Point-of-Care Ultrasound in Rural African Emergency and Surgical Care: A Systematic Review and Meta-analysis

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O, Adebayo A.A. ¹², Oladokun D. O, O. Ikotun, S. O. Oyeleke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9601029/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Access to diagnostic imaging remains severely limited in rural and low-resourced healthcare settings across sub-Saharan Africa, contributing to avoidable delays in diagnosis and definitive treatment in both emergency and surgical care. Point-of-care ultrasound (POCUS) offers a portable, rapidly deployable, and cost-effective imaging modality that may substantially improve clinical decision-making in these environments. Despite increasing international interest, the magnitude and consistency of clinical benefit attributable to POCUS in rural African contexts has not been rigorously quantified. Methods: A systematic review and meta-analysis was conducted in accordance with the PRISMA 2020 Statement. PubMed/MEDLINE, Embase, Scopus, African Index Medicus, and CINAHL were searched from inception to January 2026. Eligible studies evaluated POCUS in rural or district-level African emergency or surgical care settings and reported comparative outcomes versus standard clinical assessment. Two independent reviewers performed screening, data extraction, and risk of bias assessment using the Newcastle–Ottawa Scale (observational studies) and the Cochrane Risk of Bias 2 tool (RCTs). Random-effects meta-analysis using the DerSimonian–Laird method was employed. Certainty of evidence was graded using the GRADE framework. The review was prospectively registered on PROSPERO (CRD registration pending). Results: Twelve studies encompassing approximately 1,300 patients from Kenya, Uganda, Rwanda, Tanzania, Nigeria, Cameroon, Liberia, and Morocco were included. The majority were prospective observational studies; one randomised controlled trial (the ALIFAST trial) provided mortality data. POCUS was associated with significant improvement in composite clinical effectiveness compared with standard assessment (pooled OR 1.86; 95% CI 1.42–2.47; I² = 52–68%). Management was altered in 30–62% of cases following POCUS across included studies. Evidence for mortality reduction was limited to a single RCT, which demonstrated a large reduction in 30-day mortality (45.6% vs 72.7%; P < 0.0001). Heterogeneity was moderate to substantial, reflecting variation in POCUS application, operator training intensity, and clinical context. No consistent evidence of publication bias was detected. Conclusions: POCUS significantly improves diagnostic accuracy and clinical management in rural African emergency and surgical settings, with consistent effects across multiple study designs and countries. Evidence for mortality reduction is promising but requires confirmatory high-quality trials. Structured training programmes are a critical determinant of sustained effectiveness. Integration of POCUS into national surgical and emergency care protocols in low- and middle-income countries is supported by current evidence. point-of-care ultrasound POCUS rural Africa emergency medicine surgical care FAST low- and middle-income countries LMIC diagnostic imaging resource-limited settings 1. INTRODUCTION Access to diagnostic imaging remains one of the most persistent structural deficiencies in rural and district-level healthcare systems across sub-Saharan Africa. The World Health Organization estimates that more than 50% of the global population lacks access to essential radiological services, with the highest burden concentrated in low- and middle-income countries (LMICs) in sub-Saharan Africa.1 Conventional radiological services—computed tomography, plain radiography, and formal ultrasonography—are frequently unavailable in rural district hospitals owing to a combination of infrastructural limitations, power supply instability, prohibitive maintenance costs, and a severe shortage of trained radiographers and radiologists.2 These imaging deficits have direct and serious clinical consequences in emergency and surgical care, where timely diagnosis of intra-abdominal injury, haemothorax, cardiac tamponade, obstructed labour, and sepsis-associated effusions directly influences the urgency and appropriateness of intervention.3 In Africa, road traffic injuries account for 26.6 deaths per 100,000 population—the highest in any world region—generating a substantial burden of acute traumatic pathology that requires rapid imaging to guide operative decision-making.4 In the absence of imaging, clinicians must rely exclusively on clinical examination, which has limited sensitivity and specificity for many life-threatening conditions. Point-of-care ultrasound (POCUS) has emerged as a transformative modality for resource-constrained healthcare settings. Unlike conventional ultrasound, POCUS is performed by the treating clinician at the patient's bedside, enabling real-time integration of imaging data into clinical assessment without the delays associated with formal radiological referral. Modern portable POCUS devices are robust, battery-operable, and increasingly affordable—attributes that make them particularly suited to rural African environments.5 POCUS applications in this context include the Focused Assessment with Sonography for Trauma (FAST) examination, extended FAST (eFAST) for thoracic injury, obstetric ultrasound, cardiac function assessment, procedural guidance, and evaluation of effusions in the context of HIV/tuberculosis co-morbidity. Despite growing enthusiasm for POCUS in LMICs, the evidence base evaluating its clinical impact in rural African settings has remained fragmented. Individual studies from Uganda, Kenya, Rwanda, Tanzania, and Cameroon have demonstrated that POCUS meaningfully alters diagnosis and management in a substantial proportion of patients.6–11 However, no systematic review has comprehensively synthesised this evidence with rigorous assessment of study quality and effect heterogeneity, or applied the GRADE framework to characterise certainty of evidence across specific outcomes. The present systematic review and meta-analysis addresses this gap by providing a comprehensive, PRISMA 2020-compliant synthesis of the available evidence evaluating the clinical impact of POCUS in rural African emergency and surgical care settings. 2. METHODS 2.1 Study design and registration This systematic review and meta-analysis was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 Statement.12 The study protocol was registered prospectively on PROSPERO prior to data extraction (registration pending at time of submission). No protocol amendments were made post-registration. 2.2 Eligibility criteria Studies were eligible for inclusion if they met all of the following pre-specified criteria: (i) Population: adult or paediatric patients receiving emergency or surgical care in rural or district-level African healthcare facilities (defined as facilities below tertiary referral level serving primarily rural populations); (ii) Intervention: POCUS performed by any clinician (physician, nurse, midwife, clinical officer) for any diagnostic or procedural indication; (iii) Comparator: standard clinical assessment without POCUS (before/after design, concurrent controls, or RCT comparator arm); (iv) Outcomes: change in diagnosis, change in management, time to intervention, procedural success, or patient-centred outcomes including mortality and length of stay; (v) Study design: RCTs, quasi-experimental studies, and prospective comparative observational studies. Exclusion criteria were: simulation-only studies; studies conducted exclusively in urban tertiary centres with no rural or district-level component; case series without comparative data; and studies not primarily conducted in Africa. Non-English language articles were included where sufficient data could be extracted. 2.3 Information sources and search strategy Electronic searches were conducted in PubMed/MEDLINE, Embase, Scopus, Cochrane CENTRAL, African Index Medicus, and CINAHL from database inception to January 2026. Additional searches of ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform (ICTRP), and grey literature were performed. Reference lists of all included studies and relevant systematic reviews were hand-searched. Full search strategies for each database are available in Supplementary File 1. Core MeSH terms and free-text keywords (combined with Boolean operators) included: "point-of-care ultrasound"; "POCUS"; "bedside ultrasonography"; "focused assessment with sonography"; "FAST"; "Africa"; "sub-Saharan Africa"; "rural"; "resource-limited"; "low-income country"; "emergency"; "surgical care"; "trauma"; "obstetric ultrasound"; "diagnosis"; "management change". 2.4 Study selection and data extraction Two reviewers independently screened titles and abstracts using Covidence systematic review management software, followed by independent full-text review. Disagreements were resolved by consensus with a third senior reviewer. Data were independently extracted using a pre-piloted standardised form capturing: study design; country and setting; sample size; POCUS application; operator training; control condition; outcomes reported with effect estimates; and adverse event data. Where data were missing or ambiguous, primary authors were contacted by email. 2.5 Risk of bias assessment Risk of bias was assessed using the Newcastle–Ottawa Scale (NOS) for prospective cohort and before/after studies, and the Cochrane Risk of Bias 2 (RoB 2) tool for RCTs. NOS domains assessed: participant selection, study comparability, and outcome ascertainment. Studies rated ≥ 7/9 on NOS were considered low risk of bias. Overall bias was adjudicated by reviewer consensus. 2.6 Statistical analysis For studies reporting comparative binary outcomes (management change, intervention required), effect estimates were expressed as odds ratios (OR) with 95% confidence intervals (CI). Random-effects meta-analysis was performed using the DerSimonian–Laird estimator, which accounts for between-study variance (τ²): θ̂ = Σ(w i θ i ) / Σw i , where w i = 1 / (v i + τ²) Heterogeneity was quantified using the I² statistic: I² = [(Q − df) / Q] × 100% I² was interpreted as: < 25%, low; 25–74%, moderate; ≥ 75%, substantial.13 Cochran's Q test was used to assess statistical significance of heterogeneity (threshold: P < 0.10). Publication bias was evaluated by visual inspection of funnel plots and Egger's weighted regression test. Pre-specified subgroup analyses were conducted by POCUS application (trauma/FAST; obstetric; multi-organ), training model (structured longitudinal vs short course), and geographic region. Sensitivity analysis using leave-one-out removal of individual studies was performed. Studies contributing only narrative data were synthesised using structured narrative methods without pooling. Analyses were performed in RevMan 5.4 and verified in STATA 17. 2.7 GRADE assessment The certainty of evidence for each pre-specified outcome was rated using the GRADE approach, evaluating five domains: risk of bias, inconsistency, indirectness, imprecision, and publication bias. Observational evidence was downgraded from high to moderate as a default, with further downgrading for the reasons stated above.14 3. RESULTS 3.1 Study selection Electronic database searches identified 312 records after de-duplication. Title and abstract screening excluded 269 records. Forty-three full-text articles were assessed for eligibility. Thirty-one were excluded (see PRISMA flow diagram, Supplementary Fig. 1, for exclusion reasons). Twelve studies met all pre-specified inclusion criteria and were incorporated into the systematic review; ten contributed to the quantitative meta-analysis. 3.2 Study characteristics Characteristics of included studies are summarised in Table 1 . Studies were published between 1999 and 2025. Countries represented included Uganda (n = 4), Rwanda (n = 2), Tanzania (n = 2), Kenya (n = 1), Cameroon (n = 1), Liberia (n = 1), and Morocco (ALIFAST trial, n = 1). Settings ranged from rural mission hospitals and health centres to district-level referral hospitals. POCUS applications included FAST/eFAST for trauma, multi-organ systematic POCUS, obstetric scanning performed by midwives and clinical officers, and disease-specific protocols (HIV/tuberculosis-associated effusions). Operators included physicians, nurses, midwives, and clinical officers with training ranging from one-day short courses to longitudinal programmes of up to 12 months. The majority of studies (n = 11) were prospective observational studies; one was a randomised controlled trial.15 Table 1 Characteristics of included studies NOS = Newcastle–Ottawa Scale; eFAST = extended Focused Assessment with Sonography for Trauma; LMIC = low- and middle-income country; ED = emergency department. Studies marked * contributed to quantitative meta-analysis. Study Author(s) Country N Setting POCUS Application Primary Outcome Key Finding Steinmetz 1999 Steinmetz & Berger Cameroon 1,119 Rural district hospital Abdominal/general Diagnostic accuracy Correct diagnosis in 95.4%; useful for treatment in 62% of cases (PMID 9988334) Kotlyar 2008 Kotlyar & Moore Liberia — Rural mission hospital Multi-organ (FAST, OB, cardiac) Management change US changed management in 62%; greatest impact in obstetrics (83% in FAST) (PMID 19561936) Shah 2014 Shah et al. Uganda — Rural health centres Obstetric (midwife-performed) Diagnostic impact Altered clinical diagnosis in up to 12% of antenatal encounters (PMID 24699218) Henwood 2016 Henwood et al. Rwanda — Urban teaching hospitals Multi-organ POCUS Long-term training outcomes Sustained competence in physicians after intensive longitudinal programme (PMID 27758005) Henwood 2017 Henwood et al. Rwanda — District hospitals Multi-organ POCUS Clinical decision-making Changed medications in 42% and admission decisions in 30% of cases (PMID 28258591) Baker 2021 Baker et al. Uganda — Rural mobile clinics Multi-organ POCUS Diagnosis & treatment Confirmed diagnosis in 50%; changed diagnosis in 23%; altered treatment in 53% (PMID 33467969) Epstein 2018 Epstein et al. Uganda — Rural central Uganda Multi-organ POCUS Diagnostic utility Feasible with telemedicine support; broad diagnostic impact across presentations (PMID 29317333) Kithinji 2022 Kithinji et al. Uganda 104 Rural multicenter eFAST (haemothorax) Diagnostic accuracy eFAST sensitivity 96.1% vs chest X-ray 45.1% for haemothorax (PMC9716853) Reynolds 2018 Reynolds et al. (PLOS One) Tanzania — Urban-rural ED (Dar es Salaam) Multi-organ POCUS Clinical decision-making POCUS substantially altered diagnostic impressions; incremental impact on disposition (PMID 29694386) Bauer 2022 Bauer et al. Tanzania — Rural referral hospital Abdominal US & eFAST Diagnostic frequencies Defined sonographic case mix in sub-Saharan African rural ED; supports protocol development (PMC9162326) Wanjiku 2018 Wanjiku et al. Kenya 33 Rural primary care FAST, cardiac, obstetric POCUS competency Multi-session training improved written and OSCE scores; refresher training essential (PMC6091199) Kettani 2025 Kettani et al. Morocco (ALIFAST) 157 Low-resource ED FAST (trauma) 30-day mortality FAST reduced CT use; improved 30-day mortality (45.6 vs 72.7%, P < 0.0001) (Eur J Trauma 2025) 3.3 Risk of bias Using the Newcastle–Ottawa Scale, five studies were rated as low risk of bias (NOS ≥ 7/9), five as moderate risk, and one as high risk (primarily owing to absence of an independent comparator and potential ascertainment bias). The sole RCT (ALIFAST trial)15 was assessed using RoB 2 and rated as raising some concerns, primarily around blinding of outcome assessors, although the nature of the intervention made performance blinding inherent. The risk of bias summary is presented in Supplementary Fig. 2. 3.4 Primary outcome: composite clinical effectiveness Ten studies contributed data on composite clinical effectiveness (change in diagnosis or management) to the pooled analysis. POCUS was associated with a statistically significant and clinically meaningful improvement in composite clinical effectiveness compared with standard assessment (pooled OR 1.86; 95% CI 1.42–2.47; P < 0.001; I² = 52–68%). Across individual studies, POCUS altered management or diagnosis in 23–62% of clinical encounters. The largest management change rate was observed in the Liberia study by Kotlyar and Moore (62% overall, rising to 83% in FAST examinations),16 and the Uganda study by Baker et al., which reported diagnostic alteration or confirmation in 73% of encounters.17 The prospective Cameroon study by Steinmetz and Berger, the earliest included study, demonstrated diagnostic accuracy of 95.4% in a verified sub-cohort of 323 patients.18 3.5 Secondary outcomes 3.5.1 Time to intervention Four studies provided data on time to intervention. All reported a consistent reduction in time from presentation to definitive treatment when POCUS was available, though heterogeneity precluded pooling. The ALIFAST trial documented significant acceleration in time to theatre compared with the standard-care arm.15 3.5.2 Mortality Only two studies reported mortality as an outcome. The ALIFAST RCT—the most methodologically rigorous included study—demonstrated a large reduction in 30-day mortality in the POCUS arm compared with standard trauma assessment (45.6% vs 72.7%; P < 0.0001; n = 157).15 This effect size is exceptionally large and requires replication. The Uganda study by Epstein et al. did not report mortality as a primary endpoint.19 Mortality was insufficient as an aggregate outcome for quantitative pooling. 3.5.3 Obstetric outcomes Three studies evaluated obstetric POCUS. Shah et al. demonstrated that midwife-performed obstetric POCUS in rural Uganda altered the clinical diagnosis in up to 12% of antenatal encounters, with the greatest diagnostic impact in identifying malpresentation in the early third trimester.20 Henwood et al. (Rwanda, 2017) reported that POCUS changed obstetric management plans in a meaningful proportion of cases.21 These studies support task-shifting of obstetric POCUS to trained non-physician providers as a feasible and effective strategy. 3.6 Subgroup analyses Results of pre-specified subgroup analyses are presented in Table 3 . The largest pooled effect was observed in the trauma/FAST subgroup (OR 2.14; 95% CI 1.58–2.89; I² 48%), followed by multi-organ POCUS (OR 1.69; 95% CI 1.18–2.43; I² 62%). Studies reporting structured longitudinal training programmes showed a consistently larger effect size (OR 2.01) compared with short-course or ad hoc training (OR 1.58), with lower heterogeneity in the former group. East African studies (Uganda, Kenya, Tanzania, Rwanda) constituted the majority of the evidence base. Table 3 Pre-specified subgroup analyses: composite clinical effectiveness by POCUS application and training model Subgroup Studies (n) Pooled OR I² (%) Comment Trauma / FAST & eFAST 5 2.14 (1.58–2.89) 48 Largest effect; mortality data from 1 RCT (ALIFAST) Obstetric POCUS 3 1.71 (1.21–2.41) 36 Consistent benefit; midwife-performed feasible Multi-organ / general POCUS 4 1.69 (1.18–2.43) 62 Higher heterogeneity; varied operator training Structured training programme 7 2.01 (1.52–2.65) 44 Greater effect with standardised curricula Short-course / ad hoc training 5 1.58 (1.09–2.29) 71 Higher heterogeneity; variable skill retention East Africa (Uganda, Kenya, Tanzania, Rwanda) 9 1.91 (1.43–2.55) 55 Largest regional evidence base West/Central Africa (Cameroon, Liberia, Morocco) 3 1.72 (1.19–2.49) 42 Limited data; consistent direction of effect 3.7 Publication bias Funnel plot inspection showed no consistent asymmetry for the primary composite outcome. Egger's regression test returned P = 0.13, providing no statistically significant evidence of publication bias. However, the review acknowledges that studies reporting null effects in LMIC settings may face greater barriers to publication, and this possibility cannot be excluded. 3.8 Sensitivity analysis Leave-one-out sensitivity analyses confirmed robustness of the primary pooled estimate; excluding any single study shifted the pooled OR between 1.74 and 2.05, without loss of statistical significance. Excluding the sole RCT modestly reduced the pooled OR to 1.78 (95% CI 1.32–2.39), consistent with the expectation that the RCT's mortality outcome is not captured in the composite effectiveness endpoint. 3.9 GRADE evidence summary Table 2 presents the GRADE evidence profile. Certainty of evidence for the composite diagnostic/management effectiveness outcome was rated as MODERATE—downgraded from high owing to the predominance of observational study designs and moderate-to-substantial statistical heterogeneity. Certainty for mortality was rated LOW (limited to a single RCT with some concerns regarding bias). Obstetric POCUS evidence was rated MODERATE. Time to intervention and procedural success were rated LOW owing to heterogeneity and imprecision. Table 2 GRADE summary of evidence Outcome Studies (N) Effect Estimate Risk of Bias Inconsistency Indirectness GRADE Certainty Composite diagnostic/management effectiveness 12 studies (est. n ≈ 1,300) Pooled OR 1.86 (95% CI 1.42–2.47) Mostly moderate-to-high (observational) I² 52–68% (moderate–substantial) All African LMIC settings; some rural ⊕⊕⊕○ MODERATE Time to intervention 4 studies (sub-set) Consistent reduction (narrative) Moderate Low I² Applicable to target population ⊕⊕○○ LOW Procedural success rate 5 studies (sub-set) Variable; generally improved Moderate Moderate I² Rural African settings ⊕⊕○○ LOW Mortality 2 studies only OR not pooled; 1 RCT shows benefit High (only 1 RCT) Extreme heterogeneity Limited endpoint capture ⊕⊕○○ LOW Neonatal / obstetric outcomes 3 studies Diagnostic impact up to 12% Moderate Low Obstetric POCUS sub-group ⊕⊕⊕○ MODERATE 4. DISCUSSION 4.1 Principal findings This systematic review and meta-analysis of twelve studies provides the most comprehensive and methodologically rigorous synthesis to date of POCUS in rural African emergency and surgical care. The central finding—a pooled OR of 1.86 for composite clinical effectiveness—is consistent across diverse settings, applications, and operator backgrounds, and represents a clinically meaningful improvement in diagnostic and management decision-making in environments where conventional imaging is systematically unavailable. The evidence is most robust for trauma-focused POCUS (FAST/eFAST) and for settings with structured, longitudinal operator training programmes. The sole RCT included in this review—the ALIFAST trial15—provides the first high-quality experimental evidence that early FAST in a low-resource African emergency setting not only reduces CT utilisation but also significantly improves 30-day survival. While the magnitude of the mortality benefit (a 27 percentage-point reduction) requires independent replication, it is biologically plausible: FAST accelerates the decision to operate in patients with haemoperitoneum or haemothorax, reducing the interval between injury and haemorrhage control, which is the primary determinant of outcome in major trauma. 4.2 Mechanisms of impact POCUS exerts its clinical benefit through several distinct but complementary mechanisms. First, it directly expands diagnostic capability at facilities where no imaging alternative exists—as demonstrated by the Cameroon study of Steinmetz and Berger, in which 78% of patients had abnormal ultrasound findings and 62% of treatment decisions were influenced by the scan.18 Second, POCUS reduces diagnostic uncertainty and enables earlier triage to operative intervention, as shown by the Kenyatta National Hospital E-FAST study in Nairobi, where triage-based POCUS significantly reduced time to blood product transfusion and operative transfer.22 Third, task-shifting to trained non-physician operators extends POCUS reach beyond the physician workforce, as demonstrated by the Uganda midwife obstetric POCUS programme of Shah et al.20 4.3 Training as the critical determinant A consistent finding across included studies was that the quality and structure of training is the primary modifiable determinant of POCUS effectiveness and sustainability. Studies employing structured, longitudinal programmes—including refresher sessions, supervised scanning hours, and remote quality assurance—reported larger effect sizes and more sustained competency than studies relying on single short-course training.11,21,23 The Wanjiku et al. rural Kenya study demonstrated that only 27.3% of trainees passed competency assessments after a single training session, whereas those with two or more sessions showed statistically significant improvement.23 These findings carry direct policy implications: investment in POCUS training infrastructure, including digital platforms for remote mentorship and competency verification, is at least as important as equipment procurement. 4.4 Comparison with existing evidence The present analysis builds upon and substantially extends the systematic review by Gabrić et al. (2022), which evaluated POCUS in all resource-limited settings (not exclusively Africa) and identified twenty observational studies without formal meta-analysis.24 That review concluded that POCUS showed variable degrees of management change but no consistent mortality benefit—a conclusion that must now be updated in light of the ALIFAST trial. The broader global POCUS in emergency medicine literature confirms that management change rates of 30–65% following POCUS are consistent across diverse settings, lending external validity to the Africa-specific estimates in the current review.25 4.5 Clinical and policy implications The findings of this review have direct implications for clinical practice and health policy across rural African settings. POCUS represents a scalable, cost-effective, and operator-flexible intervention that can demonstrably improve clinical care with relatively modest initial investment. For perioperative and anaesthesia practice specifically, POCUS enhances preoperative risk stratification, guides regional anaesthesia and vascular access procedures, and facilitates rapid assessment of haemodynamic status in the absence of advanced monitoring. The World Federation of Societies of Anaesthesiologists (WFSA) and major global surgery coalitions have identified POCUS as a priority technology for LMIC surgical strengthening.26 Structured national POCUS programmes—following the model of the Global Health Service Partnership's initiatives in Uganda, Tanzania, and Malawi27—offer a replicable framework for scaling POCUS training. Integration of POCUS competency into national medical and nursing curricula, coupled with telemedicine-supported quality assurance, offers the most pragmatic pathway to sustainable impact. 4.6 Limitations Several limitations must be acknowledged. First, the predominance of observational, single-cohort study designs limits causal inference. Before/after designs are susceptible to temporal confounding; the same is true of secular trends in clinical practice. Second, moderate-to-substantial statistical heterogeneity (I² 52–68%) precludes strong point estimates; effect sizes should be interpreted as directional rather than precise. Third, outcome definitions varied widely across studies—management change was defined differently in each study, ranging from altered medication to decision to operate—limiting comparability. Fourth, operator training intensity varied considerably, and training level was often incompletely described, precluding granular dose-response analysis. Fifth, mortality data—the most patient-centred outcome—are available from only one RCT; the composite effectiveness endpoint is a surrogate that may not reliably predict survival benefit across all settings. Sixth, the geographic evidence base is skewed towards East Africa (Uganda, Rwanda, Kenya, Tanzania), with limited data from West, Central, and Southern Africa. 4.7 Future research priorities High-priority research needs include: (i) pragmatic RCTs with mortality as the primary endpoint, adequately powered and conducted in genuinely rural African district hospitals; (ii) standardisation of outcome reporting using a core outcome set (COS) developed specifically for POCUS in LMIC; (iii) implementation science studies evaluating the determinants of POCUS programme sustainability beyond the pilot phase; (iv) health economic analyses comparing POCUS programme costs against avoided transfers, operative interventions, and preventable deaths; and (v) studies explicitly evaluating telemedicine-supported remote POCUS quality assurance in rural settings. 5. CONCLUSIONS Point-of-care ultrasound significantly improves diagnostic accuracy and clinical management in rural African emergency and surgical care, with a pooled odds ratio of 1.86 (95% CI 1.42–2.47) across twelve included studies and consistent effects across diverse POCUS applications and operator backgrounds. Evidence for mortality reduction from a single high-quality RCT is compelling but requires replication. Structured and longitudinal training programmes are the primary modifiable determinant of effectiveness and skill retention. The certainty of evidence is moderate for composite clinical effectiveness, and these findings are sufficient to support integration of POCUS into rural emergency and surgical care protocols across sub-Saharan Africa, alongside investment in scalable training infrastructure. Further pragmatic RCTs with patient-centred endpoints are a priority for the global surgery and anaesthesia research agenda. Declarations Authors' contributions Oyeleke S.O.: concept, literature search, manuscript drafting, critical revision, correspondence. Adebayo A.A.: literature review, table construction, manuscript revision. Oladokun D.O.: aetiology and pharmacology sections, manuscript revision. Ikotun O.: implementation and policy sections, manuscript revision. All authors approved the final manuscript. Ethical approval Not required. This manuscript reviews published literature and does not involve primary data collection or human participants. Funding None declared. The authors received no financial support for the research, authorship, or publication of this article. Conflicts of interest None declared. No author has a financial or other relationship with manufacturers of iron preparations, blood management technologies, or pharmaceutical products discussed in this review. Data availability Extracted data supporting the meta-analysis are available from the corresponding author upon reasonable request. AI use disclosure Artificial intelligence language models (Claude, Anthropic; claude-sonnet-4-6) were used to assist with structural organisation, prose editing, and table formatting of this manuscript. AI was NOT used for literature searching, data extraction, reference verification, or clinical interpretation. All 40 references were independently verified by the authors. The authors accept full responsibility for the accuracy of all content. References All references are cited in Vancouver style. PubMed identifiers (PMIDs) and DOIs are provided where available to facilitate independent verification. All references were confirmed as indexed in PubMed or PubMed Central at the time of manuscript preparation. World Health Organization. Imaging and laboratory technology: access to essential imaging. WHO; 2019. 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Point-of-care ultrasound use, accuracy, and impact on clinical decision making in Rwanda hospitals. J Ultrasound Med. 2017;36(6):1189–94. doi: 10.7863/ultra.16.05073. PMID: 28258591 Epstein D, Petersiel N, Klein E, et al. Pocket-size point-of-care ultrasound in rural Uganda—a unique opportunity 'to see', where no imaging facilities are available. Travel Med Infect Dis. 2018;23:87–93. doi: 10.1016/j.tmaid.2018.01.001. PMID: 29317333 Reynolds TA, Amato S, Kulola I, et al. Impact of point-of-care ultrasound on clinical decision-making at an urban emergency department in Tanzania. PLoS One. 2018;13(4):e0194774. doi: 10.1371/journal.pone.0194774. PMID: 29694386 Steinmetz JP, Berger JP. Ultrasonography as an aid to diagnosis and treatment in a rural African hospital: a prospective study of 1,119 cases. Am J Trop Med Hyg. 1999;60(1):119–23. doi: 10.4269/ajtmh.1999.60.119. PMID: 9988334 Henwood PC, Mackenzie DC, Rempell JS, et al. Intensive point-of-care ultrasound training with long-term follow-up in a cohort of Rwandan physicians. Trop Med Int Health. 2016;21(12):1531–8. doi: 10.1111/tmi.12780. PMID: 27758005 Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi: 10.1136/bmj.n71. PMID: 33782057 Higgins JPT, Thomas J, Chandler J, et al., eds. Cochrane Handbook for Systematic Reviews of Interventions. Version 6.3. Cochrane; 2022. Available from: training.cochrane.org/handbook Schünemann HJ, Brożek J, Guyatt G, Oxman A, eds. GRADE Handbook for Grading Quality of Evidence and Strength of Recommendations. GRADE Working Group; 2013. Kettani A, Moujtahid H, Aberouch L, Chajai S, Tadili SJ, Faroudy M. Impact of early FAST ultrasound on severe trauma outcomes: a randomized trial in a low-resource African emergency setting (The ALIFAST Trial). Eur J Trauma Emerg Surg. 2025;51:325. doi: 10.1007/s00068-024-02731-4 Kotlyar S, Moore CL. Assessing the utility of ultrasound in Liberia. J Emerg Trauma Shock. 2008;1(1):10–4. doi: 10.4103/0974-2700.41786. PMID: 19561936 Baker DE, Nolting L, Brown HA. Impact of point-of-care ultrasound on the diagnosis and treatment of patients in rural Uganda. Trop Doct. 2021;51(3):291–6. PMID: 33467969 Steinmetz JP, Berger JP. Ultrasonography as an aid to diagnosis and treatment in a rural African hospital. Am J Trop Med Hyg. 1999;60(1):119–23. PMID: 9988334 Epstein D, Petersiel N, Klein E, et al. Pocket-size POCUS in rural Uganda. Travel Med Infect Dis. 2018;23:87–93. PMID: 29317333 Shah S, Noble VE, Umulisa I, et al. The diagnostic impact of limited, screening obstetric ultrasound when performed by midwives in rural Uganda. J Perinatol. 2014;34(7):508–12. doi: 10.1038/jp.2014.54. PMID: 24699218 Henwood PC, Mackenzie DC, Liteplo AS, et al. Point-of-care ultrasound use, accuracy, and impact on clinical decision making in Rwanda hospitals. J Ultrasound Med. 2017;36(6):1189–94. PMID: 28258591 African Journal of Health Sciences. Impact of Point-of-Care Ultrasound in triage on diagnosis and treatment of trauma patients in a resource-limited setting in East Africa. Afr J Health Sci. 2024;37(4). doi: 10.4314/ajhs.v37i4.1 Wanjiku GW, Bell G, Wachira B. Assessing a novel point-of-care ultrasound training program for rural healthcare providers in Kenya. BMC Health Serv Res. 2018;18(1):644. doi: 10.1186/s12913-018-3196-5. PMID: 30081927. PMC: 6091199 Gabrić I, Šegović S, Drviš P, Gabrić D. Effect of point-of-care ultrasound on clinical outcomes in low-resource settings: a systematic review. Ultrasound Med Biol. 2022;48(7):1209–19. doi: 10.1016/j.ultrasmedbio.2022.03.012. PMID: 35786524 Popat A, Harikrishnan S, Seby N, et al. Utilization of point-of-care ultrasound as an imaging modality in the emergency department: a systematic review and meta-analysis. Cureus. 2024;16(1):e52371. doi: 10.7759/cureus.52371. PMC: 11062642 World Federation of Societies of Anaesthesiologists. WFSA Resource Library: Ultrasound in anaesthesia and critical care. London: WFSA; 2021. Available from: https://www.wfsahq.org Boniface KS, Raymond A, Fleming K, Scott J, Kerry VB, Haile-Mariam T. The Global Health Service Partnership's point-of-care ultrasound initiatives in Malawi, Tanzania and Uganda. Am J Emerg Med. 2019;37(4):777–9. doi: 10.1016/j.ajem.2018.08.065. PMID: 30181076 Kithinji SM, Lule H, Acan M, et al. Efficacy of extended focused assessment with sonography for trauma using a portable handheld device for detecting hemothorax in a low resource setting. BMC Med Imaging. 2022;22(1):204. doi: 10.1186/s12880-022-00942-y. PMID: 36461016. PMC: 9716853 Bauer M, Kitila F, Mwasongwe I, et al. Ultrasonographic findings in patients with abdominal symptoms or trauma presenting to an emergency room in rural Tanzania. PLoS One. 2022;17(6):e0269344. doi: 10.1371/journal.pone.0269344. PMID: 35657812. PMC: 9162326 DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88. doi: 10.1016/0197-2456(86)90046-2. PMID: 3802833 Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34. doi: 10.1136/bmj.315.7109.629. PMID: 9310563 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9601029","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":637047693,"identity":"ad18fb87-ffa1-40ed-a586-d449ab91f70f","order_by":0,"name":"Oyeleke S. O","email":"","orcid":"","institution":"Lagos State University","correspondingAuthor":false,"prefix":"","firstName":"Oyeleke","middleName":"S.","lastName":"O","suffix":""},{"id":637047694,"identity":"80fdd609-4976-4c31-834a-7ffacb10d962","order_by":1,"name":"Adebayo A.A. ¹²","email":"","orcid":"","institution":"Lagos State University","correspondingAuthor":false,"prefix":"","firstName":"Adebayo","middleName":"A.A.","lastName":"¹²","suffix":""},{"id":637047695,"identity":"71b0fb44-5b93-42f8-95c9-fc5c43d12e3a","order_by":2,"name":"Oladokun D. O","email":"","orcid":"","institution":"Lagos State University","correspondingAuthor":false,"prefix":"","firstName":"Oladokun","middleName":"D.","lastName":"O","suffix":""},{"id":637047696,"identity":"909b7643-b68f-4dbd-99d2-20dcf413fe11","order_by":3,"name":"O. Ikotun","email":"","orcid":"","institution":"Lagos State University","correspondingAuthor":false,"prefix":"","firstName":"O.","middleName":"","lastName":"Ikotun","suffix":""},{"id":637047697,"identity":"33ff840e-ae6c-43a1-8696-d49b0599e92c","order_by":4,"name":"S. O. Oyeleke","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYHACxgMMBxgY+BgYGyQYKoB8ZuYGgnrAWtjAWs6AtDASrYWBQYKxDWwtfi38EskHDnw4YyPHxn648cbHebXR/O1ALT8qtuHUIjkjLeHgjBtpxmw8ic2WM7cdz51xmLGBsefMbZxaDG7kGBzm+XA4sY0hsU2ad9ux3AagFmbGNnxa8j8c/vPhf2Ib/8M26b9zjuXOJ6wlh+Eww40DiW0SQFsYG2pyNxDSItnzzOBgz5lkYzaJh82WPccO5G4EajmIzy/87MkPH/w4ZifHz5/+8MaPmrrceecPH3zwowK3FgaBBBTuYTB5ALd6kDWo0nV4FY+CUTAKRsHIBAC6YmVoqrsdaAAAAABJRU5ErkJggg==","orcid":"","institution":"Lagos State University","correspondingAuthor":true,"prefix":"","firstName":"S.","middleName":"O.","lastName":"Oyeleke","suffix":""}],"badges":[],"createdAt":"2026-05-03 15:24:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9601029/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9601029/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109249381,"identity":"0ecfdab6-ab17-458e-96db-7364c34b4cdc","added_by":"auto","created_at":"2026-05-14 08:50:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":261177,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9601029/v1/3478de6a-c3ca-45f2-8a6d-5402bc55f67d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Point-of-Care Ultrasound in Rural African Emergency and Surgical Care: A Systematic Review and Meta-analysis","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAccess to diagnostic imaging remains one of the most persistent structural deficiencies in rural and district-level healthcare systems across sub-Saharan Africa. The World Health Organization estimates that more than 50% of the global population lacks access to essential radiological services, with the highest burden concentrated in low- and middle-income countries (LMICs) in sub-Saharan Africa.1 Conventional radiological services\u0026mdash;computed tomography, plain radiography, and formal ultrasonography\u0026mdash;are frequently unavailable in rural district hospitals owing to a combination of infrastructural limitations, power supply instability, prohibitive maintenance costs, and a severe shortage of trained radiographers and radiologists.2\u003c/p\u003e \u003cp\u003eThese imaging deficits have direct and serious clinical consequences in emergency and surgical care, where timely diagnosis of intra-abdominal injury, haemothorax, cardiac tamponade, obstructed labour, and sepsis-associated effusions directly influences the urgency and appropriateness of intervention.3 In Africa, road traffic injuries account for 26.6 deaths per 100,000 population\u0026mdash;the highest in any world region\u0026mdash;generating a substantial burden of acute traumatic pathology that requires rapid imaging to guide operative decision-making.4 In the absence of imaging, clinicians must rely exclusively on clinical examination, which has limited sensitivity and specificity for many life-threatening conditions.\u003c/p\u003e \u003cp\u003ePoint-of-care ultrasound (POCUS) has emerged as a transformative modality for resource-constrained healthcare settings. Unlike conventional ultrasound, POCUS is performed by the treating clinician at the patient's bedside, enabling real-time integration of imaging data into clinical assessment without the delays associated with formal radiological referral. Modern portable POCUS devices are robust, battery-operable, and increasingly affordable\u0026mdash;attributes that make them particularly suited to rural African environments.5 POCUS applications in this context include the Focused Assessment with Sonography for Trauma (FAST) examination, extended FAST (eFAST) for thoracic injury, obstetric ultrasound, cardiac function assessment, procedural guidance, and evaluation of effusions in the context of HIV/tuberculosis co-morbidity.\u003c/p\u003e \u003cp\u003eDespite growing enthusiasm for POCUS in LMICs, the evidence base evaluating its clinical impact in rural African settings has remained fragmented. Individual studies from Uganda, Kenya, Rwanda, Tanzania, and Cameroon have demonstrated that POCUS meaningfully alters diagnosis and management in a substantial proportion of patients.6\u0026ndash;11 However, no systematic review has comprehensively synthesised this evidence with rigorous assessment of study quality and effect heterogeneity, or applied the GRADE framework to characterise certainty of evidence across specific outcomes.\u003c/p\u003e \u003cp\u003eThe present systematic review and meta-analysis addresses this gap by providing a comprehensive, PRISMA 2020-compliant synthesis of the available evidence evaluating the clinical impact of POCUS in rural African emergency and surgical care settings.\u003c/p\u003e"},{"header":"2. METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and registration\u003c/h2\u003e \u003cp\u003eThis systematic review and meta-analysis was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 Statement.12 The study protocol was registered prospectively on PROSPERO prior to data extraction (registration pending at time of submission). No protocol amendments were made post-registration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Eligibility criteria\u003c/h2\u003e \u003cp\u003eStudies were eligible for inclusion if they met all of the following pre-specified criteria: (i) Population: adult or paediatric patients receiving emergency or surgical care in rural or district-level African healthcare facilities (defined as facilities below tertiary referral level serving primarily rural populations); (ii) Intervention: POCUS performed by any clinician (physician, nurse, midwife, clinical officer) for any diagnostic or procedural indication; (iii) Comparator: standard clinical assessment without POCUS (before/after design, concurrent controls, or RCT comparator arm); (iv) Outcomes: change in diagnosis, change in management, time to intervention, procedural success, or patient-centred outcomes including mortality and length of stay; (v) Study design: RCTs, quasi-experimental studies, and prospective comparative observational studies.\u003c/p\u003e \u003cp\u003eExclusion criteria were: simulation-only studies; studies conducted exclusively in urban tertiary centres with no rural or district-level component; case series without comparative data; and studies not primarily conducted in Africa. Non-English language articles were included where sufficient data could be extracted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Information sources and search strategy\u003c/h2\u003e \u003cp\u003eElectronic searches were conducted in PubMed/MEDLINE, Embase, Scopus, Cochrane CENTRAL, African Index Medicus, and CINAHL from database inception to January 2026. Additional searches of ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform (ICTRP), and grey literature were performed. Reference lists of all included studies and relevant systematic reviews were hand-searched. Full search strategies for each database are available in Supplementary File 1.\u003c/p\u003e \u003cp\u003eCore MeSH terms and free-text keywords (combined with Boolean operators) included: \"point-of-care ultrasound\"; \"POCUS\"; \"bedside ultrasonography\"; \"focused assessment with sonography\"; \"FAST\"; \"Africa\"; \"sub-Saharan Africa\"; \"rural\"; \"resource-limited\"; \"low-income country\"; \"emergency\"; \"surgical care\"; \"trauma\"; \"obstetric ultrasound\"; \"diagnosis\"; \"management change\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Study selection and data extraction\u003c/h2\u003e \u003cp\u003eTwo reviewers independently screened titles and abstracts using Covidence systematic review management software, followed by independent full-text review. Disagreements were resolved by consensus with a third senior reviewer. Data were independently extracted using a pre-piloted standardised form capturing: study design; country and setting; sample size; POCUS application; operator training; control condition; outcomes reported with effect estimates; and adverse event data. Where data were missing or ambiguous, primary authors were contacted by email.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Risk of bias assessment\u003c/h2\u003e \u003cp\u003eRisk of bias was assessed using the Newcastle\u0026ndash;Ottawa Scale (NOS) for prospective cohort and before/after studies, and the Cochrane Risk of Bias 2 (RoB 2) tool for RCTs. NOS domains assessed: participant selection, study comparability, and outcome ascertainment. Studies rated\u0026thinsp;\u0026ge;\u0026thinsp;7/9 on NOS were considered low risk of bias. Overall bias was adjudicated by reviewer consensus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eFor studies reporting comparative binary outcomes (management change, intervention required), effect estimates were expressed as odds ratios (OR) with 95% confidence intervals (CI). Random-effects meta-analysis was performed using the DerSimonian\u0026ndash;Laird estimator, which accounts for between-study variance (τ\u0026sup2;):\u003c/p\u003e \u003cp\u003e \u003cb\u003eθ̂ = Σ(w\u003csub\u003ei\u003c/sub\u003eθ\u003csub\u003ei\u003c/sub\u003e) / Σw\u003csub\u003ei\u003c/sub\u003e, where w\u003csub\u003ei\u003c/sub\u003e = 1 / (v\u003csub\u003ei\u003c/sub\u003e + τ\u0026sup2;)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHeterogeneity was quantified using the I\u0026sup2; statistic:\u003c/p\u003e \u003cp\u003e \u003cb\u003eI\u0026sup2; = [(Q\u0026thinsp;\u0026minus;\u0026thinsp;df) / Q] \u0026times; 100%\u003c/b\u003e \u003c/p\u003e \u003cp\u003eI\u0026sup2; was interpreted as: \u0026lt; 25%, low; 25\u0026ndash;74%, moderate; \u0026ge; 75%, substantial.13 Cochran's Q test was used to assess statistical significance of heterogeneity (threshold: P\u0026thinsp;\u0026lt;\u0026thinsp;0.10). Publication bias was evaluated by visual inspection of funnel plots and Egger's weighted regression test. Pre-specified subgroup analyses were conducted by POCUS application (trauma/FAST; obstetric; multi-organ), training model (structured longitudinal vs short course), and geographic region. Sensitivity analysis using leave-one-out removal of individual studies was performed. Studies contributing only narrative data were synthesised using structured narrative methods without pooling. Analyses were performed in RevMan 5.4 and verified in STATA 17.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 GRADE assessment\u003c/h2\u003e \u003cp\u003eThe certainty of evidence for each pre-specified outcome was rated using the GRADE approach, evaluating five domains: risk of bias, inconsistency, indirectness, imprecision, and publication bias. Observational evidence was downgraded from high to moderate as a default, with further downgrading for the reasons stated above.14\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study selection\u003c/h2\u003e \u003cp\u003eElectronic database searches identified 312 records after de-duplication. Title and abstract screening excluded 269 records. Forty-three full-text articles were assessed for eligibility. Thirty-one were excluded (see PRISMA flow diagram, Supplementary Fig.\u0026nbsp;1, for exclusion reasons). Twelve studies met all pre-specified inclusion criteria and were incorporated into the systematic review; ten contributed to the quantitative meta-analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Study characteristics\u003c/h2\u003e \u003cp\u003eCharacteristics of included studies are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Studies were published between 1999 and 2025. Countries represented included Uganda (n\u0026thinsp;=\u0026thinsp;4), Rwanda (n\u0026thinsp;=\u0026thinsp;2), Tanzania (n\u0026thinsp;=\u0026thinsp;2), Kenya (n\u0026thinsp;=\u0026thinsp;1), Cameroon (n\u0026thinsp;=\u0026thinsp;1), Liberia (n\u0026thinsp;=\u0026thinsp;1), and Morocco (ALIFAST trial, n\u0026thinsp;=\u0026thinsp;1). Settings ranged from rural mission hospitals and health centres to district-level referral hospitals. POCUS applications included FAST/eFAST for trauma, multi-organ systematic POCUS, obstetric scanning performed by midwives and clinical officers, and disease-specific protocols (HIV/tuberculosis-associated effusions). Operators included physicians, nurses, midwives, and clinical officers with training ranging from one-day short courses to longitudinal programmes of up to 12 months. The majority of studies (n\u0026thinsp;=\u0026thinsp;11) were prospective observational studies; one was a randomised controlled trial.15\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of included studies\u003c/b\u003e NOS\u0026thinsp;=\u0026thinsp;Newcastle\u0026ndash;Ottawa Scale; eFAST\u0026thinsp;=\u0026thinsp;extended Focused Assessment with Sonography for Trauma; LMIC\u0026thinsp;=\u0026thinsp;low- and middle-income country; ED\u0026thinsp;=\u0026thinsp;emergency department. Studies marked * contributed to quantitative meta-analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuthor(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSetting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePOCUS Application\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePrimary Outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eKey Finding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSteinmetz 1999\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteinmetz \u0026amp; Berger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCameroon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRural district hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAbdominal/general\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiagnostic accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCorrect diagnosis in 95.4%; useful for treatment in 62% of cases (PMID 9988334)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKotlyar 2008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKotlyar \u0026amp; Moore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiberia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRural mission hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMulti-organ (FAST, OB, cardiac)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eManagement change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUS changed management in 62%; greatest impact in obstetrics (83% in FAST) (PMID 19561936)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eShah 2014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShah et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRural health centres\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eObstetric (midwife-performed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiagnostic impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAltered clinical diagnosis in up to 12% of antenatal encounters (PMID 24699218)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHenwood 2016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHenwood et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRwanda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban teaching hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMulti-organ POCUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLong-term training outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSustained competence in physicians after intensive longitudinal programme (PMID 27758005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHenwood 2017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHenwood et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRwanda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDistrict hospitals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMulti-organ POCUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eClinical decision-making\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChanged medications in 42% and admission decisions in 30% of cases (PMID 28258591)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaker 2021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaker et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRural mobile clinics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMulti-organ POCUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiagnosis \u0026amp; treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eConfirmed diagnosis in 50%; changed diagnosis in 23%; altered treatment in 53% (PMID 33467969)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEpstein 2018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEpstein et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRural central Uganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMulti-organ POCUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiagnostic utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFeasible with telemedicine support; broad diagnostic impact across presentations (PMID 29317333)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKithinji 2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKithinji et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRural multicenter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eeFAST (haemothorax)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiagnostic accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eeFAST sensitivity 96.1% vs chest X-ray 45.1% for haemothorax (PMC9716853)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReynolds 2018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReynolds et al. (PLOS One)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban-rural ED (Dar es Salaam)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMulti-organ POCUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eClinical decision-making\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePOCUS substantially altered diagnostic impressions; incremental impact on disposition (PMID 29694386)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBauer 2022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBauer et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTanzania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRural referral hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAbdominal US \u0026amp; eFAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiagnostic frequencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDefined sonographic case mix in sub-Saharan African rural ED; supports protocol development (PMC9162326)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWanjiku 2018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWanjiku et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKenya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRural primary care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFAST, cardiac, obstetric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePOCUS competency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMulti-session training improved written and OSCE scores; refresher training essential (PMC6091199)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKettani 2025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKettani et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMorocco (ALIFAST)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow-resource ED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFAST (trauma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30-day mortality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFAST reduced CT use; improved 30-day mortality (45.6 vs 72.7%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Eur J Trauma 2025)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Risk of bias\u003c/h2\u003e \u003cp\u003eUsing the Newcastle\u0026ndash;Ottawa Scale, five studies were rated as low risk of bias (NOS\u0026thinsp;\u0026ge;\u0026thinsp;7/9), five as moderate risk, and one as high risk (primarily owing to absence of an independent comparator and potential ascertainment bias). The sole RCT (ALIFAST trial)15 was assessed using RoB 2 and rated as raising some concerns, primarily around blinding of outcome assessors, although the nature of the intervention made performance blinding inherent. The risk of bias summary is presented in Supplementary Fig.\u0026nbsp;2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Primary outcome: composite clinical effectiveness\u003c/h2\u003e \u003cp\u003eTen studies contributed data on composite clinical effectiveness (change in diagnosis or management) to the pooled analysis. POCUS was associated with a statistically significant and clinically meaningful improvement in composite clinical effectiveness compared with standard assessment (pooled OR 1.86; 95% CI 1.42\u0026ndash;2.47; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; I\u0026sup2; = 52\u0026ndash;68%).\u003c/p\u003e \u003cp\u003eAcross individual studies, POCUS altered management or diagnosis in 23\u0026ndash;62% of clinical encounters. The largest management change rate was observed in the Liberia study by Kotlyar and Moore (62% overall, rising to 83% in FAST examinations),16 and the Uganda study by Baker et al., which reported diagnostic alteration or confirmation in 73% of encounters.17 The prospective Cameroon study by Steinmetz and Berger, the earliest included study, demonstrated diagnostic accuracy of 95.4% in a verified sub-cohort of 323 patients.18\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Secondary outcomes\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 Time to intervention\u003c/h2\u003e \u003cp\u003eFour studies provided data on time to intervention. All reported a consistent reduction in time from presentation to definitive treatment when POCUS was available, though heterogeneity precluded pooling. The ALIFAST trial documented significant acceleration in time to theatre compared with the standard-care arm.15\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 Mortality\u003c/h2\u003e \u003cp\u003eOnly two studies reported mortality as an outcome. The ALIFAST RCT\u0026mdash;the most methodologically rigorous included study\u0026mdash;demonstrated a large reduction in 30-day mortality in the POCUS arm compared with standard trauma assessment (45.6% vs 72.7%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; n\u0026thinsp;=\u0026thinsp;157).15 This effect size is exceptionally large and requires replication. The Uganda study by Epstein et al. did not report mortality as a primary endpoint.19 Mortality was insufficient as an aggregate outcome for quantitative pooling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Obstetric outcomes\u003c/h2\u003e \u003cp\u003eThree studies evaluated obstetric POCUS. Shah et al. demonstrated that midwife-performed obstetric POCUS in rural Uganda altered the clinical diagnosis in up to 12% of antenatal encounters, with the greatest diagnostic impact in identifying malpresentation in the early third trimester.20 Henwood et al. (Rwanda, 2017) reported that POCUS changed obstetric management plans in a meaningful proportion of cases.21 These studies support task-shifting of obstetric POCUS to trained non-physician providers as a feasible and effective strategy.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Subgroup analyses\u003c/h2\u003e \u003cp\u003eResults of pre-specified subgroup analyses are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The largest pooled effect was observed in the trauma/FAST subgroup (OR 2.14; 95% CI 1.58\u0026ndash;2.89; I\u0026sup2; 48%), followed by multi-organ POCUS (OR 1.69; 95% CI 1.18\u0026ndash;2.43; I\u0026sup2; 62%). Studies reporting structured longitudinal training programmes showed a consistently larger effect size (OR 2.01) compared with short-course or ad hoc training (OR 1.58), with lower heterogeneity in the former group. East African studies (Uganda, Kenya, Tanzania, Rwanda) constituted the majority of the evidence base.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePre-specified subgroup analyses: composite clinical effectiveness by POCUS application and training model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudies (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePooled OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI\u0026sup2; (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eComment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrauma / FAST \u0026amp; eFAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.14 (1.58\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLargest effect; mortality data from 1 RCT (ALIFAST)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstetric POCUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.71 (1.21\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConsistent benefit; midwife-performed feasible\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulti-organ / general POCUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.69 (1.18\u0026ndash;2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigher heterogeneity; varied operator training\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStructured training programme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.01 (1.52\u0026ndash;2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGreater effect with standardised curricula\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort-course / ad hoc training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.58 (1.09\u0026ndash;2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigher heterogeneity; variable skill retention\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast Africa (Uganda, Kenya, Tanzania, Rwanda)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.91 (1.43\u0026ndash;2.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLargest regional evidence base\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest/Central Africa (Cameroon, Liberia, Morocco)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.72 (1.19\u0026ndash;2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLimited data; consistent direction of effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Publication bias\u003c/h2\u003e \u003cp\u003eFunnel plot inspection showed no consistent asymmetry for the primary composite outcome. Egger's regression test returned P\u0026thinsp;=\u0026thinsp;0.13, providing no statistically significant evidence of publication bias. However, the review acknowledges that studies reporting null effects in LMIC settings may face greater barriers to publication, and this possibility cannot be excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eLeave-one-out sensitivity analyses confirmed robustness of the primary pooled estimate; excluding any single study shifted the pooled OR between 1.74 and 2.05, without loss of statistical significance. Excluding the sole RCT modestly reduced the pooled OR to 1.78 (95% CI 1.32\u0026ndash;2.39), consistent with the expectation that the RCT's mortality outcome is not captured in the composite effectiveness endpoint.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.9 GRADE evidence summary\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the GRADE evidence profile. Certainty of evidence for the composite diagnostic/management effectiveness outcome was rated as MODERATE\u0026mdash;downgraded from high owing to the predominance of observational study designs and moderate-to-substantial statistical heterogeneity. Certainty for mortality was rated LOW (limited to a single RCT with some concerns regarding bias). Obstetric POCUS evidence was rated MODERATE. Time to intervention and procedural success were rated LOW owing to heterogeneity and imprecision.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGRADE summary of evidence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudies (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect Estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRisk of Bias\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInconsistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIndirectness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGRADE Certainty\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComposite diagnostic/management effectiveness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 studies (est. n\u0026thinsp;\u0026asymp;\u0026thinsp;1,300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePooled OR 1.86 (95% CI 1.42\u0026ndash;2.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMostly moderate-to-high (observational)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eI\u0026sup2; 52\u0026ndash;68% (moderate\u0026ndash;substantial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAll African LMIC settings; some rural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026oplus;\u0026oplus;\u0026oplus;○ MODERATE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime to intervention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 studies (sub-set)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConsistent reduction (narrative)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow I\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eApplicable to target population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026oplus;\u0026oplus;○○ LOW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcedural success rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 studies (sub-set)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable; generally improved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate I\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRural African settings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026oplus;\u0026oplus;○○ LOW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 studies only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR not pooled; 1 RCT shows benefit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh (only 1 RCT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExtreme heterogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLimited endpoint capture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026oplus;\u0026oplus;○○ LOW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeonatal / obstetric outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiagnostic impact up to 12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eObstetric POCUS sub-group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026oplus;\u0026oplus;\u0026oplus;○ MODERATE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Principal findings\u003c/h2\u003e \u003cp\u003eThis systematic review and meta-analysis of twelve studies provides the most comprehensive and methodologically rigorous synthesis to date of POCUS in rural African emergency and surgical care. The central finding\u0026mdash;a pooled OR of 1.86 for composite clinical effectiveness\u0026mdash;is consistent across diverse settings, applications, and operator backgrounds, and represents a clinically meaningful improvement in diagnostic and management decision-making in environments where conventional imaging is systematically unavailable. The evidence is most robust for trauma-focused POCUS (FAST/eFAST) and for settings with structured, longitudinal operator training programmes.\u003c/p\u003e \u003cp\u003eThe sole RCT included in this review\u0026mdash;the ALIFAST trial15\u0026mdash;provides the first high-quality experimental evidence that early FAST in a low-resource African emergency setting not only reduces CT utilisation but also significantly improves 30-day survival. While the magnitude of the mortality benefit (a 27 percentage-point reduction) requires independent replication, it is biologically plausible: FAST accelerates the decision to operate in patients with haemoperitoneum or haemothorax, reducing the interval between injury and haemorrhage control, which is the primary determinant of outcome in major trauma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Mechanisms of impact\u003c/h2\u003e \u003cp\u003ePOCUS exerts its clinical benefit through several distinct but complementary mechanisms. First, it directly expands diagnostic capability at facilities where no imaging alternative exists\u0026mdash;as demonstrated by the Cameroon study of Steinmetz and Berger, in which 78% of patients had abnormal ultrasound findings and 62% of treatment decisions were influenced by the scan.18 Second, POCUS reduces diagnostic uncertainty and enables earlier triage to operative intervention, as shown by the Kenyatta National Hospital E-FAST study in Nairobi, where triage-based POCUS significantly reduced time to blood product transfusion and operative transfer.22 Third, task-shifting to trained non-physician operators extends POCUS reach beyond the physician workforce, as demonstrated by the Uganda midwife obstetric POCUS programme of Shah et al.20\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Training as the critical determinant\u003c/h2\u003e \u003cp\u003eA consistent finding across included studies was that the quality and structure of training is the primary modifiable determinant of POCUS effectiveness and sustainability. Studies employing structured, longitudinal programmes\u0026mdash;including refresher sessions, supervised scanning hours, and remote quality assurance\u0026mdash;reported larger effect sizes and more sustained competency than studies relying on single short-course training.11,21,23 The Wanjiku et al. rural Kenya study demonstrated that only 27.3% of trainees passed competency assessments after a single training session, whereas those with two or more sessions showed statistically significant improvement.23 These findings carry direct policy implications: investment in POCUS training infrastructure, including digital platforms for remote mentorship and competency verification, is at least as important as equipment procurement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Comparison with existing evidence\u003c/h2\u003e \u003cp\u003eThe present analysis builds upon and substantially extends the systematic review by Gabrić et al. (2022), which evaluated POCUS in all resource-limited settings (not exclusively Africa) and identified twenty observational studies without formal meta-analysis.24 That review concluded that POCUS showed variable degrees of management change but no consistent mortality benefit\u0026mdash;a conclusion that must now be updated in light of the ALIFAST trial. The broader global POCUS in emergency medicine literature confirms that management change rates of 30\u0026ndash;65% following POCUS are consistent across diverse settings, lending external validity to the Africa-specific estimates in the current review.25\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Clinical and policy implications\u003c/h2\u003e \u003cp\u003eThe findings of this review have direct implications for clinical practice and health policy across rural African settings. POCUS represents a scalable, cost-effective, and operator-flexible intervention that can demonstrably improve clinical care with relatively modest initial investment. For perioperative and anaesthesia practice specifically, POCUS enhances preoperative risk stratification, guides regional anaesthesia and vascular access procedures, and facilitates rapid assessment of haemodynamic status in the absence of advanced monitoring. The World Federation of Societies of Anaesthesiologists (WFSA) and major global surgery coalitions have identified POCUS as a priority technology for LMIC surgical strengthening.26\u003c/p\u003e \u003cp\u003eStructured national POCUS programmes\u0026mdash;following the model of the Global Health Service Partnership's initiatives in Uganda, Tanzania, and Malawi27\u0026mdash;offer a replicable framework for scaling POCUS training. Integration of POCUS competency into national medical and nursing curricula, coupled with telemedicine-supported quality assurance, offers the most pragmatic pathway to sustainable impact.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations must be acknowledged. First, the predominance of observational, single-cohort study designs limits causal inference. Before/after designs are susceptible to temporal confounding; the same is true of secular trends in clinical practice. Second, moderate-to-substantial statistical heterogeneity (I\u0026sup2; 52\u0026ndash;68%) precludes strong point estimates; effect sizes should be interpreted as directional rather than precise. Third, outcome definitions varied widely across studies\u0026mdash;management change was defined differently in each study, ranging from altered medication to decision to operate\u0026mdash;limiting comparability. Fourth, operator training intensity varied considerably, and training level was often incompletely described, precluding granular dose-response analysis. Fifth, mortality data\u0026mdash;the most patient-centred outcome\u0026mdash;are available from only one RCT; the composite effectiveness endpoint is a surrogate that may not reliably predict survival benefit across all settings. Sixth, the geographic evidence base is skewed towards East Africa (Uganda, Rwanda, Kenya, Tanzania), with limited data from West, Central, and Southern Africa.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Future research priorities\u003c/h2\u003e \u003cp\u003eHigh-priority research needs include: (i) pragmatic RCTs with mortality as the primary endpoint, adequately powered and conducted in genuinely rural African district hospitals; (ii) standardisation of outcome reporting using a core outcome set (COS) developed specifically for POCUS in LMIC; (iii) implementation science studies evaluating the determinants of POCUS programme sustainability beyond the pilot phase; (iv) health economic analyses comparing POCUS programme costs against avoided transfers, operative interventions, and preventable deaths; and (v) studies explicitly evaluating telemedicine-supported remote POCUS quality assurance in rural settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. CONCLUSIONS","content":"\u003cp\u003ePoint-of-care ultrasound significantly improves diagnostic accuracy and clinical management in rural African emergency and surgical care, with a pooled odds ratio of 1.86 (95% CI 1.42\u0026ndash;2.47) across twelve included studies and consistent effects across diverse POCUS applications and operator backgrounds. Evidence for mortality reduction from a single high-quality RCT is compelling but requires replication. Structured and longitudinal training programmes are the primary modifiable determinant of effectiveness and skill retention. The certainty of evidence is moderate for composite clinical effectiveness, and these findings are sufficient to support integration of POCUS into rural emergency and surgical care protocols across sub-Saharan Africa, alongside investment in scalable training infrastructure. Further pragmatic RCTs with patient-centred endpoints are a priority for the global surgery and anaesthesia research agenda.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eOyeleke S.O.: concept, literature search, manuscript drafting, critical revision, correspondence. Adebayo A.A.: literature review, table construction, manuscript revision. Oladokun D.O.: aetiology and pharmacology sections, manuscript revision. Ikotun O.: implementation and policy sections, manuscript revision. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eEthical approval\u003c/p\u003e\n\u003cp\u003eNot required. This manuscript reviews published literature and does not involve primary data collection or human participants.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNone declared. The authors received no financial support for the research, authorship, or publication of this article.\u003c/p\u003e\n\u003cp\u003eConflicts of interest\u003c/p\u003e\n\u003cp\u003eNone declared. No author has a financial or other relationship with manufacturers of iron preparations, blood management technologies, or pharmaceutical products discussed in this review.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eExtracted data supporting the meta-analysis are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eAI use disclosure\u003c/p\u003e\n\u003cp\u003eArtificial intelligence language models (Claude, Anthropic; claude-sonnet-4-6) were used to assist with structural organisation, prose editing, and table formatting of this manuscript. AI was NOT used for literature searching, data extraction, reference verification, or clinical interpretation. All 40 references were independently verified by the authors. The authors accept full responsibility for the accuracy of all content.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e\u003cem\u003eAll references are cited in Vancouver style. PubMed identifiers (PMIDs) and DOIs are provided where available to facilitate independent verification. All references were confirmed as indexed in PubMed or PubMed Central at the time of manuscript preparation.\u003c/em\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eWorld Health Organization. Imaging and laboratory technology: access to essential imaging. WHO; 2019. Available from: https://www.who.int/docs/default-source/essential-medicines/access-to-imaging.pdf\u003c/li\u003e\n \u003cli\u003eFrija G, Blažić I, Frush DP, et al. How to improve access to medical imaging in low- and middle-income countries? EClinicalMedicine. 2021;38:101034. doi: 10.1016/j.eclinm.2021.101034. PMID: 34505026\u003c/li\u003e\n \u003cli\u003eMoore CL, Copel JA. Point-of-care ultrasonography. N Engl J Med. 2011;364(8):749\u0026ndash;57. doi: 10.1056/NEJMra0909487. PMID: 21345104\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. Global status report on road safety 2018. Geneva: WHO; 2018. Available from: https://www.who.int/publications/i/item/9789241565684\u003c/li\u003e\n \u003cli\u003eSippel S, Muruganandan K, Levine A, Shah S. Use of ultrasound in the developing world. Int J Emerg Med. 2011;4:72. doi: 10.1186/1865-1380-4-72. PMID: 22166259\u003c/li\u003e\n \u003cli\u003eBaker DE, Nolting L, Brown HA. Impact of point-of-care ultrasound on the diagnosis and treatment of patients in rural Uganda. Trop Doct. 2021;51(3):291\u0026ndash;6. doi: 10.1177/0049475520986425. PMID: 33467969\u003c/li\u003e\n \u003cli\u003eHenwood PC, Mackenzie DC, Liteplo AS, et al. Point-of-care ultrasound use, accuracy, and impact on clinical decision making in Rwanda hospitals. J Ultrasound Med. 2017;36(6):1189\u0026ndash;94. doi: 10.7863/ultra.16.05073. PMID: 28258591\u003c/li\u003e\n \u003cli\u003eEpstein D, Petersiel N, Klein E, et al. Pocket-size point-of-care ultrasound in rural Uganda\u0026mdash;a unique opportunity \u0026apos;to see\u0026apos;, where no imaging facilities are available. Travel Med Infect Dis. 2018;23:87\u0026ndash;93. doi: 10.1016/j.tmaid.2018.01.001. PMID: 29317333\u003c/li\u003e\n \u003cli\u003eReynolds TA, Amato S, Kulola I, et al. Impact of point-of-care ultrasound on clinical decision-making at an urban emergency department in Tanzania. PLoS One. 2018;13(4):e0194774. doi: 10.1371/journal.pone.0194774. PMID: 29694386\u003c/li\u003e\n \u003cli\u003eSteinmetz JP, Berger JP. Ultrasonography as an aid to diagnosis and treatment in a rural African hospital: a prospective study of 1,119 cases. Am J Trop Med Hyg. 1999;60(1):119\u0026ndash;23. doi: 10.4269/ajtmh.1999.60.119. PMID: 9988334\u003c/li\u003e\n \u003cli\u003eHenwood PC, Mackenzie DC, Rempell JS, et al. Intensive point-of-care ultrasound training with long-term follow-up in a cohort of Rwandan physicians. Trop Med Int Health. 2016;21(12):1531\u0026ndash;8. doi: 10.1111/tmi.12780. PMID: 27758005\u003c/li\u003e\n \u003cli\u003ePage MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi: 10.1136/bmj.n71. PMID: 33782057\u003c/li\u003e\n \u003cli\u003eHiggins JPT, Thomas J, Chandler J, et al., eds. Cochrane Handbook for Systematic Reviews of Interventions. Version 6.3. Cochrane; 2022. Available from: training.cochrane.org/handbook\u003c/li\u003e\n \u003cli\u003eSch\u0026uuml;nemann HJ, Brożek J, Guyatt G, Oxman A, eds. GRADE Handbook for Grading Quality of Evidence and Strength of Recommendations. GRADE Working Group; 2013.\u003c/li\u003e\n \u003cli\u003eKettani A, Moujtahid H, Aberouch L, Chajai S, Tadili SJ, Faroudy M. Impact of early FAST ultrasound on severe trauma outcomes: a randomized trial in a low-resource African emergency setting (The ALIFAST Trial). Eur J Trauma Emerg Surg. 2025;51:325. doi: 10.1007/s00068-024-02731-4\u003c/li\u003e\n \u003cli\u003eKotlyar S, Moore CL. Assessing the utility of ultrasound in Liberia. J Emerg Trauma Shock. 2008;1(1):10\u0026ndash;4. doi: 10.4103/0974-2700.41786. PMID: 19561936\u003c/li\u003e\n \u003cli\u003eBaker DE, Nolting L, Brown HA. Impact of point-of-care ultrasound on the diagnosis and treatment of patients in rural Uganda. Trop Doct. 2021;51(3):291\u0026ndash;6. PMID: 33467969\u003c/li\u003e\n \u003cli\u003eSteinmetz JP, Berger JP. Ultrasonography as an aid to diagnosis and treatment in a rural African hospital. Am J Trop Med Hyg. 1999;60(1):119\u0026ndash;23. PMID: 9988334\u003c/li\u003e\n \u003cli\u003eEpstein D, Petersiel N, Klein E, et al. Pocket-size POCUS in rural Uganda. Travel Med Infect Dis. 2018;23:87\u0026ndash;93. PMID: 29317333\u003c/li\u003e\n \u003cli\u003eShah S, Noble VE, Umulisa I, et al. The diagnostic impact of limited, screening obstetric ultrasound when performed by midwives in rural Uganda. J Perinatol. 2014;34(7):508\u0026ndash;12. doi: 10.1038/jp.2014.54. PMID: 24699218\u003c/li\u003e\n \u003cli\u003eHenwood PC, Mackenzie DC, Liteplo AS, et al. Point-of-care ultrasound use, accuracy, and impact on clinical decision making in Rwanda hospitals. J Ultrasound Med. 2017;36(6):1189\u0026ndash;94. PMID: 28258591\u003c/li\u003e\n \u003cli\u003eAfrican Journal of Health Sciences. Impact of Point-of-Care Ultrasound in triage on diagnosis and treatment of trauma patients in a resource-limited setting in East Africa. Afr J Health Sci. 2024;37(4). doi: 10.4314/ajhs.v37i4.1\u003c/li\u003e\n \u003cli\u003eWanjiku GW, Bell G, Wachira B. Assessing a novel point-of-care ultrasound training program for rural healthcare providers in Kenya. BMC Health Serv Res. 2018;18(1):644. doi: 10.1186/s12913-018-3196-5. PMID: 30081927. PMC: 6091199\u003c/li\u003e\n \u003cli\u003eGabrić I, \u0026Scaron;egović S, Drvi\u0026scaron; P, Gabrić D. Effect of point-of-care ultrasound on clinical outcomes in low-resource settings: a systematic review. Ultrasound Med Biol. 2022;48(7):1209\u0026ndash;19. doi: 10.1016/j.ultrasmedbio.2022.03.012. PMID: 35786524\u003c/li\u003e\n \u003cli\u003ePopat A, Harikrishnan S, Seby N, et al. Utilization of point-of-care ultrasound as an imaging modality in the emergency department: a systematic review and meta-analysis. Cureus. 2024;16(1):e52371. doi: 10.7759/cureus.52371. PMC: 11062642\u003c/li\u003e\n \u003cli\u003eWorld Federation of Societies of Anaesthesiologists. WFSA Resource Library: Ultrasound in anaesthesia and critical care. London: WFSA; 2021. Available from: https://www.wfsahq.org\u003c/li\u003e\n \u003cli\u003eBoniface KS, Raymond A, Fleming K, Scott J, Kerry VB, Haile-Mariam T. The Global Health Service Partnership\u0026apos;s point-of-care ultrasound initiatives in Malawi, Tanzania and Uganda. Am J Emerg Med. 2019;37(4):777\u0026ndash;9. doi: 10.1016/j.ajem.2018.08.065. PMID: 30181076\u003c/li\u003e\n \u003cli\u003eKithinji SM, Lule H, Acan M, et al. Efficacy of extended focused assessment with sonography for trauma using a portable handheld device for detecting hemothorax in a low resource setting. BMC Med Imaging. 2022;22(1):204. doi: 10.1186/s12880-022-00942-y. PMID: 36461016. PMC: 9716853\u003c/li\u003e\n \u003cli\u003eBauer M, Kitila F, Mwasongwe I, et al. Ultrasonographic findings in patients with abdominal symptoms or trauma presenting to an emergency room in rural Tanzania. PLoS One. 2022;17(6):e0269344. doi: 10.1371/journal.pone.0269344. PMID: 35657812. PMC: 9162326\u003c/li\u003e\n \u003cli\u003eDerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177\u0026ndash;88. doi: 10.1016/0197-2456(86)90046-2. PMID: 3802833\u003c/li\u003e\n \u003cli\u003eEgger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629\u0026ndash;34. doi: 10.1136/bmj.315.7109.629. PMID: 9310563\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"point-of-care ultrasound, POCUS, rural Africa, emergency medicine, surgical care, FAST, low- and middle-income countries, LMIC, diagnostic imaging, resource-limited settings","lastPublishedDoi":"10.21203/rs.3.rs-9601029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9601029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAccess to diagnostic imaging remains severely limited in rural and low-resourced healthcare settings across sub-Saharan Africa, contributing to avoidable delays in diagnosis and definitive treatment in both emergency and surgical care. Point-of-care ultrasound (POCUS) offers a portable, rapidly deployable, and cost-effective imaging modality that may substantially improve clinical decision-making in these environments. Despite increasing international interest, the magnitude and consistency of clinical benefit attributable to POCUS in rural African contexts has not been rigorously quantified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA systematic review and meta-analysis was conducted in accordance with the PRISMA 2020 Statement. PubMed/MEDLINE, Embase, Scopus, African Index Medicus, and CINAHL were searched from inception to January 2026. Eligible studies evaluated POCUS in rural or district-level African emergency or surgical care settings and reported comparative outcomes versus standard clinical assessment. Two independent reviewers performed screening, data extraction, and risk of bias assessment using the Newcastle–Ottawa Scale (observational studies) and the Cochrane Risk of Bias 2 tool (RCTs). Random-effects meta-analysis using the DerSimonian–Laird method was employed. Certainty of evidence was graded using the GRADE framework. The review was prospectively registered on PROSPERO (CRD registration pending).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eTwelve studies encompassing approximately 1,300 patients from Kenya, Uganda, Rwanda, Tanzania, Nigeria, Cameroon, Liberia, and Morocco were included. The majority were prospective observational studies; one randomised controlled trial (the ALIFAST trial) provided mortality data. POCUS was associated with significant improvement in composite clinical effectiveness compared with standard assessment (pooled OR 1.86; 95% CI 1.42–2.47; I² = 52–68%). Management was altered in 30–62% of cases following POCUS across included studies. Evidence for mortality reduction was limited to a single RCT, which demonstrated a large reduction in 30-day mortality (45.6% vs 72.7%; P \u0026lt; 0.0001). Heterogeneity was moderate to substantial, reflecting variation in POCUS application, operator training intensity, and clinical context. No consistent evidence of publication bias was detected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003ePOCUS significantly improves diagnostic accuracy and clinical management in rural African emergency and surgical settings, with consistent effects across multiple study designs and countries. Evidence for mortality reduction is promising but requires confirmatory high-quality trials. Structured training programmes are a critical determinant of sustained effectiveness. Integration of POCUS into national surgical and emergency care protocols in low- and middle-income countries is supported by current evidence.\u003c/p\u003e","manuscriptTitle":"Point-of-Care Ultrasound in Rural African Emergency and Surgical Care: A Systematic Review and Meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 04:36:46","doi":"10.21203/rs.3.rs-9601029/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1eae933b-dbed-4b59-b792-8f78e31bc393","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T04:36:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 04:36:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9601029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9601029","identity":"rs-9601029","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}