Highlight
- The Predictive Aplastic Score System (PASS) is a validated clinical tool that distinguishes acquired aplastic anemia (AA) from inherited bone marrow failure syndromes (IBMFS) in adults without immediate genetic testing.
- PASS uses seven readily available clinical and laboratory variables, achieving excellent diagnostic accuracy, with an AUC of 0.990 in the training cohort and 0.977 in external validations.
- The model provides 100% positive predictive value for AA at scores ≥30, enabling rapid clinical decision-making and potentially reducing reliance on time-consuming or inaccessible genetic tests.
- PASS facilitates differentiation in approximately 80% of adult cases, improving early diagnosis and appropriate treatment selection.
Background
Acquired aplastic anemia (AA) is a rare but severe hematologic disorder characterized by immune-mediated destruction or failure of hematopoietic stem and progenitor cells, resulting in pancytopenia and bone marrow hypocellularity. Differentiating AA from inherited bone marrow failure syndromes (IBMFS) such as Fanconi anemia, dyskeratosis congenita, or Shwachman-Diamond syndrome is critical because treatment strategies differ markedly. AA generally responds to immunosuppressive therapy or hematopoietic stem cell transplantation, whereas IBMFS may require different approaches and carry distinct prognoses.
Currently, diagnosis of AA depends on excluding IBMFS, often relying on genetic testing and comprehensive clinical evaluation. However, genetic testing is not universally available, can delay diagnosis, and may yield inconclusive results. This diagnostic uncertainty poses challenges for timely and appropriate management.
Study Design
Aleixo and colleagues conducted a multi-center study to develop and validate a predictive clinical scoring system for distinguishing AA from IBMFS in adults. The study included a training cohort of 212 adult patients diagnosed with bone marrow failure: 162 with AA and 50 with IBMFS. Clinical and laboratory data were collected, focusing on parameters commonly available in clinical practice.
Using logistic regression with least absolute shrinkage and selection operator (LASSO), seven predictive variables were identified for inclusion in the model: disease severity, acuity of onset, patient age, presence of IBMFS red flags (such as family history or congenital anomalies), AA-associated comorbid conditions, AA-associated somatic mutations, and telomere length measurements. This composite was termed the Predictive Aplastic Score System (PASS).
The model’s diagnostic performance was assessed via the area under the Receiver Operating Characteristic curve (AUC). Validation was performed on four external cohorts totaling 716 patients.
Key Findings
PASS demonstrated excellent discrimination between AA and IBMFS. In the training cohort, the model achieved an AUC of 0.990 (95% CI: 0.982-0.999). A threshold score of ≥30 yielded a 100% positive predictive value (PPV) for AA, meaning all patients scoring at or above this level were diagnosed with AA. Conversely, 86.8% of patients with scores below 0 had IBMFS, supporting the score’s utility at both extremes.
Fig. 1: Defining clinical characteristics of AA and IBMFS.
External validation across four distinct cohorts reinforced these findings, with an AUC of 0.977 (95% CI: 0.968-0.987). Threshold analyses replicated 100% PPV for AA at PASS ≥30, allowing rapid classification of approximately 80% of AA cases. This supports the score’s robustness and applicability across diverse clinical settings.
Fig. 2: The PASS model performance in the training cohort.
This simplified clinical tool aids clinicians in expediting diagnosis of AA, enabling timely initiation of appropriate treatment and reducing dependence on lengthy or unavailable genetic diagnostics.
Expert Commentary
The development of PASS addresses a crucial unmet need in the diagnosis of aplastic anemia. Differentiation from IBMFS can be diagnostically challenging but essential for therapeutic decisions. By relying on clinical features, laboratory findings, and telomere length—parameters accessible in many hematology practices—PASS offers an efficient algorithm for frontline clinicians.
The inclusion of somatic mutation data and telomere length is notable, reflecting advances in our understanding of AA pathogenesis and its distinction from inherited marrow disorders. Nevertheless, limitations include reliance on some specialized tests (telomere measurement), which may not be universally accessible.
Further research should explore integration with emerging molecular diagnostics and applicability in pediatric populations. Additionally, ongoing monitoring of PASS’s performance in real-world practice will be critical to refining cutoffs and enhancing clinical utility.
Conclusion
The Predictive Aplastic Score System (PASS) is a highly accurate, pragmatic clinical tool for differentiating acquired aplastic anemia from inherited bone marrow failure syndromes in adults. By enabling rapid and reliable diagnosis in the absence or delay of genetic testing, PASS enhances clinical decision-making and optimizes patient management. Adoption of this scoring system could reduce diagnostic uncertainty, accelerate initiation of appropriate therapy, and improve outcomes in aplastic anemia care.
Funding and ClinicalTrials.gov
Details regarding study funding and clinical trial registration were not provided in the original report.
References
Aleixo G, Cheon H, Zheng J, Soewito S, Lee J, Kaphan E, Kalakuntla N, Jen WY, Kotha S, Rupsee A, Djulbegovic M, Matthews JA, Kadia TM, Olson TS, Peffault de Latour R, Sicre De Fontbrune F, Bat T, DiNardo CD, Babushok DV. Development and validation of the predictive aplastic score system (PASS): a simplified tool to diagnose acquired aplastic anemia in adults. Leukemia. 2026 Jul;40(7):1411-1426. doi: 10.1038/s41375-026-02924-3. Epub 2026 Apr 27. PMID: 42045556; PMCID: PMC13323085.
Additional relevant literature for context not provided here but should be consulted for comprehensive clinical implementation guidance.
