Despite significant advances in endovascular intervention and microneurosurgical clipping for the treatment of ruptured intracranial aneurysms, patients with aneurysmal subarachnoid haemorrhage (aSAH) still face many postprocedural complications. Anaemia is a severe complication that occurs in 30%–50% patients with aSAH.1–5 Anaemia after aSAH worsens cerebral oxygen delivery, leading to a higher rate of cerebral infarction and is associated with unfavourable poor long-term neurological outcome and mortality.6 7 Conversely, elevated haemoglobin levels in patients with aSAH correlate with improved outcomes.7 8 Physiological and preclinical evidence shows that anaemia affects a secondary brain injury after aSAH, which is potentially modifiable.1

Post-aSAH anaemia has been treated by red blood cell (RBC) transfusion but significant transfusion-related risks result in poorer outcomes and increased complication rates.9However, packed RBC transfusion has been shown to improve brain tissue oxygen delivery after SAH.10–12 Several clinical trials have demonstrated that erythropoietin increases brain tissue oxygen tension and reduces vasospasm and delayed ischaemic deficits in patients with post-SAH anaemia.13–15 A recent randomised, double-blind, placebo-controlled phase 2 clinical trial revealed that patients with delayed ischaemic neurological deficits after aSAH were more susceptible to complications of anaemia.1 Additionally, packed RBC transfusion was beneficial for patients with no prior anaemia. The authors emphasised that blood transfusion in critically ill patients could frequently serve as an independent risk factor for poor outcomes. This suggests that individuals who are already anaemic may have passed a critical threshold for adverse outcomes and mortality, rendering blood transfusion ineffective as a late intervention.

Based on current evidence, there is significant clinical value in identifying at-risk patients before anaemia occurs and it may be beneficial to reserve treatment aimed at increasing brain oxygen delivery for those patients. Anaemia following aSAH arises from complex and multifaceted causes, extending beyond surgical intervention. There is currently no tool to integrate patients’ complex clinical profiles and multiple conflicting predictors into an objective, comprehensive, validated risk assessment.

In this study, we aimed to develop and validate a nomogram (a statistical tool that incorporates numerous variables to assess the occurrence rate of a specific outcome), to predict postacute phase anaemia (3 days after aSAH).16 This phase was selected because vasospasm and delayed cerebral ischaemia usually arise 3–4 days after aSAH, whose devastating consequences can be exacerbated by anaemia.17 18

A nomogram was developed from a patient cohort from the Department of Neurosurgery, The First Affiliated Hospital, Zhejiang University School of Medicine (Hangzhou, China). A retrospective cohort study of aSAH patients admitted between 1 March 2009 and 31 December 2018 was conducted. Patients were eligible if they were admitted within 2 days of aSAH, confirmed by CT angiography or digital subtraction angiography within 24 hours of admission. Patients with a history of trauma, vascular malformation or other non-aneurysmal bleeding, and patients who died within 5 days of diagnosis were excluded.

All ruptured aneurysms were treated according to institutional guidelines by open surgery or by endovascular methods within 2 days of admission, and if patients or relatives denied permission for both therapies, conservative treatment was administered (including simple external ventricular drainage when necessary). Vasospasm and delayed cerebral ischaemia usually appear about 1 week after onset of aSAH and gradually resolve after 2 weeks. After that, patients received dynamic head CT periodically to detect vasospasm and delayed cerebral ischaemia. Nimodipine was used in all patients to prevent complications.19

Anaemia was tolerated unless haemoglobin level was <70 g/L.6

The following data were extracted from the electronic medical record system: demographic information including age, sex, body weight, body mass index, history of hypertension, diabetes, heart disease, smoking, alcohol consumption and history of stroke. Severity of clinical status on admission was evaluated by Glasgow Coma Scale (GCS) score, and severity of haemorrhage on admission was graded on CT scans using the modified Fisher score20 and Graeb score.21 The presence of hydrocephalus requiring ventricular drainage or serial lumbar puncture on admission was also recorded. On-admission haematological variables including haemoglobin, white cell count, C reactive protein level, international normalised ratio (INR), prothrombin time, D-dimer, fasting blood glucose, alanine and aspartate aminotransferases, alkaline phosphatase and creatinine were extracted. The on-admission systemic inflammatory response syndrome (SIRS) score was also assessed according to the number of following standard criteria met22: heart rate >90 bpm, respiratory rate >20 breaths/min, body temperature >38℃ or <36℃, white cell count <4.0 or >12.0×109/L. On-admission vital signs including heart rate and systolic/diastolic pressure were recorded. Location and size of the ruptured aneurysm were recorded. Details of open surgery, endovascular intervention or other methods (eg, conservative therapy with or without ventricular drainage) were extracted.

Study outcome was postacute phase anaemia, which was defined as haemoglobin <10 g/dL 3 days after occurrence of aSAH, prior to discharge.

To validate the nomogram, we used data collected at the neurosurgical department of another provincial regional medical centre (Sanmen People’s Hospital) from 1 January 2004 to 31 March 2019. These data shared identical clinical characteristics and adhered to the same inclusion and exclusion criteria as the development cohort. A uniform protocol for data entry between two collaborating centres was achieved by the development of comprehensive instructions defining variables and outcomes used in the study.

In addition to the previously mentioned medical centres, we expanded our validation efforts by incorporating data from patients treated at the Hangzhou Red Cross Hospital. This prospective validation included patients who received medical care at the hospital from 12 March 2024 to 2 April 2024. The aim of this additional dataset was to strengthen the robustness and ability to generalise our findings.

SPSS V.22 (SPSS) and R V.3.5.3 (R Foundation for Statistical Computing, Vienna, Austria) were used for statistical analysis. All values were expressed as mean and SD or median and IQR. Baseline factors were analysed in relation to development of anaemia using univariate logistic regression and variables with p<0.10 were entered into a backward stepwise analysis using multivariate logistic regression. The conventional threshold for inclusion is often set at p<0.05, but a threshold of p<0.10 was used in this study to ensure that potentially relevant variables were not overlooked, thus allowing for a more comprehensive assessment of potential predictors within the model. The significance of the variables in the model was also determined by statistical measures such as p values, CIs and other relevant statistical metrics. These measures were used to assess the strength and reliability of the associations between the variables and the outcomes within the model. We also considered several interactions with surgery or non-surgical treatment in the models if significant. Area under the receiver operating curve was calculated. The Hosmer-Lemeshow test was performed to validate the goodness of fit for logistic regression models. The Mann-Whitney test was used for comparison of factors in the final model, such as age and baseline haemoglobin in the development and validation cohorts. The χ2 test was used for comparison of aneurysm size and treatment and anaemia. The expectation-maximisation algorithm was used to input missing values. Body weight was not included in the final model because it was not available for >25% of patients and its inclusion would have excluded all these patients.

Significant interaction term in statistics refers to a term in a model (eg, regression model) where the effect of one variable on the dependent variable is modified by another variable. When this interaction term is statistically significant, it indicates that the relationship between the independent and dependent variables changes in accordance with the level of the moderating variable. In essence, it suggests that the combined effect of the variables is not simply additive, but rather affects the outcome in a way that cannot be explained by the individual effects of each variable alone.

The calibration of the model was evaluated visually with the calibration curve, which measured the matching degree between the observed outcomes in the current cohort and the outcomes predicted by the models. Perfect calibration, when the predictive model perfectly matched the patient’s real risk, was indicated by a 45° line while any deviation above or below the line demonstrated underprediction or overprediction, respectively.

In this study, patient and public involvement was pivotal in shaping the research. As this was not a randomised controlled trial, assessment of intervention burden by patients did not apply. Because of that, patient consent was not required as the study relied on anonymised data from medical records.

Although this is a retrospective study, efforts were made to ensure that patient perspectives were considered in the interpretation of historical data. Their historical experiences have been instrumental in shaping the approach of this study, ensuring that it remains considerate of their unique circumstances. Through direct engagement and dialogue, we gained insights into the challenges and concerns faced by individuals affected by aSAH.

The results of this study will be disseminated to all relevant study participants, where possible. We are committed to sharing the findings in a manner that is accessible and understandable to those who contributed to the research. Additionally, we will seek feedback from participants to ensure that the results are effectively communicated and that their perspectives are considered.

The patient advisers were acknowledged for their contributions to the institution’s research efforts, although they were not directly involved in this specific study. Patients and the public were not directly involved in the conduct of this retrospective case–control study.

Postacute phase anaemia in patients with aSAH is caused by a variety of factors, but primarily blood loss is caused by disease and treatment and is an obstacle to regenerating RBCs. Aneurysm surgery can cause anaemia by intraoperative blood loss and by inhibiting the response of bone marrow to erythropoietin due to activation of systemic inflammation by aSAH. In this study, we developed and validated a novel nomogram for aSAH patients to predict postacute phase anaemia, which showed satisfactory predictive accuracy. The key variables identified from the development cohort and recognised in the validation cohort were age, treatment method (open surgery or endovascular therapy), baseline haemoglobin level, fasting blood glucose, SIRS score on admission, GCS score, aneurysm size, prothrombin time and heart rate. Several studies have been published on anaemia outcomes and targeted treatment after aSAH,1 23–27 yet there is still a lack of evidence for proper management of post-aSAH anaemia. Current aSAH treatment guidelines recommend RBC transfusion in anaemic patients with risk of cerebral ischaemia, although no thresholds for transfusion are suggested.28–30 Two significant concerns impede the widespread application of anaemia-targeted RBC transfusion. (1) Whether transfusion is genuinely effective in improving outcomes for all aSAH patients with anaemia, or if its effectiveness is limited to a specific subgroup. (2) When is the proper timing for the transfusion? The answer to these concerns remains unclear, yet accumulating recent evidence indicates that aSAH patients are likely to benefit from transfusion when facing delayed cerebral ischaemia, and RBC transfusion is particularly beneficial for patients before anaemia actually occurs.1 31

Given the potential benefits of treating patients at risk of anaemia, several risk factors for anaemia outcomes have been identified. The conflicting results between anaemia and adverse outcomes may be due to the fact that previous studies were usually retrospective, single-centre, observational cohort studies, resulting in small sample sizes that made it difficult to draw firm conclusions.32 These findings underscore the unmet need to incorporate these predictors into patient decision-making processes. However, there is no instrument to synthesise patients’ complicated clinical features into a comprehensive and objective prediction of anaemia outcome, particularly when the decision in question is as important as how the anaemia would be treated.

In accordance with prior research,33 we collected data across seven key categories for our analysis. (1) Demographic information: age and sex; (2) medical history: hypertension, diabetes mellitus, smoking, alcohol use, stroke and heart disease; (3) clinical scores: GCS score, SIRS score on admission, Modified Fisher scale score, and Graeb score; (4) laboratory tests: baseline haemoglobin, INR, prothrombin time, D-dimer, fasting blood glucose, alanine and aspartate aminotransferase, alkaline phosphatase, creatinine, systolic and diastolic blood pressure, heart rate, white cell count, body weight and BMI; (5) aneurysm related details: treatment (open surgery, endovascular, other), size ≥10 mm and location (eg, posterior circulation); (6) length of hospital stay and (7) postoperative outcomes: symptomatic vasospasm, mortality and high-sensitivity CRP. This comprehensive dataset allowed a thorough analysis across various dimensions.

Many risk factors are associated with the development of anaemia. After univariate risk factors analysis, age, sex, smoking history, alcohol consumption, GCS score, SIRS score on admission, modified Fisher scale score, baseline haemoglobin, treatment, INR, prothrombin time, D-dimer, fasting blood glucose, aspartate aminotransferase, alkaline phosphatase, heart rate, body weight and aneurysm size were closely related to the occurrence of anaemia. Occurrence of anaemia was associated with morbidity and mortality, even after controlling for age, bleeding severity and other clinical factors such as surgical treatment or angiographic vasospasm. In our research, patients who developed anaemia tended to have longer hospital stay, higher levels of inflammation and greater mortality. In addition, they were more likely to suffer blood vessel spasms. Although there was a strong association between anaemia and adverse outcomes, it was difficult to establish causation in retrospective analyses.32 Some researchers have questioned the clinical significance of the occasionally small differences in haemoglobin concentrations observed between patients with good or poor outcomes.8 34

Based on univariate risk factors, we finally identified 7 of the 21 initially investigated variables and 3 interaction terms by multivariate logistic regression analysis. Our nomogram is a practical instrument to offer patient-centred and individualised prediction of anaemia after aSAH. Our model consists of easily ascertainable clinical characteristics to provide prediction of anaemia outcome at a period of greatest susceptibility for poor outcome (postacute phase after aSAH).

Our study had several strengths. First, to our knowledge, this is the first tool for predicting anaemia complication in patients with aSAH. Given that the accuracy of anaemia estimation is crucial for early intervention, our results, derived from an externally validated nomogram, provided improved accuracy in estimating nomogram performance. This enhances our ability to generalise the study findings and broadens applicability of the nomogram in different populations. Second, the nomogram was based on readily available clinical characteristics, making it suitable for diverse applications. Consequently, this tool might be particularly helpful and encouraging when early treatment for post-aSAH anaemia is intended. Third, the instrument accounts for different types of operation and various interactions were evaluated.

The decision to manage anaemia after aSAH is still complicated and challenging, which is definitely not solely based on the risk of anaemia occurrence. For example, an early intervention that leads to complete avoidance of post-aSAH anaemia at the expense of infarction or infection might still be considered a failure. Therefore, our nomogram is not intended to replace clinical decision-making, but rather to enhance it by providing objective and quantifiable evaluation of post-aSAH anaemia outcome, which is a pivotal factor influencing clinical decision-making.

There were some limitations that should be considered. First, although our nomogram was derived from a large development cohort, the validation cohort consists of only one external institution with limited datasets. In spite of the high c-statistic on the absolute scale, further work is needed to validate the instrument in a diverse, multicentre international cohort. Second, our nomogram did not include other critical post-aSAH outcomes of interest including vasospasm, delayed cerebral ischaemia and clinical prognosis. Third, other than open surgical clipping, we did not specify diverse types of endovascular treatment; however, endovascular interventions to treat aSAH are becoming increasingly common, with rapidly advancing technologies and many treatment strategies. Fourth, although we conducted external and prospective validations, our study was confined to the East Asian population. Future research should involve multicentre studies encompassing samples from different ethnicities and geographical regions to validate and expand the generalisation of our findings. Such research can help ascertain whether the risk factors identified and predictive models developed remain robust across diverse populations, providing more comprehensive guidance for clinical practice.