Dataset Information

Machine Learning to Predict Delayed Cerebral Ischemia and Outcomes in Subarachnoid Hemorrhage.

ABSTRACT:

Objective

To determine whether machine learning (ML) algorithms can improve the prediction of delayed cerebral ischemia (DCI) and functional outcomes after subarachnoid hemorrhage (SAH).

Methods

ML models and standard models (SMs) were trained to predict DCI and functional outcomes with data collected within 3 days of admission. Functional outcomes at discharge and at 3 months were quantified using the modified Rankin Scale (mRS) for neurologic disability (dichotomized as good [mRS ≤ 3] vs poor [mRS ≥ 4] outcomes). Concurrently, clinicians prospectively prognosticated 3-month outcomes of patients. The performance of ML, SMs, and clinicians were retrospectively compared.

Results

DCI status, discharge, and 3-month outcomes were available for 399, 393, and 240 participants, respectively. Prospective clinician (an attending, a fellow, and a nurse) prognostication of 3-month outcomes was available for 90 participants. ML models yielded predictions with the following area under the receiver operating characteristic curve (AUC) scores: 0.75 ± 0.07 (95% confidence interval [CI] 0.64-0.84) for DCI, 0.85 ± 0.05 (95% CI 0.75-0.92) for discharge outcome, and 0.89 ± 0.03 (95% CI 0.81-0.94) for 3-month outcome. ML outperformed SMs, improving AUC by 0.20 (95% CI -0.02 to 0.4) for DCI, by 0.07 ± 0.03 (95% CI -0.0018 to 0.14) for discharge outcomes, and by 0.14 (95% CI 0.03-0.24) for 3-month outcomes and matched physician's performance in predicting 3-month outcomes.

Conclusion

ML models significantly outperform SMs in predicting DCI and functional outcomes and has the potential to improve SAH management.

SUBMITTER: Savarraj JPJ

PROVIDER: S-EPMC7905786 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Delayed cerebral ischemia (DCI) is a common secondary complication and an important cause of disability and mortality among patients who survive aneurysmal subarachnoid hemorrhage (aSAH). Knowledge on DCI pathogenesis, risk factors, and biomarkers are essential for early detection and improved prognosis. To investigate the role of DNA methylation in DCI risk, we conducted an epigenome-wide association study (EWAS) in 68 patients followed up to 1 year after the initial aneurysm rupture. Blood samples were collected within 48 h post hemorrhage and used for DNA methylation profiling at ~ 450k CpG sites. A separate cohort of 175 patients was sequenced for the top CpG sites from the discovery analysis for a replication of the EWAS findings. EWAS did not identify any epigenome-wide significant CpGs. The top signal, cg18031596, was annotated to ANGPT1, a gene with critical functions in angiogenesis after vascular injury. Post hoc power calculations indicated a well-powered discovery analysis for cg18031596. Analysis of the replication cohort showed that four out of the five CpG sites sequenced at the ANGPT1 locus passed a Bonferroni-adjusted significance threshold. In a pooled analysis of the entire sample, three out of five yielded a significant p-value, and the top association signal (p-value = 0.004) was seen for a CpG that was not originally measured in the discovery EWAS. However, four ANGPT1 CpG sites had an opposite effect direction in the replication analysis compared to the discovery EWAS, marking a failure of replication. We carefully examined this observed flip in directions and propose several possible explanations in addition to that it was a random chance that ANGPT1 ranked at the top in the discovery EWAS. We failed to demonstrate a significant and consistent effect of ANGPT1 methylation in DCI risk in two cohorts. Though the replication attempt to weaken the overall support of this gene, given its relevant function and top rank of significance in the EWAS, our results call for future studies of larger aSAH cohorts to determine its relevance for the occurrence of DCI.