Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia

Background Aplastic anemia is a severe hematologic disorder marked by pancytopenia and bone marrow failure. ICU admission often reflects disease progression or complications requiring critical care. Predicting short-term survival in these patients is vital for individualized treatment and resource optimization. Nomograms provide a practical tool for integrating clinical parameters, offering accurate visualized survival predictions to guide decision-making in patients with aplastic anemia in the ICU.

Using the MIMIC-IV database, we identified ICU patients diagnosed with aplastic anemia. From thousands of available variables, we extracted data across five dimensions: demographic, synthetic indicators, laboratory events, comorbidities, and drug usage. Based on existing studies of aplastic anemia, more than 400 variables were further refined and machine learning techniques were applied to identify the seven most effective predictors for modeling. Preprocessing was performed using machine learning approaches, and the feasibility of these predictors was validated through additional classification and regression models, the verification method is AUROC. Furthermore, external validation was performed using data from the eICU Collaborative Research Database to assess the generalizability of our models.The interactive nomograms were constructed using logistic regression (LR) to predict mortality rates at 7 days, 14 days, and 28 days in patients with aplastic anemia.

A total of 1,662 patients diagnosed with aplastic anemia were included in this study, with a 7:3 ratio split into training and testing cohorts. The logistic regression model demonstrated strong predictive performance, achieving AUC values of 0.8227, 0.8311, and 0.8298 for 7-day, 14-day, and 28-day mortality predictions, respectively. External validation using the eICU database further confirmed the model’s generalizability, with AUC values of 0.7391, 0.7119, and 0.7093. These results highlight the model’s stability and effectiveness in predicting short-term survival in aplastic anemia patients.

A set of seven predictors, led by APS III, proved effective for modeling short-term survival in aplastic anemia patients. Using these predictors, Cox and logistic regression models generated nomograms that accurately predict 7-day, 14-day, and 28- day mortality. These tools can support clinicians in personalized risk assessment and decision-making. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used ONLY openly available human data from the MIMIC-IV and eICU databases, which were publicly accessible before the initiation of this study. These datasets are maintained by the MIT Laboratory for Computational Physiology and require registration and approval for access. MIMIC-IV Database: Available at https://physionet.org/content/mimiciv/2.2/ eICU Collaborative Research Database: Available at https://physionet.org/content/eicu-crd/2.0/ Access to these datasets requires completing the necessary training and obtaining approval via the PhysioNet Credentialing Process, ensuring compliance with data use agreements and ethical guidelines. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are available upon reasonable request to the authors