 articles were included in the review. Of those, 193, 176, and 53 mentioned the concepts of multi-, inter-, and transdisciplinarity, respectively. Overall, 44 articles provided a definition. Whilst definitions of multidisciplinarity were scarce, definitions of inter- and transdisciplinarity were more common and richer, highlighting the aim of the collaboration and the blurring and dissolution of disciplinary boundaries. A better understanding of the application of multi-, inter-, and transdisciplinarity is an important step to implementing these concepts in practice, including in institutional structures, academic curricula, and approaches in tackling public health challenges.

ACCURACY OF C-REACTIVE PROTEIN AND PROCALCITONIN FOR DIAGNOSING BACTERIAL INFECTIONS AMONG SUBJECTS WITH PERSISTENT FEVER IN THE TROPICS

Background

In low-resource settings, inflammatory biomarkers can help identify patients with acute febrile illness who do not require antibiotics. Their use has not been studied in persistent fever (defined as fever lasting for ≥7 days at presentation).

Methods

C-reactive protein (CRP) and procalcitonin (PCT) levels were measured in stored serum samples of patients with persistent fever prospectively enrolled in Cambodia, the Democratic Republic of Congo, Nepal, and Sudan. Diagnostic accuracy was assessed for identifying all bacterial infections and the subcategory of severe infections judged to require immediate antibiotics.

Results

Among 1838 participants, CRP and PCT levels were determined in 1777 (96.7%) and 1711 (93.1%) samples, respectively, while white blood cell (WBC) count was available for 1762 (95.9%). Areas under the receiver operating characteristic curve for bacterial infections were higher for CRP (0.669) and WBC count (0.651) as compared with PCT (0.600; P <.001). Sensitivity for overall and severe bacterial infections was 76.3% (469/615) and 88.2% (194/220) for CRP >10 mg/L, 62.4% (380/609) and 76.8% (169/220) for PCT >0.1 µg/L, and 30.5% (184/604) and 43.7% (94/215) for WBC >11 000/µL, respectively. Initial CRP level was <10 mg/L in 45% of the participants who received antibiotics at first presentation.

Conclusions

In patients with persistent fever, CRP and PCT showed higher sensitivity for bacterial infections than WBC count, applying commonly used cutoffs for normal values. A normal CRP value excluded the vast majority of severe infections and could therefore assist in deciding whether to withhold empiric antibiotics after cautious clinical assessment.

Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries

INTRODUCTION

Early and accurate recognition of children at risk of progressing to critical illness could contribute to improved patient outcomes and resource allocation. In resource limited settings digital triage tools can support decision making and improve healthcare delivery. We developed a model for rapid identification of critically ill children at triage.

METHODS

This was a prospective cohort study of acutely ill children presenting at Jinja Regional Referral Hospital in Eastern Uganda. Variables collected in the emergency department informed the development of a logistic model based on hospital admission using bootstrap stepwise regression. Low and high-risk thresholds for 90% minimum sensitivity and specificity, respectively generated three risk level categories. Performance was assessed using receiver operating characteristic curve analysis on a held-out test set generated by an 80:20 split with 10-fold cross validation. A risk stratification table informed clinical interpretation.

RESULTS

The model derivation cohort included 1,612 participants, with an admission rate of approximately 23%. The majority of admitted patients were under five years old and presenting with sepsis, malaria, or pneumonia. A 9-predictor triage model was derived: logit (p) = -32.888 + (0.252, square root of age) + (0.016, heart rate) + (0.819, temperature) + (-0.022, mid-upper arm circumference) + (0.048 transformed oxygen saturation) + (1.793, parent concern) + (1.012, difficulty breathing) + (1.814, oedema) + (1.506, pallor). The model afforded good discrimination, calibration, and risk stratification at the selected thresholds of 8% and 40%.

CONCLUSION

In a low income, pediatric population, we developed a nine variable triage model with high sensitivity and specificity to predict who should be admitted. The triage model can be integrated into any digital platform and used with minimal training to guide rapid identification of critically ill children at first contact. External validation and clinical implementation are in progress.

Target product profiles for novel medicines to prevent and treat preeclampsia: An expert consensus

BACKGROUND

Preeclampsia and eclampsia are a leading cause of global maternal and newborn mortality. Currently, there are few effective medicines that can prevent or treat preeclampsia. Target Product Profiles (TPPs) are important tools for driving new product development by specifying upfront the characteristics that new products should take. Considering the lack of investment and innovation around new medicines for obstetric conditions, we aimed to develop two new TPPs for medicines to prevent and treat preeclampsia.

METHODS AND FINDINGS

We used a multi-methods approach comprised of a literature review, stakeholder interviews, online survey, and public consultation. Following an initial literature review, diverse stakeholders (clinical practice, research, academia, international organizations, funders, consumer representatives) were invited for in-depth interviews and an online international survey, as well as public consultation on draft TPPs. The level of stakeholder agreement with TPPs was assessed, and findings from interviews were synthesised to inform the final TPPs. We performed 23 stakeholder interviews and received 46 survey responses. A high level of agreement was observed in survey results, with 89% of TPP variables reaching consensus (75% agree or strongly agree). Points of discussion were raised around the target population for preeclampsia prevention and treatment, as well as the acceptability of cold-chain storage and routes of administration.

CONCLUSION

There is consensus within the maternal health research community on the parameters that new medicines for preeclampsia prevention and treatment must achieve to meet real-world health needs. These TPPs provide necessary guidance to spur interest, innovation and investment in the development of new medicines to prevent and treat preeclampsia.

Development of a target product profile for a point-of-care cardiometabolic device

Introduction

Multi-parameter diagnostic devices can simplify cardiometabolic disease diagnosis. However, existing devices may not be suitable for use in low-resource settings, where the burden of non-communicable diseases is high. Here we describe the development of a target product profile (TPP) for a point-of-care multi-parameter device for detection of biomarkers for cardiovascular disease and metabolic disorders, including diabetes, in primary care settings in low- and middle-income countries (LMICs).

Methods

A draft TPP developed by an expert group was reviewed through an online survey and semi-structured expert interviews to identify device characteristics requiring refinement. The draft TPP included 41 characteristics with minimal and optimal requirements; characteristics with an agreement level for either requirement of ≤ 85% in either the survey or among interviewees were further discussed by the expert group and amended as appropriate.

Results

Twenty people responded to the online survey and 18 experts participated in the interviews. Twenty-two characteristics had an agreement level of ≤ 85% in either the online survey or interviews. The final TPP defines the device as intended to be used for basic diagnosis and management of cardiometabolic disorders (lipids, glucose, HbA1c, and creatinine) as minimal requirement, and offering an expanded test menu for wider cardiometabolic disease management as optimal requirement. To be suitable, the device should be intended for level 1 healthcare settings or lower, used by minimally trained healthcare workers and allow testing using self-contained cartridges or strips without the need for additional reagents. Throughput should be one sample at a time in a single or multi-analyte cartridge, or optimally enable testing of several samples and analytes in parallel with random access.

Conclusion

This TPP will inform developers of cardiometabolic multi-parameter devices for LMIC settings, and will support decision makers in the evaluation of existing and future devices.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02298-7.

Refining clinical algorithms for a neonatal digital platform for low-income countries: a modified Delphi technique

OBJECTIVES

To determine whether a panel of neonatal experts could address evidence gaps in local and international neonatal guidelines by reaching a consensus on four clinical decision algorithms for a neonatal digital platform (NeoTree).

DESIGN

Two-round, modified Delphi technique.

SETTING AND PARTICIPANTS

Participants were neonatal experts from high-income and low-income countries (LICs).

METHODS

This was a consensus-generating study. In round 1, experts rated items for four clinical algorithms (neonatal sepsis, hypoxic ischaemic encephalopathy, respiratory distress of the newborn, hypothermia) and justified their responses. Items meeting consensus for inclusion (≥80% agreement) were incorporated into the algorithms. Items not meeting consensus were either excluded, included following revisions or included if they contained core elements of evidence-based guidelines. In round 2, experts rated items from round 1 that did not reach consensus.

RESULTS

Fourteen experts participated in round 1, 10 in round 2. Nine were from high-income countries, five from LICs. Experts included physicians and nurse practitioners with an average neonatal experience of 20 years, 12 in LICs. After two rounds, a consensus was reached on 43 of 84 items (52%). Per experts' recommendations, items in line with local and WHO guidelines yet not meeting consensus were still included to encourage consistency for front-line healthcare workers. As a result, the final algorithms included 53 items (62%).

CONCLUSION

Four algorithms in a neonatal digital platform were reviewed and refined by consensus expert opinion. Revisions to NeoTree will be made in response to these findings. Next steps include clinical validation of the algorithms.

Following Guidelines for Drug-Resistant Tuberculosis: “Yes, it’s a challenge”

Background

Drug-resistant tuberculosis (DR-TB) is a major contributor to antimicrobial resistance (AMR) globally and is projected to be responsible for up to a quarter of AMR-associated deaths in the future. Management of DR-TB is increasingly decentralised to primary healthcare settings, and simultaneously becoming more complex due to a growing range of treatment options (e.g. novel agents, shorter regimens). This is reflected in the numerous recent updates to international guidelines and as such understanding the barriers and enablers to how healthcare workers access and use guidelines is vital.

Materials and Methods

We used an established psychological framework – the theoretical domains framework (TDF) – to construct and analyse an online survey and focus groups to explore healthcare workers current use of DR-TB guidelines in South Africa. We aimed to identify barriers and enablers with which to direct future attempts at improving guideline use.

Results

There were 19 responses to the online survey and 14 participants in two focus groups. 28% used the most up-to-date national guidelines, 79% accessed guidelines primarily on electronic devices. The TDF domains of ‘Social Influences’ (mean Likert score = 4.3) and ‘Beliefs about Consequences’ (4.2) were key enablers, with healthcare workers encouraged to use guidelines and also recognising the value in doing so. ‘Environmental Resources’ (3.7) and ‘Knowledge’ (3.3) were key barriers with limited, or variable access to guidelines and lack of confidence using them being notable issues. This was most noted for certain subgroups: children, HIV co-infected, pregnant women (2.7).

Discussion

Current use of DR-TB guidelines in South Africa is suboptimal. Planned interventions should focus on overcoming the identified key barriers and might include an increased use of digital tools.

The Promise of Clinical Decision Support Systems Targetting Low-Resource Settings

Low-resource clinical settings are plagued by low physician-to-patient ratios and a shortage of high-quality medical expertise and infrastructure. Together, these phenomena lead to over-burdened healthcare systems that under-serve the needs of the community. Alleviating this burden can be undertaken by the introduction of clinical decision support systems (CDSSs); systems that support stakeholders (ranging from physicians to patients) within the clinical setting in their day-to-day activities. Such systems, which have proven to be effective in the developed world, remain to be under-explored in low-resource settings. This review attempts to summarize the research focused on clinical decision support systems that either target stakeholders within low-resource clinical settings or diseases commonly found in such environments. When categorizing our findings according to disease applications, we find that CDSSs are predominantly focused on dealing with bacterial infections and maternal care, do not leverage deep learning, and have not been evaluated prospectively. Together, these highlight the need for increased research in this domain in order to impact a diverse set of medical conditions and ultimately improve patient outcomes.