Changes in Outcome Prediction During the First Week After Subarachnoid Hemorrhage

Interest in machine learning (ML)-based predictive modeling has led to the development of models predicting outcomes after aneurysmal subarachnoid hemorrhage (aSAH), including the Nijmegen acute subarachnoid hemorrhage calculator (Nutshell). Generalizability of such models to external data remains unclear. To externally validate the performance of the Nutshell tool while comparing it with the conventional Subarachnoid Hemorrhage International Trialists (SAHIT) models and to review the ML literature on outcome prediction after aSAH and aneurysm treatment. A prospectively maintained database of patients with aSAH presenting consecutively to our institution in the 2013 to 2018 period was used. The web-based Nutshell and SAHIT calculators were used to derive the risks of poor long-term (12-18 months) outcomes and 30-day mortality. Discrimination was evaluated using the area under the curve (AUC), and calibration was investigated using calibration plots. The literature on relevant ML models was surveyed for a synopsis. In 269 patients with aSAH, the SAHIT models outperformed the Nutshell tool (AUC: 0.786 vs 0.689, P = .025) in predicting long-term functional outcomes. A logistic regression model of the Nutshell variables derived from our data achieved adequate discrimination (AUC = 0.759) of poor outcomes. The SAHIT models outperformed the Nutshell tool in predicting 30-day mortality (AUC: 0.810 vs 0.636, P < .001). Calibration properties were more favorable for the SAHIT models. Most published aneurysm-related ML-based outcome models lack external validation and usable testing platforms. The Nutshell tool demonstrated limited performance on external validation in comparison with the SAHIT models. External validation and the dissemination of testing platforms for ML models must be emphasized.

Thromboelastography Indices for Predicting Outcomes After Aneurysmal Subarachnoid Hemorrhage: A Prospective Study.

A non-reactive pupil in standard pupillary light reflex (sPLR) is regarded as a factor predicting neurological sequelae at 1-month after carbon monoxide (CO) poisoning. An automated pupillometer is used in the intensive care unit to quantitatively assess PLR. Quantitative PLR (qPLR) was superior to sPLR using penlight for prognosis of various neurological diseases. Therefore, this study aimed to analyze whether quantitative pupillary variables (neurological Pupil index [NPi] and qPLR) are superior to sPLR in predicting 1-month neurocognitive sequelae after acute CO poisoning. We performed a prospective observational study of consecutive patients with acute CO poisoning admitted to an emergency department (ED) between August 2019 and December 2020 in a single academic medical center. sPLR and pupillometer examinations (qPLR and NPi) were performed by emergency physicians at the ED on hospital days 0-2. The lowest values among those recorded within 24 h and during the total measurement period were considered the 24-h and total lowest values, respectively. Global Deterioration Scale scores were measured at 1 month as an outcome and were dichotomized into favorable (1-4) or poor (5-7) outcomes. We analyzed the data of 104 adult patients with acute CO poisoning. qPLR was significantly higher in the favorable outcome group than in the poor outcome group 24-h and total lowest values (21.2% vs. 15.0%, p = 0.006 and 21.0% vs. 14.8%, p = 0.006). qPLR <18% had fair predictive power for poor neurocognitive outcomes [area under the curve (AUC), 0.70; 95% confidence interval (0.60-0.78)]. Among the patients with decreased mental status (Glasgow Coma Scale ≤12), the power of NPi and qPLR increased [AUC, 0.72 and AUC, 0.80]. NPi < 1 and qPLR <18% showed sensitivity (9.5% vs. 76.2%) and specificity (98.8% vs. 67.5%) for the prediction of poor outcomes. qPLR was significantly superior to sPLR in predicting poor neurocognitive outcomes at 1 month after CO poisoning (p = 0.007). qPLR and NPi were superior to sPLR in terms of predicting poor neurocognitive outcomes. qPLR and NPi measured from hospital days 0-2 may be valuable in predicting neurocognitive outcome.

Study question What is CHLOE’s (Fairtility) efficacy of prediction of blastulation (at 30hpi), pregnancy and ongoing clinical pregnancy following single embryo transfer (SET)? Summary answer CHLOE(Fairtility) algorithms are effective predictors of blastulation, ploidy, pregnancy, implantation and ongoing clinical pregnancy What is known already Time-lapse incubators have increased the amount of information available to the embryologist to help determine the fate of embryos. This has led to differences in clinical practice between clinics in how this information is prioritised. Moreover, inter-operator inconsistencies and the time-consuming nature of manually annotating time-lapse videos are challenges currently experienced by time-lapse users that can be relieved with Artificial Intelligence(AI) tools, such as CHLOE(Fairtility). CHLOE levergaes AI-based predictors to predict blastulation and implantation, whilst providing transparency to which biological characteristics have led to that determination. There is a need to validate AI tools before their incorporation into clinical practice. Study design, size, duration This was a single centre study that took place between 2017-2020, at Istanbul Memorial Sisli Hospital in Turkey, ART and Center. This was a retrospective cohort analysis that reviewed 6748 time-lapse videos containing 5392 cleaved embryos, 3763 blastocysts, 877 single embryo transfers(SET) with known ongoing pregnancy outcome (KOPO), 306 euploid SETs and 25 mosaic embryo SETs with KOPO. CHLOE blastocyst and implantation score efficacy of prediction of clinical outcomes was quantified using the metric AUC. Participants/materials, setting, methods Time-lapse videos were assessed using CHLOE(Fairtility), an AI based tool, to quantify quantitative and qualitative morphokinetics (including automated annotations of tPNa,tPNf,t2,t3,t4,t5,t6,t7,t8,t9,tM,tSB,tB,tEB), CHLOE implantation score and CHLOE blastocyst score (calculated at 30hpi) relative to laboratory (ploidy results, blastulation) and clinical outcomes (biochemical, clinical and ongoing pregnancy) following overall SET. Binary logistic regression was used to calculate area under the curve (AUC) as a measure of prediction efficacy. Main results and the role of chance Blastulation score assessment of cleaved embryos was predictive of blastulation (AUC=0.96, baseline=70% n = 5392, p &amp;lt; 0.001). Following PGT-A, implantation score was predictive of euploids (AUC=0.61, baseline=34%, n = 1456, p &amp;lt; 0.001), but not of embryos classified as mosaics (AUC=0.5, baseline=19%, n = 1456, p &amp;gt; 0.05). Following SET, implantation score was predictive of biochemical (AUC=0.71, baseline=49%, n = 866, p &amp;lt; 0.001), clinical and ongoing pregnancy rate (AUC=0.69, baseline=37%, n = 866, p &amp;lt; 0.001). Following SET of non-PGT-A embryos, implantation score decreased with increasing patient age (p &amp;lt; 0.001). The type of aneuploidy (such as monosomy, trisomy, segmental) did not affect implantation score or blastulation score (p &amp;gt; 0.05). Implantation score prediction of outcome was higher for non-PGT-A transfers than overall transfers for biochemical (Non-PGTA: AUC=0.73, baseline=33%, n = 535, p &amp;lt; 0.001; OVERALL: AUC=0.71, baseline=49%, n = 866, p &amp;lt; 0.001), clinical and ongoing pregnancy (Non-PGTA: AUC=0.76, baseline=24%, n = 535, p &amp;lt; 0.001; OVERALL: AUC=0.69, baseline=37%, n = 866, p &amp;lt; 0.001), despite lower baselines. Limitations, reasons for caution This is a single centre study, using retrospective data where embryos were selected for transfer by human embryologists. Despite the data has heterogeneity in terms of clinical features, the study is part of a larger framework for responsible incorporation of AI into clinical practice through robust validation. Wider implications of the findings AI-based tools have the potential of increasing consistency, efficiency and efficacy of embryo selection. The additional information on quantitative and qualitative morphokinetics that AI tools such as CHLOE provide, bring transparency to the prediction, allowing for improvement in personalisation of care down to each individual embryo. Trial registration number None

Background: Subarachnoid hemorrhage is a devastating disease. Even after state-of-the-art treatment patients suffer from complications, including cerebral vasospasm (CVS), delayed cerebral ischemia (DCI), and chronic hydrocephalus (CH) following aneurysmal subarachnoid hemorrhage (aSAH). The aim of our study is to identify the predictive value of the C-reactive protein to lymphocyte ratio (CLR) for neurological functional outcome and complications after aSAH. Methods: We retrospectively analyzed a total of 166 aSAH patients who met the inclusion criteria enrolled in our study. Multivariate logistic regression analyses were performed to evaluate the independent risk factors. The predictive value of different models was compared by calculating the areas under the receiver operating characteristic (ROC) curve. Results: On-admission levels of CLR in patients with poor outcomes (6 months mRS 3-6), CVS, DCI, and CH were significantly higher than those in patients with good outcomes (6 months mRS 0-2), non-CVS, non-DCI, and non-CH. Multivariate logistic regression analysis revealed that admission CLR was independently associated with CVS (OR [95% CI] 2.116 [1.507-2.971]; p < 0.001), and DCI (OR [95% CI] 1.594 [1.220-2.084]; p = 0.001). In ROC analysis, the area under the curve (AUC) of CLR for poor outcomes (6 months mRS 3-6), CVS, DCI, and CH prediction were (AUC [95% CI] 0.639 [0.555-0.724]; p = 0.002), (AUC [95% CI] 0.834 [0.767-0.901]; p < 0.001), (AUC [95% CI] 0.679 [0.581-0.777]; p < 0.001), and (AUC [95% CI] 0.628 [0.543-0.713]; p = 0.005) revealing that admission CLR had a favorable predictive value for CVS after aSAH. The sensitivity and specificity of admission CLR for CVS prediction were 77.1% and 75.4%. On-admission CLR of 0.757 mg × 10-6 was identified as the best cutoff threshold to discriminate between CVS and non-CVS (CVS: CLR < 0.757 mg × 10-6 11/100 [11.0%] vs. CLR ≥ 0.757 mg × 10-6 37/66 [56.1%]; p < 0.001). Conclusions: High levels of on-admission CLR serve as an independent risk factor for CVS and DCI after aSAH. Admission CLR is an easy-to-quantify laboratory parameter that efficiently predicts the CVS after aSAH, which can provide some guidance for clinicians to evaluate for possible progression and treatment strategies in patients with aSAH.

Aneurysmal subarachnoid hemorrhage (aSAH) is characterized by high morbidity and mortality proceeding from the initial severity and following complications of aSAH. Various scores have been developed to predict these risks. We aimed to analyze the clinical value of different radiographic scores for prognostication of aSAH outcome. Initial computed tomography scans (≤48 h after ictus) of 745 aSAH cases treated between January 2003 and June 2016 were reviewed with regard to Subarachnoid Hemorrhage Early Brain Edema Score (SEBES), and Claassen, Barrow Neurological Institute (BNI), Hijdra, original Graeb and Fisher scale scores. The primary endpoints were development of delayed cerebral ischemia (DCI), in-hospital mortality and unfavorable outcome (modified Rankin Scale score >3) at 6months after subarachnoid hemorrhage. Secondary endpoints included the different complications that can occur during aSAH. Clinically relevant cutoffs were defined using receiver-operating characteristic curves. The radiographic scores with the highest values for area under the curve (AUC) were included in the final multivariate analysis. The Hijdra sum score had the most accurate predictive value and independent associations with all primary endpoints: DCI (AUC0.678, adjusted odds ratio [aOR]2.83; p < 0.0001); in-hospital mortality (AUC0.704, aOR2.83; p < 0.0001) and unfavorable outcome (AUC0.726, aOR2.91; p < 0.0001). Multivariate analyses confirmed the independent predictive value of the radiographic scales for risk of decompressive craniectomy (SEBES and Fisher score), cerebral vasospasm (SEBES, BNI score and Fisher score) and shunt dependency (Hijdra ventricle score and Fisher score) after aSAH. Initial radiographic severity of aSAH was independently associated with occurrence of different complications during aSAH and the final outcome. The Hijdra sum score showed the highest diagnostic accuracy and robust predictive value for early detection of risk of DCI, in-hospital mortality and unfavorable outcome after aSAH.

Background and Purpose- Early prediction of clinical outcome after aneurysmal subarachnoid hemorrhage (aSAH) is still lacking accuracy. In this observational cohort study, we aimed to develop and validate an accurate bedside prediction model for clinical outcome after aSAH, to aid decision-making at an early stage. Methods- For the development of the prediction model, a prospectively kept single-center cohort of 1215 aSAH patients, admitted between 1998 and 2014, was used. For temporal validation, a prospective cohort of 224 consecutive aSAH patients from the same center, admitted between 2015 and 2017, was used. External validation was performed using the ISAT (International Subarachnoid Aneurysm Trial) database (2143 patients). Primary outcome measure was poor functional outcome 2 months after aSAH, defined as modified Rankin Scale score 4-6. The model was constructed using multivariate regression analyses. Performance of the model was examined in terms of discrimination and calibration. Results- The final model included 4 predictors independently associated with poor outcome after 2 months: age, World Federation of Neurosurgical Societies grade after resuscitation, aneurysm size, and Fisher grade. Temporal validation showed high discrimination (area under the receiver operating characteristic curve, 0.90; 95% CI, 0.85-0.94), external validation showed fair to good discrimination (area under the receiver operating characteristic curve, 0.73; 95% CI, 0.70-0.76). The model showed satisfactory calibration in both validation cohorts. The SAFIRE grading scale was derived from the final model: size of the aneurysm, age, Fisher grade, world federation of neurosurgical societies after resuscitation. Conclusions- The SAFIRE grading scale is an accurate, generalizable, and easily applicable model for early prediction of clinical outcome after aSAH.

Introduction: Several prognostic scores have been developed to predict patient outcome after ischemic stroke. We aimed to compare eight scores in the IMS -III dataset. Hypothesis: Prediction of good and poor outcome using different scores is possible in IMS-III. Methods: We retrospectively calculated area under the curve (AUC), sensitivity, and specificity for poor (mRS 4-6) and good (mRS 0-2) 90-day outcome in all IMS-III participants with complete data. The scoring systems analyzed were: Houston Intra-arterial Therapy 2 (HIAT2), Ischemic Stroke Predictive Risk Score (iScore), Totaled Health Risks in Vascular Events (THRIVE), DRAGON, Hemorrhage after Thrombolysis (HAT), Alberta Stroke Programme Early CT Score (ASPECTS), Stroke-Thrombolytic Predictive Instrument (TPI), and Simple Variables Model (SVM). Those receiving IV rtPA followed by endovascular therapy (treatment) and those receiving IV rtPA only (control) were analyzed separately. Results: Among 656 IMS III subjects, 651 had complete data, including 429 treatment and 222 control subjects. Overall, all scoring systems showed good prediction abilities (AUC range 0.60 to 0.78). AUC comparisons did not clearly favor a single scoring system. Treatment group: For predicting poor outcome, the TPI score outperformed ASPECTS (AUC 0.73 vs 0.61; P &lt;0.05). For predicting good outcome, the HAT score was significantly better than ASPECTS (0.73 vs. 0.64; P &lt;0.05). Control group: For poor outcome, the TPI score was significantly better than ASPECTS and THRIVE (0.78 vs. 0.62 and 0.71, respectively; P &lt;0.05). For good outcome, TPI performed better than ASPECTS and THRIVE (0.73 vs. 0.61 and 0.67, respectively; P &lt;0.05). Conclusions: Outcome prediction using different scores in IMS III is possible. HIAT2, iSCORE, DRAGON, HAT, TPI, and SVM all performed comparably. Prognostic performances were comparable in both treatment arms.

Neutrophil Related Data Obtained From Newly Developed Automatic Hematology Analyzer Sysmex XN-2000 Can Provide Useful Informations For The Discrimination Of Sepsis Patients

Quantitative estimation of the hemorrhage volume associated with aneurysm rupture is a new tool of assessing prognosis. To determine the prognostic value of the quantitative estimation of the amount of bleeding after aneurysmal subarachnoid hemorrhage, as well the relative importance of this factor related to other prognostic indicators, and to establish a possible cut-off value of volume of bleeding related to poor outcome. A prospective cohort of 206 patients consecutively admitted with the diagnosis of aneurysmal subarachnoid hemorrhage to Hospital 12 de Octubre were included in the study. Subarachnoid, intraventricular, intracerebral, and total bleeding volumes were calculated using analytic software. For assessing factors related to prognosis, univariate and multivariate analysis (logistic regression) were performed. The relative importance of factors in determining prognosis was established by calculating their proportion of explained variation. Maximum Youden index was calculated to determine the optimal cut point for subarachnoid and total bleeding volume. Variables independently related to prognosis were clinical grade at admission, age, and the different bleeding volumes. The proportion of variance explained is higher for subarachnoid bleeding. The optimal cut point related to poor prognosis is a volume of 20 mL both for subarachnoid and total bleeding. Volumetric measurement of subarachnoid or total bleeding volume are both independent prognostic factors in patients with aneurysmal subarachnoid hemorrhage. A volume of more than 20 mL of blood in the initial noncontrast computed tomography is related to a clear increase in poor outcome risk. : aSAH, aneurysmal subarachnoid hemorrhage.

Subarachnoid hemorrhage (SAH) is a common and frequently devastating condition, accounting for ≈5% of all strokes and affecting as many as 30 000 Americans each year.1,2 The American Heart Association (AHA) previously published “Guidelines for the Management of Aneurysmal Subarachnoid Hemorrhage.”3 Since then, considerable advances have been made in endovascular techniques, diagnostic methods, and surgical and perioperative management paradigms. Nevertheless, outcome for patients with SAH remains poor, with population-based mortality rates as high as 45% and significant morbidity among survivors.4–9 Several multicenter, prospective, randomized trials and prospective cohort analyses have influenced treatment protocols for SAH. However, rapid evolution of newer treatment modalities, as well as other practical and ethical considerations, has meant that rigorous clinical scientific assessment of the treatment protocols has not been feasible in several important areas. To address these issues, the Stroke Council of the AHA formed a writing group to reevaluate the recommendations for management of aneurysmal SAH. A consensus committee reviewed existing data in this field and prepared the recommendations in 1994.3 In an effort to update those recommendations, a systematic literature review was conducted based on a search of MEDLINE to identify all relevant randomized clinical trials published between June 30, 1994, and November 1, 2006 (search terms: subarachnoid hemorrhage , cerebral aneurysm , trial ; Table 1). Each identified article was reviewed by at least 2 members of the writing group. Selected articles had to meet one of the following criteria to be included: randomized trial or nonrandomized concurrent cohort study. Case series and nonrandomized historical cohort studies were reviewed if no studies with a higher level of evidence were available for a particular topic covered in the initial guidelines. These were chosen on the basis of sample size and the relevance of the particular studies to subjects that …

PurposeThe technique of color-coding blood flow analysis was used to explore the correlation between the microcirculatory hemodynamic changes on digital subtraction angiography (DSA) images in patients with aneurysmal subarachnoid hemorrhage (SAH) at the early stage and functional outcomes at discharge.MethodsData of 119 patients who underwent DSA examination due to SAH were retrospectively analyzed. The following hemodynamic parameters of the four region of interests (ROIs) [an ophthalmic segment of the internal carotid artery (ICA), frontal and parietal lobe, and superior sagittal sinus] were analyzed: the time-to-peak (TTP), the area under the curve (AUC), the full width at half maximum (FWHM), mean transit time (MTT), and circulation time. Multifactor regression analysis was performed to explore the correlation between the hemodynamic parameters and functional outcomes in patients at discharge.ResultsOf 119 patients with SAH, good and poor outcomes were found in 83 (69.7%) and 36 (30.3%) patients, respectively. The hemodynamic parameters including the FWHM, relative TTP (rTTP), and circulation time were significantly correlated with the Hunt–Hess grade (p < 0.005, p = 0.03, and p < 0.005) and the World Federation of Neurological Societies Scale grade (p < 0.005, p = 0.02, and p = 0.01). The FWHM was significantly prolonged with the increase of modified Fisher grade (p = 0.02). The multifactor analysis showed that the FWHM [odds ratio (OR) 17.56, 95% CI: 1.13–272.03, p = 0.04] was an independent risk factor predicting the functional outcomes in patients at discharge.ConclusionThe technique of color-coding blood flow analysis could be suitable for the qualified evaluation of disease conditions at an early stage of SAH as well as the prediction of outcomes.

To assess the predictive accuracy of advanced AI language models and established clinical scales in prognosticating outcomes for patients with aneurysmal subarachnoid hemorrhage (aSAH). This retrospective cohort study included 82 patients suffering from aSAH. We evaluated the predictive efficacy of AtlasGPT and ChatGPT 4.0 by examining the area under the curve (AUC), sensitivity, specificity, and Youden’s Index, in comparison to established clinical grading scales such as the World Federation of Neurological Surgeons (WFNS) scale, Simplified Endovascular Brain Edema Score (SEBES), and Fisher scale. This assessment focused on four endpoints: in-hospital mortality, need for decompressive hemicraniectomy, and functional outcomes at discharge and after 6-month follow-up. In-hospital mortality occurred in 22% of the cohort, and 34.1% required decompressive hemicraniectomy during treatment. At hospital discharge, 28% of patients exhibited a favorable outcome (mRS ≤ 2), which improved to 46.9% at the 6-month follow-up. Prognostication utilizing the WFNS grading scale for 30-day in-hospital survival revealed an AUC of 0.72 with 59.4% sensitivity and 83.3% specificity. AtlasGPT provided the highest diagnostic accuracy (AUC 0.80, 95% CI: 0.70–0.91) for predicting the need for decompressive hemicraniectomy, with 82.1% sensitivity and 77.8% specificity. Similarly, for discharge outcomes, the WFNS score and AtlasGPT demonstrated high prognostic values with AUCs of 0.74 and 0.75, respectively. Long-term functional outcome predictions were best indicated by the WFNS scale, with an AUC of 0.76. The study demonstrates the potential of integrating AI models such as AtlasGPT with clinical scales to enhance outcome prediction in aSAH patients. While established scales like WFNS remain reliable, AI language models show promise, particularly in predicting the necessity for surgical intervention and short-term functional outcomes. The study explored the use of advanced AI language models, AtlasGPT and ChatGPT 4.0, to predict outcomes for patients with aneurysmal subarachnoid hemorrhage (aSAH). It found that AtlasGPT provided the highest diagnostic accuracy for predicting the need for decompressive hemicraniectomy, outperforming traditional clinical scales, while both AI models showed promise in enhancing outcome predictions when integrated with established clinical assessment tools.

Background and PurposeOutcome prediction after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and large vessel occlusion (LVO) is commonly performed by focusing on favorable outcome (modified Rankin Scale, mRS 0–2) after 3 months but poor outcome representing severe disability and mortality (mRS 5 and 6) might be of equal importance for clinical decision-making.MethodsWe retrospectively analyzed patients with AIS and LVO undergoing MT from 2009 to 2018. Prognostic variables were grouped in baseline clinical (A), MRI-derived variables including mismatch [apparent diffusion coefficient (ADC) and time-to-maximum (Tmax) lesion volume] (B), and variables reflecting speed and extent of reperfusion (C) [modified treatment in cerebral ischemia (mTICI) score and time from onset to mTICI]. Three different scenarios were analyzed: (1) baseline clinical parameters only, (2) baseline clinical and MRI-derived parameters, and (3) all baseline clinical, imaging-derived, and reperfusion-associated parameters. For each scenario, we assessed prediction for favorable and poor outcome with seven different machine learning algorithms.ResultsIn 210 patients, prediction of favorable outcome was improved after including speed and extent of recanalization [highest area under the curve (AUC) 0.73] compared to using baseline clinical variables only (highest AUC 0.67). Prediction of poor outcome remained stable by using baseline clinical variables only (highest AUC 0.71) and did not improve further by additional variables. Prediction of favorable and poor outcomes was not improved by adding MR-mismatch variables. Most important baseline clinical variables for both outcomes were age, National Institutes of Health Stroke Scale, and premorbid mRS.ConclusionsOur results suggest that a prediction of poor outcome after AIS and MT could be made based on clinical baseline variables only. Speed and extent of MT did improve prediction for a favorable outcome but is not relevant for poor outcome. An MR mismatch with small ischemic core and larger penumbral tissue showed no predictive importance.

With the aging of the world population, the number of elderly patients suffering from aneurysmal subarachnoid hemorrhage (aSAH) is gradually growing. We aim to investigate the potential association between plasma ALT level and clinical complications of elderly aSAH patients, and explore its predictive value for clinical outcomes of elderly aSAH patients. Between January 2013 and March 2018, 152 elderly aSAH patients were analyzed in this study. Clinical information, imaging findings and laboratory data were reviewed. According to the Glasgow Outcome Scale (GOS), clinical outcomes at 3 months were classified into favorable outcomes (GOS 4-5) and poor outcomes (GOS 1-3). Logistic regression analysis was used to assess the indicators associated with poor outcomes, and receiver curves (ROC) and corresponding area under the curve (AUC) were used to detect the accuracy of the indicator. A total of 48 (31.6 %) elderly patients with aSAH had poor outcome at 3 months. In addition to ICH, IVH, Hunt-Hess 4 or 5 Grade and Modified Fisher 3 or 4 Grade, plasma ALT level was also strongly associated with poor outcome of elderly aSAH patients. After adjusting for other covariates, plasma ALT level remained independently associated with pulmonary infection (OR 1.05; 95% CI 1.00-1.09; P = 0.018), cardiac complications (OR 1.05; 95% CI 1.01-1.08; P = 0.014) and urinary infection (OR 1.04; 95% CI 1.00-1.08; P = 0.032). Besides, plasma ALT level had a predictive ability in the occurrence of systemic complications (AUC 0.676; 95% CI: 0.586- 0.766; P<0.001) and poor outcome (AUC 0.689; 95% CI: 0.605-0.773; P<0.001) in elderly aSAH patients. Plasma ALT level of elderly patients with aSAH was significantly associated with systemic complications, and had additional clinical value in predicting outcomes. Given that plasma ALT levels on admission could help to identify high-risk elderly patients with aSAH, these findings are of clinical relevance.