Health Care Expenditures Associated with Delayed Cerebral Ischemia Following Subarachnoid Hemorrhage: A Propensity-Adjusted Analysis

Systematic review and meta-analysis of transcranial doppler biomarkers for the prediction of delayed cerebral ischemia following subarachnoid hemorrhage

Delayed cerebral ischemia (DCI) following an aneurysmal subarachnoid hemorrhage (aSAH) significantly impacts mortality, morbidity, and healthcare costs. This study assessed the diagnostic accuracy of Transcranial Doppler (TCD)-derived biomarkers for predicting DCI via a systematic review and meta-analysis. Included studies had to correctly define DCI and report data on sensitivity, specificity, positive predictive value, and negative predictive value. Univariate or bivariate analyses with a random effects model were used, and risk of bias was evaluated with the Quality Assessment of Diagnostic Accuracy Studies. From 23 eligible articles (n = 2371 patients), three biomarker categories were identified: cerebral blood flow velocities (CBFV), cerebral autoregulation, and microembolic signals (MES). The highest sensitivity (0.86, 95% CI 0.71-0.94) and specificity (0.75, 95% CI 0.52-0.94) for DCI prediction were achieved with a mean CBFV of 120 cm/s combined with a Lindegaard ratio. The transient hyperemic response test showed the best performance among autoregulatory biomarkers with a sensitivity of 0.88, (95% CI 0.54-0.98) and specificity of 0.82 (95% CI 0.52-0.94). MES were less effective predictors. Combining CBFV with autoregulatory biomarkers enhanced TCD's predictive value. High heterogeneity and risk of bias were noted, indicating the need for a standardized TCD approach for improved DCI evaluation.

Relationship between age and delayed cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage requiring invasive mechanical ventilation: a secondary analysis

The association between age and delayed cerebral ischemia (DCI) has long been controversial. This study aimed to explore the relationship between age and DCI in patients with aneurysmal subarachnoid hemorrhage (aSAH) requiring invasive mechanical ventilation (IMV). This study reanalyzed previously published data from a French university hospital. Chalard K et al. conducted a retrospective cohort study in the intensive care unit from 2010 to 2015. Univariate and multivariate logistic regression analyses were employed to identify risk factors for DCI. The non-linear relationship between age and DCI was estimated through a restricted cubic spline regression, and a two-piecewise linear regression model was further performed to calculate the threshold effect. The analysis included 222 patients with aSAH requiring IMV. Their median age was 56 years (range 19-84 years). Patients were equally divided into four groups: Q1 (19-45 years), Q2 (46-55 years), Q3 (56-63 years), and Q4 (64-84 years). The incidence of DCI was 27.48% (N = 61). After adjustment for potential confounders, patients in group Q2 were found to be more likely to develop DCI [odds ratio (OR), 4.91; 95% Confidence Interval (CI), 1.53-15.70] compared to those in group Q1. After adjusting for confounding variables, for patients aged < 53 years, the risk of DCI increased by 14% (OR, 1.14; 95% CI 1.05-1.23) for each 1-year increase in age. For patients aged 53 to 77 years, the risk of DCI decreased by 19% (OR, 0.81; 95% CI 0.73-0.88) for each 1-year increase. For patients aged > 77 years, the risk of DCI did not decrease with increasing age (OR, 1.42; 95% CI 0.47-4.34). Age was non-linearly associated with DCI in patients with aSAH requiring IMV.

Big Data-Enabled Analysis of Factors Affecting Medical Expenditure in the Cerebral Infarction of a Developing City in Western China

Purpose

Cerebral infarction (CI) has been one of the leading causes of death in China since 2017, and controlling the medical expenses of this disease is an urgent issue for the Chinese government. This study aims to explore the important factors that affect the hospitalization expenses of CI patients and to provide a scientific basis for establishing a reasonable reimbursement mechanism and hospitalization expense standard for CI patients.

Methods

Data from 109,314 inpatients from the Healthcare Security Administration of Chengdu in western China from January 2016 to December 2018 were utilized. Descriptive statistical analysis was used for variable characteristic analysis. The Mann-Whitney test and Kruskal-Wallis test were used for single-factor analysis, and multiple linear stepwise regression was used for single-factor analysis and multiple-factor analysis.

Results

This study found that the average direct economic burden of CI in Chengdu was approximately 10,569 Chinese yuan (CNY), about 1450 US dollars, the average length of stay (LOS) was 14.47 days, the indirect economic burden was approximately 2817 CNY, and the total economic burden was 13,386 CNY for a CI inpatient. Gender, insurance type, grade of medical institution, the level of payment type, age, LOS, and complications and comorbidities (CCs) are the most important factors affecting CI medical costs.

Conclusion

Citizens should improve their lifestyle habits to reduce disease risk to avoid the associated medical and economic burdens. Hospitals should improve their medical technology to decrease the LOS and reduce direct medical costs. The government should actively promote the hierarchical diagnosis and treatment policy to reduce the waste of medical resources caused by low-acuity patients going to high-level hospitals for treatment. The National Healthcare Security Administration should optimize the medical insurance payment method and establish a corresponding mechanism to reduce the occurrence of excessive medical treatments such as overuse.