SICH refers to the occurrence of a blood clot in the brain parenchyma without any associated trauma or surgical intervention (1). SICH is a type of stroke with high mortality and disability rates that causes significant stress to individuals and society (2). The overall incidence of stroke has declined in recent decades; however, the incidence of SICH remains controversial. Some studies have demonstrated a decrease in SICH incidence in individuals aged <75 years (3) or a decline in hypertension-related SICH (4). The incidence of stroke is relatively stable in high-income countries; however, it has been increasing in low-income countries (5). The incidence of SICH is expected to increase with the aging of the global population.

Although the incidence of SICH is lower than that of ischemic stroke, the associated death and disability rates are higher, causing difficulties for individuals and families (6). Extensive studies on risk factors for SICH, as well as preliminary studies on its prognosis, have been conducted. The prediction of disease prognosis is an important part of clinical research; therefore, researching prognostic factors for SICH is important, especially in determining controllable risk factors. With the emergence of artificial intelligence (AI), radiological techniques have become extensively utilized for predicting prognosis in ICH patients. However, it is important to note that this method is still in the research phase, and its predictive performance needs to be further validated (7, 8).

The nomogram is a visual prediction tool that assigns different scores to different states of each variable to obtain the probability of an ending. Currently, there are several prediction models to predict patients with SICH; however, they may be controversial owing to their predictive power and clinical application ability (9, 10). It is necessary to optimize the predictive capability of the model and improve its patients with SICH.

In this study, multiple regression analysis showed that NIHSS score, age, hematoma volume, and IVH were risk factors for poor short-term prognosis in patients with SICH, and a nomogram prediction model was constructed based on these clinical characteristics. The model has a good prognostic prediction ability and high clinical applicability and can therefore provide a reference value to aid in developing risk stratification and precision medicine strategies for patients with cerebral hemorrhage.

The NIHSS is an internationally recognized, comprehensive, and effective assessment tool for assessing neurological function in patients with SICH. The univariate analysis had both GCS and NIHSS scores as risk factors; however, only NIHSS scores were an independent risk factor for poor prognosis. Existing prognostic scales for SICH mostly use the GCS scores due to their simplicity and effectiveness. Some studies found that the ability to predict death after replacing the GCS scores with the NIHSS scores was weaker than the original ICH score, but the ability to predict neurological outcomes was enhanced (14). This may be related to the wider range of NIHSS scores because in addition to assessing patients' level of consciousness, the NIHSS also assesses neurological deficits (15). However, both the GCS and NIHSS are subjective indexes, and this may cause evaluation bias; therefore, this score should be affirmed with further evaluation. Therefore, an accurate and objective evaluation of a patient's onset or admission can help guide follow-up treatment.

Age is a risk factor for cerebrovascular disease, and SICH incidence increases with age (6). Advanced age was strongly associated with initial SICH severity as well as 90-day mortality or morbidity (16). As the patient's age increases, immunity decreases, reducing disease tolerance. This leads to more severe neurological damage and subsequently to an increased likelihood of other complications. Elderly patients often find it inconvenient to move due to their families' economic struggles and their own physical limitations. In addition, the medical policy lacks targeted treatment for elderly patients, leading to a poor prognosis. A retrospective study by Bernardo et al. identified increased age as an independent risk factor for high long-term mortality after SICH (17); however, some studies have suggested that age is not associated with SICH prognostic (18). Therefore, this topic remains controversial. This study also suggests that age is an independent risk factor for poor prognosis in patients with SICH at 90 days.

The amount of bleeding after a cerebral hemorrhage is an important indicator of its severity. Hematoma size has been reported to be closely related to prognosis in patients with SICH (19–21). In these patients, intracranial hematoma is involved in secondary brain injury as well as mechanical injury. Furthermore, hematoma size is closely related to intracranial pressure, with intracranial hypertension being an important factor in poor prognosis (22). The size of the hematoma on admission is significantly associated with short-term mortality (23). This corresponds to the results of the present study, which indicated that hematoma size on admission was an independent risk factor for poor 90-day prognosis in patients with SICH. Therefore, a large volume of intracranial hematoma after SICH can cause severe neurological deficits and secondary brain damage, resulting in poor prognosis. No clear criteria have been established for determining the standard hematoma volume in relation to SICH prognosis.

IVH occurs in 30–45% of patients with SICH (19). Its occurrence depends not only on the location and volume of the hemorrhage, as well as blood pressure fluctuations and abnormalities in the coagulation cascade (24, 25). The occurrence of new IVH, even of only 1 ml, appears to be a strong predictor of poor prognosis in patients with SICH (26). A follow-up analysis of the ATACH II trial by Okazaki et al. identified IVH as a risk factor for late 90-day neurological deterioration in these patients (27). Similar to the results of this study, SICH with IVH was identified as an independent risk factor for poor prognosis. However, a prospective study by Roeder et al. concluded that minor IVH (not causing obstructive hydrocephalus) was not associated with adverse outcomes or death after SICH (28).

There are still some limitations in this study. First, the relatively small sample size has limited persuasive power, and further studies should expand the sample size and increase the follow-up time. Second, a single external validation center was used in this study; therefore, further validation of the generalizability of this prediction model is required. In future studies, we will combine different regions and multiple external validation centers to optimize and validate the model. Third, this study did not combine multiomics, such as imaging and metabolomics, for model construction; however, its clinical applicability was increased since the predictive model was based on clinical characteristics. Fourth, the patients included in this study were non-surgical patients, so the results are not applicable to patients who underwent surgical treatment. In the following study, we will further expand the research population and establish a more complete prediction model.

In summary, NIHSS scores, age, hematoma volume, and IVH were found to be independent risk factors for the 90-day poor functional outcomes of SICH. In addition, the ROC curve, calibration plot, and DCA curve showed good predictive performance and calibration. Therefore, this model can help predict the 90-day functional outcomes of SICH.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by the Ethics Committee of Hefei Second People's Hospital and First Affiliated Hospital of Anhui Medical University. Written informed consent to participate in this study was provided by the patient/participants' OR patient/participants legal guardian/next of kin.

ZG: Formal analysis, Writing—review and editing, Data curation. TG: Writing—original draft, Software, Supervision. ZC: Investigation, Methodology, Writing—original draft. XH: Formal analysis, Writing—review and editing. JC: Writing—original draft, Resources. HY: Writing—original draft. AW: Writing—original draft, Writing—review and editing, Data curation. LW: Writing—original draft, Writing—review and editing, Formal analysis, Investigation, Visualization.