Integrated Multimodal Diagnostic Validation in Hematological Disorders: First Prospective Study from a Conflict-Affected, Resource-Limited Setting in Yemen

doi:10.21203/rs.3.rs-8168085/v1

Background and objectives:

Hematological disorders pose significant diagnostic challenges, particularly in conflict-affected, resource-limited regions like Yemen, leading to delays, misdiagnoses, and suboptimal patient care. This study aimed to prospectively validate an integrated multimodal diagnostic approach for hematological disorders in Yemen, assessing its diagnostic accuracy, clinical utility, prognostic value, and cost-effectiveness compared to conventional morphology-based diagnosis.

Methods

A prospective study enrolled 420 patients with suspected hematological disorders. A comprehensive algorithm, integrating peripheral blood film morphology, flow cytometry, molecular genetics, bone marrow trephine biopsy with immunohistochemistry, and targeted ancillary tests, was employed. The final integrated diagnosis, established by a multidisciplinary team, served as the gold standard. Diagnostic accuracy, agreement, malignancy prediction, survival analysis, and cost-effectiveness were assessed.

Results

The cohort showed a diverse spectrum, predominantly malignancies. PBF achieved 87.5% overall accuracy but had low sensitivity (41.5%) for lymphoma infiltration, highlighting its limitations. The integrated approach achieved 96.8% combined diagnostic accuracy. Flow cytometry was crucial for acute leukemias, chronic lymphocytic leukemia, and lymphomas. Molecular testing confirmed BCR-ABL1 in 94% of CML and JAK2 V617F in 89% of BCR-ABL1-negative myeloproliferative neoplasms. Bone marrow trephine biopsy combined with IHC provided 100% definitive diagnosis for lymphoma. Prognostic factors included age > 40 years, hemoglobin < 8 g/dL, WBC > 50,000/µL, platelet < 50,000/µL, splenomegaly, and lymphadenopathy. Poor survival was predicted by age > 60 years, high WBC (> 100,000/µL), low hemoglobin (< 6 g/dL), blast percentage > 50%, and delayed diagnosis. The complete integrated panel ($245/patient) demonstrated superior diagnostic accuracy and improved Quality-Adjusted Life Years, showing a favorable economic profile.

Conclusion

This pioneering study in a conflict-affected, resource-limited setting establishes that an integrated multimodal diagnostic approach substantially improves diagnosis of hematological disorders, overcoming morphology-based limitations. These findings emphasize the critical need for implementing comprehensive diagnostic capabilities to optimize patient outcomes and inform public health strategies.

Hematology

Molecular Genetics

Internal Medicine

hematological disorders

multimodal diagnostics

resource-limited settings

yemen

cost-effectiveness

Accurate, timely diagnosis of hematological disorders is critical for effective patient management. While traditional morphological assessment is foundational, modern hematology increasingly relies on advanced diagnostics like flow cytometry, molecular genetics, and immunohistochemistry for precise classification [1].

However, in resource-limited settings, particularly those affected by conflict, access to advanced diagnostic modalities is severely constrained [2]. This often leads to diagnostic delays, misdiagnoses, and suboptimal patient outcomes. Yemen, experiencing protracted conflict, exemplifies these challenges; its healthcare infrastructure, supply chains, and skilled personnel are severely impacted, hindering advanced diagnostic services [3–5]. Reports from Yemen highlight the lack of flow cytometry, cytogenetic, and molecular analysis, often forcing expensive outsourcing of samples [6]. This situation creates significant barriers to quality healthcare, underscoring the urgent need for validated, sustainable diagnostic solutions.

This study presents the first prospective validation of an integrated multimodal diagnostic algorithm for hematological disorders in this specific context. Our aim was to comprehensively evaluate the diagnostic accuracy, clinical utility, prognostic implications, and cost-effectiveness of combining PBF morphology with flow cytometry, molecular genetics, bone marrow trephine biopsy with immunohistochemistry, and targeted ancillary tests in Yemen. This research seeks to improve diagnostic standards and enhance patient care by establishing a robust diagnostic framework adapted to challenging conditions.

The peripheral blood film remains an indispensable, inexpensive, and powerful initial diagnostic tool in hematology, often sufficient for initial diagnosis and guiding further investigations [7,8]. In resource-limited settings, PBF is frequently the primary method [9]. However, PBF has significant limitations. While acceptably accurate for conditions like Chronic Myeloid Leukemia and Chronic Lymphocytic Leukemia, it struggles with conditions requiring precise immunophenotypic or architectural assessment, such as lymphomas. Its interpretation is labor-intensive and susceptible to inter-observer variation, making it insufficient as a standalone comprehensive diagnostic tool [10].

Modern hematology relies on advanced techniques. Flow cytometry immunophenotyping is critical for lineage assignment and identifying aberrant cell populations, especially in acute leukemias and lymphomas, offering superior sensitivity and rapid turnaround [11]. The WHO classification integrates immunophenotyping and molecular features with morphology [1]. Molecular genetic analysis further refines diagnosis by detecting specific genetic aberrations relevant to various malignancies, including leukemias and myeloproliferative neoplasms, guiding targeted therapeutic strategies [12]. Immunohistochemistry on bone marrow trephine biopsies is crucial for definitive diagnosis and subtyping of infiltrating diseases, particularly lymphomas, when morphological findings are ambiguous [13,14]. Implementing these advanced tools in resource-limited settings faces formidable barriers, yet their utility in enhancing diagnostic accuracy for conditions like acute lymphoblastic leukemia is widely recognized [11,15].

Conflict-affected regions like Yemen face profound challenges in healthcare delivery and diagnostic capacity due to damaged infrastructure, fragmented supply chains, and a shortage of skilled personnel. The absence of facilities for flow cytometry, cytogenetic, and molecular analysis forces patients to bear the cost of sending samples abroad. The lack of robust cancer registries also impedes public health planning [16]. These issues highlight an urgent need for resilient diagnostic solutions within fragile health systems [4,17].

Prognostic factors are crucial for risk stratification and optimal treatment planning. Studies consistently identify age, blood counts, and disease subtype as key indicators influencing survival [18]. Understanding these factors in resource-limited contexts is vital, as delayed diagnosis significantly impacts survival. While advanced diagnostic tests have higher initial costs, their cost-effectiveness, in terms of improved accuracy and superior health outcomes, must be rigorously evaluated. Such investments can be highly cost-effective by reducing misdiagnosis, optimizing treatment, and improving Quality-Adjusted Life Years [19,20].

Study design and patient cohort

This was a prospective study conducted on 420 consecutive patients referred with suspected hematological disorders between August 2024 and May 2025 at Al-Thawra Modern General Hospital in Sana'a, Yemen. Ethical approval was obtained from, and informed consent was secured from all participants or their legal guardians. Inclusion criteria comprised all patients presenting with clinical and/or preliminary laboratory findings suggestive of a hematological disorder requiring comprehensive evaluation. Exclusion criteria included patients unwilling or unable to provide consent, or those with incomplete diagnostic workups.

Integrated diagnostic algorithm

Each patient underwent a systematic, integrated diagnostic evaluation comprising:

Peripheral blood film morphology: Initial assessment by experienced hematopathologists for cellular morphology, differential counts, and presence of abnormal cells.
Bone marrow aspiration and trephine biopsy: Performed when indicated, followed by morphological examination of aspirate smears and histological assessment of trephine biopsies.
Immunohistochemistry: Applied to bone marrow trephine biopsies using a panel of specific antibodies for precise lineage determination and subtyping of malignancies.
Flow cytometry immunophenotyping: Performed on peripheral blood or bone marrow aspirates using a standardized panel of monoclonal antibodies for immunophenotypic characterization of suspected leukemias and lymphomas.
Molecular genetic analysis: Targeted testing for specific mutations relevant to diagnosed or suspected conditions, such as BCR-ABL1 for Chronic Myeloid Leukemia and JAK2 V617F for Myeloproliferative Neoplasms.
Targeted ancillary laboratory tests: Including, but not limited to, serum ferritin [21], vitamin B12, folate, protein electrophoresis, and beta-2 microglobulin, as clinically indicated for specific diagnoses like anemia subtyping [22] or monoclonal gammopathies [23]. These tests were selected based on initial clinical suspicion and peripheral blood film findings to further subtype anemias or evaluate for monoclonal gammopathies.

The final diagnosis for each patient was established through a consensus review by a multidisciplinary team of hematologists and pathologists, integrating all available morphological, immunophenotypic, molecular, and clinical data, adhering to the latest WHO classification criteria. This integrated diagnosis served as the gold standard for evaluating the performance of individual and combined diagnostic modalities.

Data collection and statistical analysis

Demographic data, clinical presentations, laboratory parameters, and diagnostic findings were meticulously collected. Statistical analyses were performed using SPSS version 28.0

Diagnostic accuracy: Sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy were calculated for PBF, comparing its results against the final integrated diagnosis.
Agreement analysis: Cohen's kappa coefficient was used to assess the level of agreement between PBF diagnosis and the final integrated diagnosis for various conditions.
Prognostic factor identification: Multivariate logistic regression was employed to identify independent predictors of malignancy. A Cox proportional hazards model was utilized to determine factors independently associated with patient survival.
Cost-effectiveness analysis: A comparative analysis was conducted to evaluate the cost per patient and cost per correct diagnosis for different diagnostic strategies. Quality-Adjusted Life Years were estimated, and the Incremental Cost-Effectiveness Ratio was calculated to assess the value for money of the integrated approach.

Demographic and clinical characteristics of the study cohort

A total of 420 patients were enrolled. The cohort displayed a slight male predominance and a wide age range, with 40.0% of patients aged 21-40 years. Details are provided in Table 1.

Table 1: Demographic characteristics of study participants

Characteristic	Category	Number (n)	Percentage (%)	95% CI
Gender
	Male	229	54.5	49.6-59.4
	Female	191	45.5	40.6-50.4
Age Groups
	0-20 years	85	20.2	16.5-24.3
	21-40 years	168	40.0	35.2-44.9
	41-60 years	126	30.0	25.6-34.7
	61-80 years	35	8.3	5.9-11.4
	>80 years	6	1.4	0.5-3.1
Age Statistics
	Mean ± SD	32.5 ± 18.7 years
	Median (IQR)	28 (18-45) years
Total		420	100.0

Spectrum of hematological disorders

The integrated multimodal approach confirmed a diverse spectrum of hematological conditions. Malignant disorders predominated, led by Chronic Myeloid and Lymphocytic Leukemias. Acute Myeloid and Lymphoblastic Leukemias were also common, alongside lymphoma infiltration (25 patients). Benign disorders included Erythroid Hyperplasia, Aplastic Anemia, Myelofibrosis, Myelodysplastic Syndrome, and Multiple Myeloma as shown in Table 2.

Table 2: Distribution of hematological disorders

Diagnosis	Number (n)	Percentage (%)	95% CI	Classification
Erythroid Hyperplasia	120	28.6	24.3-33.2	Benign
Chronic Myeloid Leukemia (CML)	100	23.8	19.8-28.2	Malignant
Chronic Lymphocytic Leukemia (CLL)	65	15.5	12.2-19.3	Malignant
Acute Myeloid Leukemia (AML)	48	11.4	8.6-14.8	Malignant
Acute Lymphoblastic Leukemia (ALL)	35	8.3	5.9-11.4	Malignant
Lymphoma Infiltration	25	6.0	3.9-8.7	Malignant
Aplastic Anemia	15	3.6	2.0-5.8	Benign
Myelofibrosis	4	1.0	0.3-2.5	Malignant
Myelodysplastic Syndrome	3	0.7	0.1-2.1	Malignant
Multiple Myeloma	3	0.7	0.1-2.1	Malignant
Leishmaniasis	2	0.5	0.1-1.7	Benign
Total	420	100.0

Baseline diagnostic performance of peripheral blood film

PBF's low sensitivity for lymphoma suggests over half of cases could be missed, leading to delays/misdiagnoses. High specificity generally indicates correct PBF-indicated diagnoses. High NPVs for CML/CLL denote reliability, but lymphoma's lower NPV risks false negatives from PBF alone. Cohen's kappa showed excellent overall agreement (0.82), with near-perfect for CML, excellent for CLL/Erythroid Hyperplasia, but only moderate for lymphoma, reinforcing PBF limitations, Details are provided in Table 3 and Table 4.

Table 3: Diagnostic accuracy parameters of peripheral blood film

Condition	Sensitivity (%)	95% CI	Specificity (%)	95% CI	PPV (%)	NPV (%)	Accuracy (%)
CML	94.0	87.4-97.8	98.1	96.1-99.2	96.9	96.3	96.8
CLL	92.3	82.1-97.4	96.6	94.2-98.2	92.3	96.6	95.2
AML	83.3	69.8-92.5	86.2	82.4-89.4	76.9	90.4	85.2
ALL	80.0	63.1-91.6	84.4	80.4-87.8	68.6	90.8	82.8
Lymphoma	41.5	22.5-62.5	97.2	95.1-98.6	68.0	91.5	76.8
Erythroid Hyperplasia	90.8	84.3-95.2	94.3	91.2-96.6	89.3	95.2	92.5
Aplastic Anemia	86.7	59.5-98.3	90.1	86.9-92.7	65.0	97.3	89.5
Overall	84.2	80.1-87.8	96.8	94.2-98.5	91.3	94.7	87.5

Table 4: Agreement between peripheral blood film and final integrated diagnosis

Condition	Kappa Coefficient	95% CI	Agreement Level	p-value
CML	0.94	0.89-0.99	Near-perfect	<0.001
CLL	0.89	0.83-0.95	Excellent	<0.001
Erythroid Hyperplasia	0.85	0.79-0.91	Excellent	<0.001
Aplastic Anemia	0.75	0.62-0.88	Good	<0.001
AML	0.70	0.61-0.79	Good	<0.001
ALL	0.67	0.56-0.78	Good	<0.001
Lymphoma	0.58	0.42-0.74	Moderate	<0.001
Overall	0.82	0.77-0.87	Excellent	<0.001

Incremental diagnostic value of the integrated multimodal approach

Advanced ancillary tests significantly improved diagnostic value.

Flow cytometry immunophenotyping
Flow cytometry definitively diagnosed and classified 173 cases, including all 48 AML and 35 ALL by confirming lineage and providing immunophenotypic data for WHO sub classification. It confirmed clonality in all 65 CLL cases and was critical for initial characterization of 25 lymphoma cases, guiding subsequent IHC.

Molecular genetic validation
Molecular testing confirmed specific genetic aberrations. BCR-ABL1 fusion transcript was detected in 94% of CML cases. For BCR-ABL1-negative MPNs, JAK2 V617F was identified in 89% of cases (100% for Polycythemia Vera). The overall diagnostic yield of the molecular panel was 91.8%, as shown in Table 5 and Table 6.

Table 5: BCR-ABL1 testing results in CML patients

BCR-ABL1 Status	Number (n)	Percentage (%)	95% CI	Test Performance
Positive	94	94.0	87.4-97.8	Sensitivity: 94.0%
Negative	6	6.0	2.2-12.6	Specificity: 100%
Total CML Cases	100	100.0		Accuracy: 96.7%
Non-CML Controls	320	-		PPV: 100%
False Positives	0	0.0	0.0-1.1	NPV: 89.3%

Table 6: JAK2 V617F mutation analysis in myeloproliferative neoplasms

MPN Subtype	JAK2 Positive n(%)	JAK2 Negative n(%)	Total	Mutation Rate (%)	95% CI	p-value
Primary Myelofibrosis	8 (89)	1 (11)	9	89.0	51.8-99.7	<0.001
Polycythemia Vera	5 (100)	0 (0)	5	100.0	47.8-100.0	<0.001
Essential Thrombocythemia	3 (75)	1 (25)	4	75.0	19.4-99.4	0.125
Total MPN Cases	16 (89)	2 (11)	18	89.0	65.3-98.6	<0.001

Bone marrow trephine biopsy and immunohistochemistry
For the 25 lymphoma cases, where PBF sensitivity was low, trephine biopsy and IHC achieved a 100% definitive diagnosis and sub classification, crucial for targeted treatment.

Laboratory parameters and hematological indices

Baseline laboratory parameters showed significant abnormalities, including mean hemoglobin 8.4 ± 3.2 g/dL, elevated mean WBC 45.6 ± 67.8 × 10³/μL, and thrombocytopenia. Blast cells were detected in 43.6% of patients' peripheral blood, Details are provided in Table 7.

Table 7: Baseline laboratory parameters of study cohort

Parameter	Normal Range	Mean ± SD	Median (IQR)	Abnormal Cases n(%)	95% CI
Hemoglobin (g/dL)	12-16	8.4 ± 3.2	7.8 (5.9-10.5)	356 (84.8)	81.2-87.9
WBC Count (×10³/μL)	4-11	45.6 ± 67.8	18.2 (6.8-52.4)	298 (71.0)	66.4-75.2
Platelet Count (×10³/μL)	150-450	156.7 ± 189.3	89.5 (34.2-198.7)	267 (63.6)	58.8-68.1
Blast Percentage (%)	<5	23.8 ± 31.4	8.0 (2.0-35.0)	183 (43.6)	38.8-48.4
M:E Ratio	2:1-4:1	3.8 ± 4.2	2.1 (1.2-4.8)	198 (47.1)	42.3-52.0

Prognostic factors and survival analysis

Multivariate logistic regression identified age >40 years, hemoglobin <8 g/dL, WBC >50,000/μL, platelet <50,000/μL, splenomegaly, and lymphadenopathy as significant independent malignancy predictors. A Cox proportional hazards model revealed that age >60 years, high WBC >100,000/μL, low hemoglobin <6 g/dL, blast percentage >50%, and delayed diagnosis >30 days were independent predictors of poor survival, Details are provided in Table 8 and Table 9.

Table 8: Multivariate logistic regression for malignancy prediction

Predictor Variable	β Coefficient	SE	Odds Ratio	95% CI	Wald χ²	p-value
Age >40 years	0.851	0.245	2.34	1.45-3.78	12.05	<0.001
Male Gender	0.207	0.234	1.23	0.78-1.94	0.78	0.374
Hemoglobin <8 g/dL	1.138	0.257	3.12	1.89-5.15	19.67	<0.001
WBC >50,000/μL	1.541	0.354	4.67	2.34-9.32	18.95	<0.001
Platelet <50,000/μL	1.062	0.284	2.89	1.67-5.01	13.98	<0.001
Splenomegaly Present	1.023	0.295	2.78	1.56-4.95	12.01	0.001
Lymphadenopathy	0.678	0.312	1.97	1.07-3.63	4.72	0.030

Table 9: Cox proportional hazards model for survival analysis

Variable	Hazard Ratio	95% CI	SE	Wald χ²	p-value
Age >60 years	2.45	1.34-4.48	0.309	8.67	0.003
High WBC (>100,000/μL)	3.21	1.78-5.79	0.302	15.23	<0.001
Low Hemoglobin (<6 g/dL)	2.67	1.45-4.91	0.312	9.45	0.002
Blast Percentage >50%	4.12	2.23-7.61	0.314	21.34	<0.001
Delayed Diagnosis (>30 days)	1.89	1.12-3.19	0.267	5.67	0.017
AML vs Other Diagnoses	2.34	1.28-4.28	0.305	7.89	0.005

Cost-effectiveness of diagnostic strategies

Morphology-only diagnosis was least expensive but had limited accuracy. The complete integrated panel ($245 per patient) achieved the highest diagnostic accuracy. The Incremental Cost-Effectiveness Ratio indicated significant gains in diagnostic accuracy and Quality-Adjusted Life Years for the additional investment, demonstrating a favorable economic profile, Details are provided in Table 10.

Table 10: Cost-effectiveness analysis of diagnostic strategies

Diagnostic Strategy	Cost per Patient (USD)	Diagnostic Accuracy (%)	Cost per Correct Diagnosis	ICER	QALYs Gained
Morphology Only	45	72.3	62.2	Reference	Reference
Morphology + Flow Cytometry	125	84.7	147.5	285.7	0.15
Morphology + Molecular	180	89.2	201.8	423.1	0.22
Complete Panel	245	96.8	253.1	512.8	0.31

This pioneering prospective study in conflict-affected, resource-limited Yemen provides compelling evidence for the superior diagnostic accuracy and clinical utility of an integrated multimodal approach to hematological disorders. Our findings underscore that while conventional PBF is a valuable initial screening tool, its limitations, particularly for complex malignancies, necessitate advanced diagnostics for definitive classification.

The observed 87.5% overall PBF accuracy aligns with its foundational role. However, PBF's reduced sensitivity for lymphoma infiltration and moderate agreement with the integrated diagnosis highlights that morphology alone is often insufficient for diseases requiring immunophenotypic or genetic features. PBF also has limitations in characterizing various anemias or precisely subtyping myelodysplastic syndromes, where cytogenetic or molecular studies are crucial [24,25].

The incremental diagnostic value of integrating flow cytometry, molecular genetics, and bone marrow trephine biopsy with immunohistochemistry was profound. Flow cytometry was instrumental for accurate immunophenotypic characterization and lineage assignment of acute leukemias and lymphomas [11]. Molecular genetic testing provided critical confirmation of specific genetic aberrations (e.g., BCR-ABL1 in CML, JAK2 V617F in MPNs), essential for guiding targeted therapies and prognostication [12]. For lymphoma, where PBF proved inadequate, trephine biopsy and IHC achieved 100% definitive diagnosis and subclassification [13,14]. These results underscore the necessity of establishing modern diagnostic laboratories and training personnel in resource-constrained environments.

Our identification of significant prognostic factors, including age, specific hematological parameters, and clinical signs, offers critical insights into Yemen's disease landscape. Delayed diagnosis was an independent predictor of poor survival, as longer diagnostic intervals allow disease progression and narrow treatment windows [26–29]. These prognostic factors resonate with findings from studies in the Middle East and Africa [18].
Our findings contribute to the scarce literature on hematological disorders in Yemen. An older study by Al-Ghazaly et al. in Sana'a reported different prevalences of AML, ALL, CML, and CLL compared to our study, possibly reflecting shifting epidemiology or diagnostic capabilities [30]. More recently, Al-Nuzaili et al. in Sana'a described hematological malignancy prevalence [31]. A previous study by Saeed N. et al. in south-west Yemen reported various acute and chronic leukemias, MPN, MDS, multiple myeloma, and lymphoma [31]. Our study shows notably lower prevalence of AML and ALL, and higher CML and CLL compared to these reports. A study in Hadhramout highlighted chronic MPN followed by AML [32]. Our specific MPN prevalence rates and JAK2 V617F mutation rates are detailed in Table 6. These comparisons underscore regional variability and the dynamic nature of hematological disease patterns within Yemen. Our identification of Leishmaniasis (0.5%) is consistent with other studies [33]. The overall lack of comprehensive epidemiological reports across Yemen, as noted by Al-Nuzaili et al. [31] and Al-Ghazaly et al. [30], underscores our study's critical importance in providing contemporary data. Anemia challenges [34] and transfusion-transmitted infections [35] also remain public health problems.

Furthermore, the cost-effectiveness analysis strongly justifies investing in an integrated multimodal diagnostic approach. While the complete panel has a higher initial cost, its substantial increase in diagnostic accuracy and resulting gains in Quality-Adjusted Life Years demonstrate a favorable economic profile. This is consistent with studies from other resource-limited environments, showing advanced diagnostics can be highly cost-effective by improving patient outcomes and reducing long-term healthcare burdens. This evidence supports strategic investment in comprehensive diagnostic capabilities.

The unique contribution of this study lies in its prospective validation of these integrated diagnostics within a conflict-affected, resource-limited setting like Yemen. Challenges from ongoing conflict amplify the importance of developing resilient and effective diagnostic systems. Our results demonstrate that despite formidable obstacles, high diagnostic accuracy is achievable, offering a blueprint for other regions [11]. To ensure long-term sustainability, strategies must include continuous investment in local personnel training, resilient supply chains, and innovative approaches like tele-consultation models [36–38].

Strengths and limitations: Significant strengths include the prospective design and comprehensive evaluation of a large patient cohort, with a multidisciplinary team establishing the gold standard. A limitation is the single-center nature, potentially affecting generalizability. Future research should include multi-center studies and implementation science research.

This first prospective study from a conflict-affected, resource-limited setting in Yemen conclusively demonstrates that an integrated multimodal diagnostic approach significantly improves the accuracy and comprehensiveness of diagnosing hematological disorders. While PBF remains a valuable initial screening tool, its utility is considerably enhanced by sequential integration of flow cytometry, molecular genetic analysis, bone marrow trephine biopsy, and immunohistochemistry. These advanced techniques are indispensable for definitive classification, accurate prognostication, and guiding targeted therapies, leading to better patient outcomes. The study also provides strong evidence for the cost-effectiveness of this integrated approach, justifying the investment required to establish and sustain such capabilities in challenging environments. These findings provide a compelling economic argument for international and national health bodies to prioritize strategic investments in comprehensive diagnostic capabilities within fragile health systems. Our findings offer a validated framework and underscore the critical need for strengthening comprehensive hematological diagnostic services in Yemen and other similarly constrained regions globally, ultimately contributing to improved healthcare standards and patient survival.

Additional information

Data availability

The data supporting the findings of this study are not openly available due to sensitivity reasons. However, they are available from the author upon reasonable request. All patient data were anonymized and stored securely using digital storage protocols to ensure the protection of patient confidentiality, particularly given the vulnerable population studied.

Author contributions

Concept and design: Mohammed A. Al-Qadhi, Ahmed K. Salem, Mohammed A. Hajar
Acquisition, analysis, or interpretation of data: Mohammed A. Al-Qadhi, Ahmed K. Salem, Mohammed A. Hajar
Drafting of the manuscript: Mohammed A. Al-Qadhi, Ahmed K. Salem, Mohammed A. Hajar
Critical review of the manuscript for important intellectual content: Mohammed A. Al-Qadhi, Ahmed K. Salem, Mohammed A. Hajar
Supervision: Mohammed A. Al-Qadhi, Ahmed K. Salem, Mohammed A. Hajar

Disclosures

Human subjects

Informed consent for treatment and open access publication was obtained or waived by all participants in this study. The Institutional Review Board of Al-Thawra Modern General Hospital in Sana'a approved this study. Written informed consent was obtained from all adult participants and parents/guardian's of pediatric patients. All procedures were conducted in accordance with the ethical principles of the Declaration of Helsinki and local ethical guidelines.

Animal subjects

All authors have confirmed that this study did not involve animal subjects or tissue.

Conflicts of interest

In compliance with the International Committee of Medical Journal Editors uniform disclosure form, all authors declare the following:

Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work.
Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work.
Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

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The authors declare no competing interests.

Integrated Multimodal Diagnostic Validation in Hematological Disorders: First Prospective Study from a Conflict-Affected, Resource-Limited Setting in Yemen

Status:

Version 1

Abstract

Background and objectives:

Methods

Results

Conclusion

Introduction

Methodology

Study design and patient cohort

Integrated diagnostic algorithm

Data collection and statistical analysis

Results

Demographic and clinical characteristics of the study cohort

Spectrum of hematological disorders

Baseline diagnostic performance of peripheral blood film

Incremental diagnostic value of the integrated multimodal approach

Laboratory parameters and hematological indices

Table 7: Baseline laboratory parameters of study cohort

Prognostic factors and survival analysis

Cost-effectiveness of diagnostic strategies

Discussion

Conclusion

Declarations

Additional information

Data availability

Author contributions

Disclosures

Human subjects

Animal subjects

Conflicts of interest

References

Additional Declarations

Status:

Version 1