Spot Sign in Secondary Intraventricular Hemorrhage Predicts Early Neurological Decline

Soun, Jennifer E.; Montes, Daniel; Yu, Fang; Morotti, Andrea; Qureshi, Adnan I; Barnaure, Isabelle; Rosand, Jonathan; Goldstein, Joshua N.; Romero, Javier; Atach-Ii,; investigators, Nett

doi:10.1007/s00062-019-00857-2

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…Further integration of quantitative imaging characteristics may hold additional prognostic value [9]. In the past, specific CT markers and histogram-based analyses of ICH heterogeneity have been linked to poor clinical outcome and reinforce this notion [14][15][16]. The goal of this study was twofold: First, we hypothesized that quantitative radiomic filter-and texturederived high-end image features extracted from nonenhanced computed tomography (NECT) brain scans can be used to predict clinical outcome of ICH patients.…”

Section: Introductionmentioning

confidence: 99%

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

Nawabi

Kniep

Elsayed

et al. 2021

Transl. Stroke Res.

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We hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional outcome at mRS 2, 3, and 4. Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach. Receiver-operating-characteristic area under the curve (ROC AUC) of the machine learning classifier using image features only was 0.80 (95% CI [0.77; 0.82]) for predicting mRS ≤ 2, 0.80 (95% CI [0.78; 0.81]) for mRS ≤ 3, and 0.79 (95% CI [0.77; 0.80]) for mRS ≤ 4. Trained on survival prediction (mRS ≤ 5), the classifier reached an AUC of 0.80 (95% CI [0.78; 0.82]) which was equivalent to results of the ICH Score. If combined, the integrated model showed a significantly higher AUC of 0.84 (95% CI [0.83; 0.86], P value <0.05). Accordingly, sensitivities were significantly higher at Youden Index maximum cut-offs (77% vs. 74% sensitivity at 76% specificity, P value <0.05). Machine learning–based evaluation of quantitative high-end image features provided the same discriminatory power in predicting functional outcome as multidimensional clinical scoring systems. The integration of conventional scores and image features had synergistic effects with a statistically significant increase in AUC.

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Section: Introductionmentioning

confidence: 99%

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

Nawabi

Kniep

Elsayed

et al. 2021

Transl. Stroke Res.

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“…33,34 Alternatively, in a recent randomized clinical trial, spot sign was related to the early neurological decline in patients with secondary IVH extension and had a negative impact on outcome, but they found no association between spot sign or early neurological decline with IVH extension and hydrocephalus. 35 The inflammatory response may contribute to the development of IVH and worsen the prognosis; hence, early intraventricular injection of anti-inflammatory drugs may minimize inflammatory complications and mortality. 29,33 IVH growth is strongly associated with progressive ICH expansion and should be considered as an indicator of worsening clinical status.…”

Section: Risk Factorsmentioning

confidence: 99%

Natural History, Pathophysiology, and Recent Management Modalities of Intraventricular Hemorrhage

Essibayi,

Ibrahim Abdallah,

Mortezaei

et al. 2023

J Intensive Care Med

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Intraventricular hemorrhage (IVH) is a clinical challenge observed among 40–45% of intracerebral hemorrhage (ICH) cases. IVH can be classified according to the source of the hemorrhage into primary and secondary IVH. Primary intraventricular hemorrhage (PIVH), unlike secondary IVH, involves only the ventricles with no hemorrhagic parenchymal source. Several risk factors of PIVH were reported which include hypertension, smoking, age, and excessive alcohol consumption. IVH is associated with high mortality and morbidity and several prognostic factors were identified such as IVH volume, number of ventricles with blood, involvement of fourth ventricle, baseline Glasgow Coma Scale score, and hydrocephalus. Prompt management of patients with IVH is required to stabilize the clinical status of patients upon admission. Nevertheless, further advanced management is crucial to reduce the morbidity and mortality associated with intraventricular bleeding. Recent treatments showed promising outcomes in the management of IVH patients such as intraventricular anti-inflammatory drugs, lumbar drainage, and endoscopic evacuation of IVH, however, their safety and efficacy are still in question. This literature review presents the epidemiology, physiopathology, risk factors, and outcomes of IVH in adults with an emphasis on recent treatment options.

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“…Further integration of disease imaging features may have additional prognostic value ( 14 ). Previous studies have demonstrated that specific CT markers and histogram-based analysis of brain hemorrhage heterogeneity are associated with poorer clinical outcomes ( 19 – 21 ). However, most of the current studies have performed radiomic features feature analysis or clinical feature analysis alone to construct machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Predicting the 90-day prognosis of stereotactic brain hemorrhage patients by multiple machine learning using radiomic features combined with clinical features

Li,

Liang,

Dang

et al. 2024

Front. Surg.

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Hypertensive Intracerebral Hemorrhage (HICH) is one of the most common types of cerebral hemorrhage with a high mortality and disability rate. Currently, preoperative non-contrast computed tomography (NCCT) scanning-guided stereotactic hematoma removal has achieved good results in treating HICH, but some patients still have poor prognoses. This study collected relevant clinical and radiomic data by retrospectively collecting and analyzing 432 patients who underwent stereotactic hematoma removal for HICH from January 2017 to December 2020 at the Liuzhou Workers Hospital. The prognosis of patients after 90 days was judged by the modified Rankin Scale (mRS) scale and divided into the good prognosis group (mRS ≤ 3) and the poor prognosis group (mRS > 3). The 268 patients were randomly divided into training and test sets in the ratio of 8:2, with 214 patients in the training set and 54 patients in the test set. The least absolute shrinkage and selection operator (Lasso) was used to screen radiomics features. They were combining clinical features and radiomic features to build a joint prediction model of the nomogram. The AUCs of the clinical model for predicting different prognoses of patients undergoing stereotactic HICH were 0.957 and 0.922 in the training and test sets, respectively, while the AUCs of the radiomics model were 0.932 and 0.770, respectively, and the AUCs of the combined prediction model for building a nomogram were 0.987 and 0.932, respectively. Compared with a single clinical or radiological model, the nomogram constructed by fusing clinical variables and radiomic features could better identify the prognosis of HICH patients undergoing stereotactic hematoma removal after 90 days.

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Spot Sign in Secondary Intraventricular Hemorrhage Predicts Early Neurological Decline

Cited by 5 publications

References 36 publications

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

Natural History, Pathophysiology, and Recent Management Modalities of Intraventricular Hemorrhage

Predicting the 90-day prognosis of stereotactic brain hemorrhage patients by multiple machine learning using radiomic features combined with clinical features

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