Identification of clinical and paraclinical findings predictive for headache occurrence during spontaneous subarachnoid hemorrhage

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review

Background

Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective

This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Methods

A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.

Results

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.

Conclusions

Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

D-dimer may predict poor outcomes in patients with aneurysmal subarachnoid hemorrhage: a retrospective study

Serum biomarkers may play a reliable role in predicting the outcomes of patients with aneurysmal subarachnoid hemorrhage. This study retrospectively analyzed the relationship between serum biomarkers on admission and outcomes in patients with aneurysmal subarachnoid hemorrhage. We recruited 146 patients with aneurysmal subarachnoid hemorrhage who were treated in Renmin Hospital of Wuhan University of China between 1 May 2014 and 30 March 2016. There were 57 males and 89 females included and average age of included patients was 57.03 years old. Serum samples were taken immediately on admission (within 48 hours after initial hemorrhage) and the levels of serum biomarkers were detected. Baseline information, complications, and outcomes at 6 months were recorded. Univariate and multivariate logistic regression analyses were used to explore the relationship between biomarkers and clinical outcomes. Receiver operating characteristic curves were obtained to investigate the possibility of the biomarkers predicting prognosis. Of the 146 patients, 102 patients achieved good outcomes and 44 patients had poor outcomes. Univariate and multivariate analyses showed that high World Federation of Neurosurgical Societies grade, high serum D-dimer levels, and high neurological complications were significantly associated with poor outcomes. Receiver operating characteristic curves verified that D-dimer levels were associated with poor outcomes. D-dimer levels strongly correlated with neurological complications. In conclusion, we suggest that D-dimer levels are a good independent prognostic factor for poor outcomes in patients with aneurysmal subarachnoid hemorrhage.