Shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage

Russo, Paolo di; Carlo, Davide Tiziano Di; Lutenberg, Ariel; Morganti, Riccardo; Evins, Alexander I.; Perrini, Paolo

doi:10.23736/s0390-5616.19.04641-1

Cited by 28 publications

(18 citation statements)

References 39 publications

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“…Patients with age > 55 years, presence of intraventricular hemorrhage, a Fisher grade ≥ 3, a H&H ≥ 3, a history of acute hydrocephalus, a history of CNSI, a history of EVD placement and a history of postoperative LP had higher odds of developing shunt-dependent hydrocephalus. Many of these risk factors have been found in prior studies [5,6,13,14]. Our 14.3% rate of shunt dependence is consistent with past reports [8,14].…”

Section: Discussionsupporting

confidence: 92%

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WITHDRAWN: Shunt-Dependent Hydrocephalus After Aneurysm Subarachnoid Hemorrhage: Predictors and its Relationship with Lumbar Puncture

Shi

Shu

et al. 2021

Preprint

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Objective To identify predictors of shunt-dependent hydrocephalus (SDHC) after aneurysmal subarachnoid hemorrhage (aSAH) and determine the effect of postoperative lumbar puncture (LP) on the incidence of SDHC. Methods We conducted a retrospective review of consecutive patients with aSAH who underwent surgical clipping or endovascular embolization for cerebral aneurysm in our hospital from October 2009 to May 2019. Univariate and multivariate analyses were performed to characterize risk factors for SDHC after aSAH. The incidence of SDHC in the LP and No LP groups were compared after propensity score matching analysis. Results A total of 414 patients were enrolled in this study. SDHC developed in 59 patients (14.3%). On multivariate analysis, age > 55 years (P = 0.004), intraventricular hemorrhage (IVH) (P = 0.038), Fisher grade ≥ 3 (P = 0.043), Hunt and Hess grade (H&H) ≥ 3 (P = 0.027), acute hydrocephalus (P = 0.001), central nervous system infection (CNSI) (P = 0.021), placement of external ventricular drainage (EVD) (P = 0.044), and a history of postoperative LP (P < 0.016) were independently associated with SDHC after aSAH. Propensity score matched cohorts exhibited a significantly higher incidence of SDHC after LP treatment than that without LP (20.7% vs 12.1%, P = 0.019). Receiver operating characteristic analysis revealed an area under the curve of 0.818 for the final regression model. Conclusion Evaluating risk factors can help identify patients at high risk of developing SDHC. Postoperative LP may increase the incidence of SDHC after aSAH. Strategies for treatment of ruptured aneurysms should be used to mitigate SDHC and minimize poor outcomes.

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Section: Discussionsupporting

confidence: 92%

“…Many of these risk factors have been found in prior studies [5,6,13,14]. Our 14.3% rate of shunt dependence is consistent with past reports [8,14]. Our study showed that elderly patients had a higher incidence of shunt dependency than younger patients.…”

Section: Discussionsupporting

confidence: 92%

WITHDRAWN: Shunt-Dependent Hydrocephalus After Aneurysm Subarachnoid Hemorrhage: Predictors and its Relationship with Lumbar Puncture

Shi

Shu

et al. 2021

Preprint

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“…Chronic hydrocephalus following aSAH reportedly contributes to poor neurological outcomes and severe cognitive deficits [ 22 , 23 , 24 , 25 ], which was also found in our cases ( Figure 1 ). The development of predictive models that could stratify patients with aSAH based on their risk of developing shunt-dependent chronic hydrocephalus is important.…”

Section: Introductionsupporting

confidence: 72%

“…In another retrospective study including 1448 aSAH patients, microsurgical clipping conferred a two-fold higher risk of chronic hydrocephalus over coiling alone [ 128 ]. Other studies reported no significant difference for the predictive risks of shunt-dependent hydrocephalus between surgical clipping and coiling groups [ 24 , 129 , 130 , 131 , 132 ]. Also, a multicenter aSAH database in Japan consisting of 566 patients treated for aSAH has revealed that shunt dependency following aSAH occurred significantly more frequently in patients who underwent clipping than in those treated with endovascular coiling (30% vs. 16%) [ 126 ].…”

Section: Coil Vs Clip and Their Relationship With Shunt Dependencymentioning

confidence: 99%

The Pathogenesis of Hydrocephalus Following Aneurysmal Subarachnoid Hemorrhage

Kuo

Huang

2021

IJMS

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Hydrocephalus is a common complication of aneurysmal subarachnoid hemorrhage (aSAH) and reportedly contributes to poor neurological outcomes. In this review, we summarize the molecular and cellular mechanisms involved in the pathogenesis of hydrocephalus following aSAH and summarize its treatment strategies. Various mechanisms have been implicated for the development of chronic hydrocephalus following aSAH, including alterations in cerebral spinal fluid (CSF) dynamics, obstruction of the arachnoid granulations by blood products, and adhesions within the ventricular system. Regarding molecular mechanisms that cause chronic hydrocephalus following aSAH, we carried out an extensive review of animal studies and clinical trials about the transforming growth factor-β/SMAD signaling pathway, upregulation of tenascin-C, inflammation-dependent hypersecretion of CSF, systemic inflammatory response syndrome, and immune dysregulation. To identify the ideal treatment strategy, we discuss the predictive factors of shunt-dependent hydrocephalus between surgical clipping and endovascular coiling groups. The efficacy and safety of other surgical interventions including the endoscopic removal of an intraventricular hemorrhage, placement of an external ventricular drain, the use of intraventricular or cisternal fibrinolysis, and an endoscopic third ventriculostomy on shunt dependency following aSAH were also assessed. However, the optimal treatment is still controversial, and it necessitates further investigations. A better understanding of the pathogenesis of acute and chronic hydrocephalus following aSAH would facilitate the development of treatments and improve the outcome.

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“…Among these variables, some have been proposed, like the patient's age and gender [22,23,33,35,36,47,48], the neurological status at presentation (Hunt & Hess and WFNS scales) [12, 20-22, 25, 27, 47, 48], the amount of cisternal blood on the first CT scan (Fisher and BNI scales) [12, 16, 21-23, 25, 35, 47], the presence of acute hydrocephalus on the first CT scan and the need for external ventricular drain (EVD) [1, 12, 21-23, 25, 27, 33, 36, 43, 47, 48], the duration of EVD treatment [25,48], aneurysms location and size [9,22,25,33,36,47], the type of treatment for aneurysm exclusion (endovascular or surgical) [9,25,35,36,50], the onset and duration of posttreatment complications (i.e., fever and/or infections) [25,27,36,47,50], the duration of blood clearance detected on serial CT scans [29], and altered values of blood or cerebrospinal fluid (CSF) markers [27,31,46]. Some meta-analyses [11,45,47,50] have synthesized this information, and scores for risk stratification have been proposed to be used in the clinical practice [5,12,13,21,27], whose validity, however, has not yet been confir...…”

Section: Introductionmentioning

confidence: 99%

Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage

et al. 2020

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Background Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing different ML models to predict shunt-dependent hydrocephalus after aneurysmal SAH. Methods We consulted electronic records of patients with aneurysmal SAH treated at our institution between January 2013 and March 2019. We selected variables for the models according to the results of the previous works on this topic. We trained and tested four ML algorithms on three datasets: one containing binary variables, one considering variables associated with shunt-dependency after an explorative analysis, and one including all variables. For each model, we calculated AUROC, specificity, sensitivity, accuracy, PPV, and also, on the validation set, the NPV and the Matthews correlation coefficient (ϕ). Results Three hundred eighty-six patients were included. Fifty patients (12.9%) developed shunt-dependency after a mean follow-up of 19.7 (± 12.6) months. Complete information was retrieved for 32 variables, used to train the models. The best models were selected based on the performances on the validation set and were achieved with a distributed random forest model considering 21 variables, with a ϕ = 0.59, AUC = 0.88; sensitivity and specificity of 0.73 (C.I.: 0.39–0.94) and 0.92 (C.I.: 0.84–0.97), respectively; PPV = 0.59 (0.38–0.77); and NPV = 0.96 (0.90–0.98). Accuracy was 0.90 (0.82–0.95). Conclusions Machine learning prognostic models allow accurate predictions with a large number of variables and a more subject-oriented prognosis. We identified a single best distributed random forest model, with an excellent prognostic capacity (ϕ = 0.58), which could be especially helpful in identifying low-risk patients for shunt-dependency.

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Shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage

Cited by 28 publications

References 39 publications

WITHDRAWN: Shunt-Dependent Hydrocephalus After Aneurysm Subarachnoid Hemorrhage: Predictors and its Relationship with Lumbar Puncture

WITHDRAWN: Shunt-Dependent Hydrocephalus After Aneurysm Subarachnoid Hemorrhage: Predictors and its Relationship with Lumbar Puncture

The Pathogenesis of Hydrocephalus Following Aneurysmal Subarachnoid Hemorrhage

Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage

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