Diffuse Angiogram-Negative Subarachnoid Hemorrhage is Associated with an Intermediate Clinical Course

Clinical Outcomes of Diffuse Angiogram-Negative Subarachnoid Hemorrhage Versus Aneurysmal Subarachnoid Hemorrhage: A Propensity Score-Matched Analysis

BACKGROUND

The outcome of diffuse angiogram-negative subarachnoid hemorrhage (dan-SAH) compared with aneurysmal SAH (aSAH) remains unclear. This study aimed to compare outcomes using propensity score matching.

METHODS AND RESULTS

Sixty-five patients with dan-SAH and 857 patients with aSAH admitted between January 2018 and December 2022 were retrospectively reviewed. Propensity score matching resulted in matching 65 patients with dan-SAH to 260 patients with aSAH, and clinical outcomes were compared between the groups. Compared with patients with dan-SAH, patients with aSAH were more likely to experience rehemorrhage (8.8% versus 0%, P=0.027), death (11.2% versus 1.5%; odds ratios [OR] 8.04 [95% CI, 1.07-60.12]; P=0.042), or delayed cerebral ischemia (12.3% versus 3.1%; OR, 4.42 [95% CI, 1.03-18.95]; P=0.045). Multivariate analysis revealed that Hunt-Hess grade 4 to 5 (OR, 3.13 [95% CI, 2.11-4.64]; P<0.001), presence of intraventricular hemorrhage (OR, 3.58 [95% CI, 1.72-7.46]; P=0.001), and smoking (OR, 2.44 [95% CI, 1.12-5.28]; P=0.024) were independently associated with the incidence of unfavorable outcomes (modified Rankin scale score >2 at 3 months), whereas dan-SAH was not (OR, 0.66 [95% CI, 0.25-1.73]; P=0.40).

CONCLUSIONS

Compared with patients with dan-SAH, patients with aSAH had higher rehemorrhage rates and in-hospital mortality, as well as a higher incidence of delayed cerebral ischemia. Unfavorable outcomes were associated with admission Hunt-Hess grade, the presence of intravenetricular hemorrhage, and smoking history, but there was no relation with the  pathogenesis of the hemorrhage (dan-SAH versus aSAH).

The clinical course and outcomes of non-aneurysmal subarachnoid hemorrhages in a single-center retrospective study

BACKGROUND

Non-aneurysmal subarachnoid hemorrhages (SAHs) are thought to have a benign clinical course compared to aneurysmal SAHs. The aim of this study is to report the clinical course and outcomes of non-aneurysmal SAHs in a large single-center study.

METHODS

The patients with non-aneurysmal SAHs were screened from Tampere University Hospital from 2005 to 2020. The clinical data were collected from the patient's medical records and from the imaging studies. The primary interest was the neurological outcome assessed by dichotomized GOS at 2 months. Multivariable logistic regression was used to study the factors associated with unfavorable outcome.

RESULTS

We found 216 non-aneurysmal SAHs in 214 patients (2 patients with > 1 bleed). Ninety-seven percent of patients with a typical perimesencephalic bleeding pattern SAH (PSAH) (75/77) had a favorable outcome, while 86% of patients with non-perimesencephalic SAH (NPSAH) had a favorable outcome (84/98). In a multivariable logistic regression analysis, loss of consciousness (LOC) (aOR 214.67, 95% CI 17.62-2615.89) and Fisher grade 4 bleeding pattern (aOR 23.32, 95% CI 1.40-387.98) were associated with increased risk for unfavorable outcome (GOS 1-3). Vasospasm was seen in 20% of non-aneurysmal SAH patients, hydrocephalus in 17%, and 13% needed ventriculostomy.

CONCLUSIONS

Non-aneurysmal SAH seems to have a good prognosis for majority of patients, especially for patients with a PSAH. Non-aneurysmal SAH patients are however affected by vasospasm and hydrocephalus and have similar risk factors for poor outcome as patients with aneurysmal SAH. This suggests that it is the severity of the bleed rather than the etiology that associates with poor outcome.

Radiographic predictors of aneurysmal etiology in patients with aneurysmal pattern subarachnoid hemorrhage

OBJECTIVE

Spontaneous angiogram-negative nonperimesencephalic subarachnoid hemorrhage (an-NPSAH) can represent a diagnostic and management dilemma. The authors sought to determine radiographic predictors of aneurysmal etiology based on admission noncontrast head CT scans.

METHODS

The authors performed a retrospective cohort study of prospectively collected data from consecutive patients who were admitted for spontaneous subarachnoid hemorrhage (SAH) with suspected aneurysmal etiology to an academic center from 2016 to 2021. They compared blood thickness in the basal cisterns and sylvian fissures and modified Graeb scores on admission head CT scans between the two groups and subsequently developed a predictive model to identify aneurysmal etiology.

RESULTS

Of 259 included patients (mean age 56 years [SD 12.7 years]; 55% female), 209 had aneurysmal SAH (aSAH) and 50 had an-NPSAH. The median modified Graeb scores were similar for aSAH and an-NPSAH (6 [IQR 2-10] vs 3.5 [IQR 0-8.5], p = 0.33). The mean blood thickness was greater in the sylvian fissure (p = 0.010) and interhemispheric cisterns (p = 0.002), and there was a greater median degree of extension of blood in the sylvian fissures (p = 0.001) in aSAH than in an-NPSAH patients, but the mean blood thickness was less in the prepontine cistern (p = 0.014). The authors' scoring model was constructed based on differences in radiographic features. Receiver operating characteristic curve analysis showed acceptable accuracy in predicting aneurysmal etiology (area under the curve 0.71, 95% CI 0.62-0.79).

CONCLUSIONS

There are differences in radiographic features on admission head CT between an-NPSAH and aSAH patients. The authors' proposed risk stratification model may be considered for further development and use in clinical practice in the future.

ABBREVIATIONS

an-NPSAH = angiogram-negative nonperimesencephalic SAH; aSAH = aneurysmal SAH; DSA = digital subtraction angiography; LOS = length of stay; NCHCT = noncontrast head CT; ROC = receiver operating characteristic; SAH = subarachnoid hemorrhage.

A Machine Learning Approach for the Prediction of Severe Acute Kidney Injury Following Traumatic Brain Injury

BACKGROUND

Acute kidney injury (AKI), a prevalent non-neurological complication following traumatic brain injury (TBI), is a major clinical issue with an unfavorable prognosis. This study aimed to develop and validate machine learning models to predict severe AKI (stage 3 or greater) incidence in patients with TBI.

METHODS

A retrospective cohort study was conducted by using two public databases: the Medical Information Mart for Intensive Care IV (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Recursive feature elimination was used to select candidate predictors obtained within 24 h of intensive care unit admission. The area under the curve and decision curve analysis curves were used to determine the discriminatory ability. On the other hand, the calibration curve was employed to evaluate the calibrated performance of the newly developed machine learning models.

RESULTS

In the MIMIC-IV database, there were 808 patients diagnosed with moderate and severe TBI (msTBI) (msTBI is defined as Glasgow Coma Score < 12). Of these, 60 (7.43%) patients experienced severe AKI. External validation in the eICU-CRD indicated that the random forest (RF) model had the highest area under the curve of 0.819 (95% confidence interval 0.783-0.851). Furthermore, in the calibration curve, the RF model was well calibrated (P = 0.795).

CONCLUSIONS

In this study, the RF model demonstrated better discrimination in predicting severe AKI than other models. An online calculator could facilitate its application, potentially improving the early detection of severe AKI and subsequently improving the clinical outcomes among patients with msTBI.

Management of moderate to severe traumatic brain injury: an update for the intensivist

Traumatic brain injury (TBI) remains one of the most fatal and debilitating conditions in the world. Current clinical management in severe TBI patients is mainly concerned with reducing secondary insults and optimizing the balance between substrate delivery and consumption. Over the past decades, multimodality monitoring has become more widely available, and clinical management protocols have been published that recommend potential interventions to correct pathophysiological derangements. Even while evidence from randomized clinical trials is still lacking for many of the recommended interventions, these protocols and algorithms can be useful to define a clear standard of therapy where novel interventions can be added or be compared to. Over the past decade, more attention has been paid to holistic management, in which hemodynamic, respiratory, inflammatory or coagulation disturbances are detected and treated accordingly. Considerable variability with regards to the trajectories of recovery exists. Even while most of the recovery occurs in the first months after TBI, substantial changes may still occur in a later phase. Neuroprognostication is challenging in these patients, where a risk of self-fulfilling prophecies is a matter of concern. The present article provides a comprehensive and practical review of the current best practice in clinical management and long-term outcomes of moderate to severe TBI in adult patients admitted to the intensive care unit.