Abstract P248: CSC Implementation of Artificial Intelligence Software Significantly Improves Door-In to Groin Puncture Time Interval and Recanalization Rates

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Ameer E Hassan ◽  
Victor M Ringheanu ◽  
Laurie Preston ◽  
WONDWOSSEN TEKLE
Keyword(s):  
Artificial Intelligence ◽  
Treatment Time ◽  
Care Center ◽  
Mass Effect ◽  
Comprehensive Care ◽  
Time Interval ◽  
Vessel Occlusion ◽  
Ct Angiogram ◽  
Detection Technology ◽  
The Mean

Introduction: Viz.ai artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams. We performed this analysis to determine if utilization of this AI software can reduce the door-in to groin puncture time interval within the comprehensive care center (CSC) for patients arriving at the CSC for endovascular treatment. Methods: We compared the time interval between door-in to groin puncture for all LVO transfer patients who arrived at our comprehensive care center for approximately two years prior to and after the implementation of the AI software in November of 2018. Using a prospectively collected database at a CSC, demographics, door-in to groin time, modified Rankin Scale at discharge (mRS dc), mortality rate at discharge, length of stay (LOS) in hospital, mass effect, and hemorrhage rates were examined. Results: There were a total of 188 patients during the study period (average age 69.26 ± 14.55, 42.0% women). We analyzed 86 patients from the pre-AI (average age 68.53 ± 13.13, 40.7% women) and 102 patients from the post-AI (average age 69.87 ± 15.75, 43.1% women); see Table 1 for comparison of baseline characteristics and outcomes. Following the implementation of the AI software, the mean door-in to groin puncture time interval within the CSC significantly improved by 86.7 minutes (206.6 vs 119.9 minutes; p < 0.0001); significant improvements were also noted in the rate of good recanalization (mTICI 2B-3) for patients in the post-AI population (p=0.0364). Conclusion: The incorporation of the AI software was associated with a significant improvement in treatment time within the CSC as well as significantly higher rates of good recanalization for patients treated. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.

Abstract P266: The Implementation of Artificial Intelligence Significantly Reduces Door-In Door-Out Times in Primary Care Center Prior to Transfer

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Ameer E Hassan ◽  
Victor M Ringheanu ◽  
Laurie Preston ◽  
WONDWOSSEN TEKLE
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Care Center ◽  
Time Interval ◽  
Primary Care Center ◽  
Vessel Occlusion ◽  
Discharge Rates ◽  
Ct Angiogram ◽  
Detection Technology ◽  
The Mean

Introduction: Viz.ai artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams. We performed this analysis to determine if utilization of AI software can reduce the door-in door-out (DIDO) time interval within the primary care center (PSC) prior to transfer to the comprehensive care center (CSC). Methods: We compared the time interval between door-in and door-out for all LVO transfer patients from a single spoke PSC to our CSC prior to (Feb. 2017 to Nov. 2018) and after (Nov. 2018 to June 2020) incorporating Viz.ai. Using a prospectively collected stroke database at a CSC, demographics, DIDO time at PSC, modified Rankin Scale at discharge (mRS dc), mortality rate at discharge, length of stay (LOS) in hospital and neurological ICU, and intracranial hemorrhage rates were examined. Results: There were a total of 63 patients during the study period (average age 69.99 ± 13.72, 55.56% women). We analyzed 28 patients from the pre-AI (average age 71.64 ± 12.28, 46.4%), and 35 patients from the post-AI (average age 68.67 ± 14.88, 62.9% women); see Table 1 for comparison of baseline characteristics and outcomes. Following the implementation of the AI software, the mean DIDO time interval within the PSC significantly improved by 102.3 minutes (226.7 versus 124.4 minutes; p=0.0374); significant reductions were not noted in mRS at discharge, rates of hemorrhage, or mortality. Conclusion: The incorporation of the AI software was associated with a significant improvement in DIDO times within the PSC and may lead to significant improvements in functional outcome and mortality in transfer patients. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.

scholarly journals Early experience utilizing artificial intelligence shows significant reduction in transfer times and length of stay in a hub and spoke model

2020 ◽  
Vol 26 (5) ◽  
pp. 615-622 ◽  
Author(s):  
Ameer E Hassan ◽  
Victor M Ringheanu ◽  
Rani R Rabah ◽  
Laurie Preston ◽  
Wondwossen G Tekle ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Length Of Stay ◽  
Time Interval ◽  
Transfer Time ◽  
Vessel Occlusion ◽  
Hub And Spoke ◽  
Ct Angiogram ◽  
Detection Technology ◽  
Stroke Center ◽  
Transfer Times

Background Recently approved artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams. We performed this analysis to determine if utilization of AI software and workflow platform can reduce the transfer time (time interval between CTA at a primary stroke center (PSC) to door-in at a comprehensive stroke center (CSC)). Methods We compared the transfer time for all LVO transfer patients from a single spoke PSC to our CSC prior to and after incorporating AI Software (Viz.ai LVO). Using a prospectively collected stroke database at a CSC, demographics, mRS at discharge, mortality rate at discharge, length of stay (LOS) in hospital and neurological-ICU were examined. Results There were a total of 43 patients during the study period (median age 72.0 ± 12.54 yrs., 51.16% women). Analysis of 28 patients from the pre-AI software (median age 73.5 ± 12.28 yrs., 46.4% women), and 15 patients from the post-AI software (median age 70.0 ± 13.29 yrs., 60.00% women). Following implementation of AI software, median CTA time at PSC to door-in at CSC was significantly reduced by an average of 22.5 min. (132.5 min versus 110 min; p = 0.0470). Conclusions The incorporation of AI software was associated with an improvement in transfer times for LVO patients as well as a reduction in the overall hospital LOS and LOS in the neurological-ICU. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.

Abstract TMP62: Early Experience Utilizing Artificial Intelligence Shows Significant Reduction in Transfer Times and Length of Stay in a Hub and Spoke Model

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Ameer E Hassan ◽  
Victor M Ringheanu ◽  
Rani Rabah ◽  
Laurie Preston ◽  
Adnan I Qureshi
Keyword(s):  
Artificial Intelligence ◽  
Length Of Stay ◽  
Time Interval ◽  
Time Of Arrival ◽  
Vessel Occlusion ◽  
Hub And Spoke ◽  
Ct Angiogram ◽  
Detection Technology ◽  
Stroke Center ◽  
Transfer Times

Background: Viz.ai artificial intelligence (AI) software utilizes AI powered large vessel occlusion (LVO) detection technology which automatically identifies suspected LVO through CT angiogram (CTA) imaging and alerts on-call stroke teams. We performed this analysis to determine if utilization of AI software can reduce the time interval between CTA at a primary stroke center (PSC) and arrival time at a comprehensive stroke center (CSC). Methods: We compared time interval between CTA and time of arrival for all LVO transfer patients from a single spoke PSC to our CSC prior to (Feb. 2017 to Nov. 2018) and after (Nov. 2018 to May 2019) incorporating Viz.ai. Using a prospectively collected stroke database at a CSC, demographics, transfer time (CTA time to time of arrival at CSC), modified Rankin Scale at discharge (mRS dc), mortality rate at discharge, length of stay (LOS) in hospital and neurological ICU, and intracranial hemorrhage rates were examined. Results: There were a total of 43 patients during the study period (average age 70.77 ± 12.54 yrs., 51.16% women). Analysis of 28 patients from the pre-Viz.ai (average age 71.64 ± 12.28 yrs., 46.4% women), and 15 patients from the post-Viz.ai (average age 69.13 ± 13.29 yrs., 60.0% women); see Table 1 for comparison of baseline characteristics and outcomes. Following implementation of Viz.ai, CTA time at PSC to time of arrival at CSC was significantly reduced by an average of 66 min. (mean CTA to time of arrival, 171 min. vs 105 min; P= 0.0163); significant reductions were also noted in the overall LOS (9.7 days vs 7.2 days; P= 0.0324) and LOS in the neurological ICU (6.4 days vs 2.9 days; P= 0.0039). Conclusions: The incorporation of Viz.ai was associated with a significant improvement in transfer times for LVO patients as well as a significant reduction in the overall hospital LOS and LOS in the neurological ICU. More extensive studies are warranted to expand on the ability of AI technology to improve transfer times and outcomes for LVO patients.

Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients

2021 ◽  
pp. 1-6
Author(s):  
Jacob R. Morey ◽  
Xiangnan Zhang ◽  
Kurt A. Yaeger ◽  
Emily Fiano ◽  
Naoum Fares Marayati ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Outcomes ◽  
Warning System ◽  
Large Vessel ◽  
Time Interval ◽  
Large Vessel Occlusion ◽  
Time Intervals ◽  
Vessel Occlusion ◽  
Left Behind ◽  
Stroke Center

<b><i>Background and Purpose:</i></b> Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent when patients require a transfer from hospitals without EVT capability onsite. A computer-aided triage system, Viz LVO, has the potential to streamline workflows. This platform includes an image viewer, a communication system, and an artificial intelligence (AI) algorithm that automatically identifies suspected LVO strokes on CTA imaging and rapidly triggers alerts. We hypothesize that the Viz application will decrease time-to-treatment, leading to improved clinical outcomes. <b><i>Methods:</i></b> A retrospective analysis of a prospectively maintained database was assessed for patients who presented to a stroke center currently utilizing Viz LVO and underwent EVT following transfer for LVO stroke between July 2018 and March 2020. Time intervals and clinical outcomes were compared for 55 patients divided into pre- and post-Viz cohorts. <b><i>Results:</i></b> The median initial door-to-neuroendovascular team (NT) notification time interval was significantly faster (25.0 min [IQR = 12.0] vs. 40.0 min [IQR = 61.0]; <i>p</i> = 0.01) with less variation (<i>p</i> &#x3c; 0.05) following Viz LVO implementation. The median initial door-to-skin puncture time interval was 25 min shorter in the post-Viz cohort, although this was not statistically significant (<i>p</i> = 0.15). <b><i>Conclusions:</i></b> Preliminary results have shown that Viz LVO implementation is associated with earlier, more consistent NT notification times. This application can serve as an early warning system and a failsafe to ensure that no LVO is left behind.

scholarly journals Clinical Review of Ocular Traumas Resulting in Enucleation or Evisceration in a Tertiary Eye Care Center in Hungary

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Gábor Tóth ◽  
Milán Tamás Pluzsik ◽  
Béla Csákány ◽  
Gábor László Sándor ◽  
Olga Lukáts ◽  
...  
Keyword(s):  
Ocular Trauma ◽  
Care Center ◽  
Clinical Indication ◽  
Time Interval ◽  
Eye Care ◽  
Primary Indication ◽  
Phthisis Bulbi ◽  
Open Globe ◽  
Department Of Ophthalmology ◽  
The Mean

Purpose. To analyse the demographic and clinical characteristics of ocular traumas resulting in enucleation/evisceration in a large tertiary referral center in a developed country (Hungary) over a period of 15 years. Patients and Methods. A retrospective review of enucleated/eviscerated eyes that underwent surgery between 2006 and 2020 at the Department of Ophthalmology of Semmelweis University, Budapest, Hungary, due to ocular trauma as the primary indication for enucleation/evisceration. For each subject, clinical history, B-scan ultrasound report, and histopathology results were reviewed. Results. There were 124 enucleated/eviscerated eyes from 124 patients (91 males (73.4%)). The mean age at the time of trauma was 37.3 ± 26.0 years while the mean age at the time of enucleation/evisceration was 46.9 ± 20.3 years. The main clinical diagnoses after ocular trauma were open globe injury (n = 96; 77.4%), ocular burns (n = 6; 4.8%), traumatic optic neuropathy (n = 4; 3.2%), bulbar avulsion (n = 3; 2.4%), traumatic cataract (n = 2; 1.6%), retinal ablation (n = 1; 0.8%), and traumatic carotid-cavernous fistula (n = 1; 0.8%). Among the 124 patients, 98 (79.0%) underwent enucleation and 26 (21.0%) evisceration. Patients who underwent primary enucleation/evisceration (n = 24 19.4%) were significantly older at the time of the injury (57.7 ± 22.7 years) than people who underwent secondary eye removal (32.4 ± 24.4 years) ( p < 0.0001 ). The mean time interval between trauma and enucleation/evisceration was 114.9 ± 163.5 months. The main clinical indications for anophthalmic surgery were atrophia/phthisis bulbi (n = 56, 45.2%), acute trauma (n = 25, 20.2%), painful blind eye due to glaucoma (n = 17, 13.7%), endophthalmitis (n = 10, 8.1%), and cosmetic reasons (n = 7, 5.6%). One patient (0.8%) had sympathetic ophthalmia. Conclusions. Primary enucleation/evisceration was performed in one-fifth of all ocular trauma-related anophthalmic surgeries in our tertiary eye care center with enucleation being the most common procedure. Atrophia/phthisis bulbi was the most frequent immediate clinical indication for enucleation/evisceration.

Abstract WP61: Artificial Intelligence for Automated Detection of Large Vessel Occlusion Strokes

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Catarina Perry da Câmara ◽  
Gabriel M Rodrigues ◽  
Clara Barreira ◽  
Mehdi Bouslama ◽  
Leonardo Pisani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Detrimental Effect ◽  
Poor Quality ◽  
Automated Detection ◽  
Large Vessel ◽  
Large Vessel Occlusion ◽  
Accurate Identification ◽  
Vessel Occlusion ◽  
The Mean ◽  
Stroke Center

Introduction: Identification of Large Vessel Occlusion (LVO) in acute ischemic stroke (AIS) patients is critical for proper decision-making. Limited availability of trained experts and delays in LVO recognition can have a detrimental effect on outcomes. We sought to evaluate an artificial intelligence-based algorithm for LVO detection in AIS. Methods: A retrospective analysis of a prospectively-collected database of AIS patients admitted to a large volume stroke center between 2014-2018 was performed. Experienced vascular neurologists graded CTA for presence and site of LVO. Concurrently, studies were analyzed by the Viz-LVO Algorithm® version 1.4 (GA) - a convolutional neural network programmed to detect occlusions from the internal carotid artery terminus (ICA-T) to the sylvian fissure, which would include all MCA M1-segment and most M2-segment lesions. CTA readings were categorized as LVOs (ICA-T, MCA-M1, MCA-M2) versus non-LVOs/more distal occlusions. Comparisons between human and AI-based readings were done by accuracy analysis and calculating Cohen’s kappa. Results: A total of 610 CTAs were analyzed. The AI algorithm rejected 3.4% of the CTAs due to poor quality. Viz-LVO identified LVOs with an overall sensitivity of 81.3%, specificity of 87.8%, and accuracy of 83.2% (AUC 0.845 (95%CI:0.81-0.88, p<0.001). Table 1 shows the results per occlusion site. Accuracy was higher for ICA-T and M1 occlusions as compared to M2 occlusions. The mean run time of the algorithm was 2.78±0.5minutes. Conclusion: Our study demonstrates that automated AI reading allows for fast and accurate identification of LVO strokes. Future efforts should be made to improve the detection of the more distal occlusions.

Abstract TP39: High Accuracy of Magnetic Resonance T2*- Weighted Sequences for Detection of Intracranial Arterial Thrombus

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Afonso P Liberato ◽  
Santosh Shah ◽  
Noor Maza ◽  
Isabelle Barnaure ◽  
Ramon G Gonzalez ◽  
...  
Keyword(s):  
Ct Angiography ◽  
Imaging Modality ◽  
Arterial Occlusion ◽  
High Accuracy ◽  
Time Interval ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Arterial Thrombus ◽  
The Mean ◽  
Mean Time

Introduction: Rapid detection and location of vessel occlusion are pivotal in the intra-arterial management of patients with acute stroke in the emergency room. MRI has demonstrated to detect intravascular thrombus but its accuracy compared to CT angiography has not been well established. Hypothesis: Our purpose is to determine the accuracy of 1.5 T MRI T2*-weighted (W) sequences compared to immediate CT angiography as the standard reference imaging modality, for detection of intra-arterial thrombus in patients with suspected acute MCA infarction. Methods: Consecutive patients with suspected middle cerebral artery (MCA) territory stroke were selected from 2008 to 2009. The inclusion criteria for the study subjects: CTA, T2*W sequences included on MRI protocol and restricted diffusion in MCA territory on DWI within 12 hrs of clinical onset. Two investigators reviewed DWI and T2*W sequences for the presence of infarction and thrombus. Intracranial internal carotid artery (ICA), M1 and M2 segments of the MCA were accessed. Consensus was reached with a third reviewer for data analyses. Accuracy, sensitivity, specificity, positive and negative predictive values (PPV/NPV) were calculated. Results: Fifty-one patients were included in the study, of which 40 patients had confirmed arterial thrombus and 11 patients had normal studies on CTA. Of the subjects with arterial occlusion on CTA, the mean time interval from stroke onset to CTA was 4.2 h +/- 2.3 h (range, 0.4-12h). The mean time interval from CTA to MRI was 29.5 min +/- 11.1 min. Twenty-six cases showed M1 thrombus on CTA, of these, 22 cases had corresponding thrombus and 4 cases had no abnormality in T2*W sequences on MRI. Nevertheless, 25 patients demonstrated no M1 thrombus, either on CTA or MRI. After statistical analyses, we observed an accuracy of 92%, sensitivity of 85%, specificity of 100%, PPV 100% and NPV of 86% for M1 occlusion. The Kappa obtained was 0.79. Conclusion: In conclusion, T2*W sequences demonstrated overall high accuracy and specificity for detection of arterial thrombus in the M1 segment of the MCA in patients with suspected acute MCA ischemic stroke.

Reduction in STEMI Transfer Times Utilizing a Municipal “911” Ambulance Service

2014 ◽  
Vol 29 (1) ◽  
pp. 50-53 ◽  
Author(s):  
Joseph C. Tennyson ◽  
Mark R. Quale
Keyword(s):  
Local Community ◽  
Community Hospital ◽  
Life Support ◽  
Care Center ◽  
Tertiary Care ◽  
Ambulance Service ◽  
Time Interval ◽  
The Mean ◽  
Service Data ◽  
Transfer Times

AbstractIntroductionThe time interval from diagnosis to reperfusion therapy for patients experiencing ST-segment elevation myocardial infarction (STEMI) has a significant impact on morbidity and mortality.HypothesisIt is hypothesized that the time required for interfacility patient transfers from a community hospital to a regional percutaneous coronary intervention (PCI) center using an Advanced Life Support (ALS) transfer ambulance service is no different than utilizing the “911” ALS ambulance.MethodsQuality assurance data collected by a tertiary care center cardiac catheterization program were reviewed retrospectively. Data were collected on all patients with STEMI requiring interfacility transfer from a local community hospital to the tertiary care center's PCI suite, approximately 16 miles away by ground, 12 miles by air. In 2009, transfers of patients with STEMI were redirected to the municipal ALS ambulance service, instead of the hospital's contracted ALS transfer service. Data were collected from January 2007 through May 2013. Temporal data were compared between transports initiated through the contracted ALS ambulance service and the municipal ALS service. Data points included time of initial transport request and time of ambulance arrival to the sending facility and the receiving PCI suite.ResultsDuring the 4-year study period, 63 patients diagnosed with STEMI and transferred to the receiving hospital's PCI suite were included in this study. Mean times from the transport request to arrival of the ambulance at the sending hospital's emergency department were six minutes (95% CI, 4-7 minutes) via municipal ALS and 13 minutes (95% CI, 9-16 minutes) for the ALS transfer service. The mean times from the ground transport request to arrival at the receiving hospital's PCI suite when utilizing the municipal ALS ambulance and hospital contracted ALS ambulance services were 48 minutes (95% CI, 33-64 minutes) and 56 minutes (95% CI 52-59 minutes), respectively. This eight-minute period represented a 14% (P = .001) reduction in the mean transfer time to the PCI suite for patients transported via the municipal ALS ambulance.ConclusionIn the appropriate setting, the use of the municipal “911” ALS ambulance service for the interfacility transport of patients with STEMI appears advantageous in reducing door-to-catheterization times.TennysonJC, QualeMR. Reduction in STEMI transfer times utilizing a municipal “911” ambulance service. Prehosp Disaster Med. 2014;29(1):1-4.

scholarly journals Endovascular treatment of acute ischemic stroke in patients with recurrent intracranial large vessel occlusion

2020 ◽  
Vol 26 (4) ◽  
pp. 376-382 ◽  
Author(s):  
Andrej Klepanec ◽  
Jan Harsany ◽  
Jozef Haring ◽  
Miroslav Mako ◽  
Matus Hoferica ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Mechanical Thrombectomy ◽  
Large Vessel ◽  
Time Interval ◽  
Large Vessel Occlusion ◽  
Effective Treatment Option ◽  
Vessel Occlusion ◽  
The Mean ◽  
Second Procedure

Background Data on the treatment with recurrent mechanical thrombectomy of patients with acute ischemic stroke with recurrent large vessel occlusion are limited. We report our experience with recurrent mechanical thrombectomy for recurrent large vessel occlusion. Methods During the period between May 2013 and August 2018, data on patients with recurrent large vessel occlusion were collected. Baseline clinical characteristics, recanalization technique, recanalization rates and clinical outcomes of patients with recurrent large vessel occlusion treated with mechanical thrombectomy were analyzed. Patients with recurrent large vessel occlusion treated with mechanical thrombectomy were compared with patients who underwent single mechanical thrombectomy. Results During the study period, 7 of 474 patients (1.5%) were treated with mechanical thrombectomy for recurrent large vessel occlusion. The mean age of these patients was 64.4 (±7.9) years, and the mean time interval between thrombectomies was 47 (±48) h. The median baseline National Institutes of Health Stroke Scale (NIHSS) was 12 (range 5–24) before the first and 20 (range 3–34) before the second procedure; the mean NIHSS at discharge was 5 (range 2–25). Good clinical outcome after repeated mechanical thrombectomy defined as modified Rankin scale of 0–2 was achieved in 29% of patients at three months of follow-up. Conclusions Repeat mechanical thrombectomy is a rare procedure, but appears to be a feasible, safe and effective treatment option in patients with acute ischemic stroke and early recurrent large vessel occlusion.

scholarly journals De novo Aneurysms

2018 ◽  
Vol 23 (4) ◽  
pp. 288-294
Author(s):  
Gabriel A. Gonzales-Portillo
Keyword(s):  
De Novo ◽  
English Literature ◽  
Time Interval ◽  
Medline Search ◽  
Multiple Aneurysms ◽  
Non Invasive ◽  
Ct Angiogram ◽  
De Novo Aneurysm ◽  
The Mean

Introduction: Patients who have undergone craniotomy and clipping of an aneurysm are believed to be cured. We report two cases with previous ruptured aneurysms who presented 10 and 14 years later with de novo aneurysms. Methods: The charts of 39 patients who underwent craniotomy and clipping of 52 intracranial aneurysms by a single surgeon at a single institution from July 1999 to June 2003 were reviewed. Medline search for published English literature on de novo aneurysms was conducted from 1964 to 2003. Results: Two female patients out of 39 (5%) were found to have de novo aneurysms in our series. Mean age was 47. One patient presented again with SAH. Both were found to have multiple aneurysms less than 6 mm in size. There are 77 cases of de novo aneurysms in the English literature. Sixty-two (81%) patients presented with ruptured de novo aneurysm. The mean age of initial aneurysm presentation was 42.2 years, with average time interval between this and diagnosis of de novo aneurysm formation was 9.2 years. The female to male ratio is 2:1. Multiple aneurysms were found in 17 (22%) and 16 (21%) patients in the initial presentation and the de novo presentation respectively. Conclusions: Patients who undergo craniotomy and aneurysm clipping at an early age may benefit from follow up imaging studies for up to 20 years. Helicoidal CT angiogram with 3D reconstruction will allow for non-invasive follow up. 

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