Surgical and Neurological Complications in a Series of 708 Epilepsy Surgery Procedures

Behrens, Elga; Schramm, Johannes; Zentner, Josef; König, R.

doi:10.1097/00006123-199707000-00004

Cited by 280 publications

(159 citation statements)

References 31 publications

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“…In addition to these 24 papers using least-squares regression, another paper used Monte Carlo simulation to estimate the shape of the learning curve (33). The correlation between experience and outcome was tested by Spearman's correlation coefficient (35;42), chi-squared test for trend (36;41) or a Komolgorov-Smirnoff test (5). Three studies attempted to model the relationship between experience and outcome using repeated measures ANOVA (7;10;25).…”

Section: Statistical Methods Usedmentioning

confidence: 99%

ASSESSMENT OF THE LEARNING CURVE IN HEALTH TECHNOLOGIES A Systematic Review

Ramsay

Grant

Wallace

et al. 2000

J. of Inter. Tech. of Health Care

188

135

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Objective: We reviewed and appraised the methods by which the issue of the learning curve has been addressed during health technology assessment in the past. Method: We performed a systematic review of papers in clinical databases (BIOSIS, CINAHL, Cochrane Library, EMBASE, HealthSTAR, MEDLINE, Science Citation Index, and Social Science Citation Index) using the search term "learning curve." Results: The clinical search retrieved 4,571 abstracts for assessment, of which 559 (12%) published articles were eligible for review. Of these, 272 were judged to have formally assessed a learning curve. The procedures assessed were minimal access (51%), other surgical (41%), and diagnostic (8%). The majority of the studies were case series (95%). Some 47% of studies addressed only individual operator performance and 52% addressed institutional performance. The data were collected prospectively in 40%, retrospectively in 26%, and the method was unclear for 31%. The statistical methods used were simple graphs (44%), splitting the data chronologically and performing a t test or chi-squared test (60%), curve fitting (12%), and other model fitting (5%). Conclusions: Learning curves are rarely considered formally in health technology assessment. Where they are, the reporting of the studies and the statistical methods used are weak. As a minimum, reporting of learning should include the number and experience of the operators and a detailed description of data collection. Improved statistical methods would enhance the assessment of health technologies that require learning.

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Section: Statistical Methods Usedmentioning

confidence: 99%

ASSESSMENT OF THE LEARNING CURVE IN HEALTH TECHNOLOGIES A Systematic Review

Ramsay

Grant

Wallace

et al. 2000

J. of Inter. Tech. of Health Care

188

135

View full text Add to dashboard Cite

show abstract

“…The issue regarding what is a complication and how it should be classified has also been an object of debate for many years in neurosurgery. 2,[5][6][7]22,23,26,38 Nevertheless, patients do not care how complications and outcomes in neurosurgery are defined. Their main question would be: "Given that I have to undergo surgery, where do I have to go to have a greater possibility of getting rid of my tumor without negative outcomes?"…”

mentioning

confidence: 99%

Predicting functional impairment in brain tumor surgery: the Big Five and the Milan Complexity Scale

Ferroli¹,

Broggi²,

Schiavolin³

et al. 2015

FOC

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OBJECT The Milan Complexity Scale—a new practical grading scale designed to estimate the risk of neurological clinical worsening after performing surgery for tumor removal—is presented. METHODS A retrospective study was conducted on all elective consecutive surgical procedures for tumor resection between January 2012 and December 2014 at the Second Division of Neurosurgery at Fondazione IRCCS Istituto Neurologico Carlo Besta of Milan. A prospective database dedicated to reporting complications and all clinical and radiological data was retrospectively reviewed. The Karnofsky Performance Scale (KPS) was used to classify each patient’s health status. Complications were divided into major and minor and recorded based on etiology and required treatment. A logistic regression model was used to identify possible predictors of clinical worsening after surgery in terms of changes between the preoperative and discharge KPS scores. Statistically significant predictors were rated based on their odds ratios in order to build an ad hoc complexity scale. For each patient, a corresponding total score was calculated, and ANOVA was performed to compare the mean total scores between the improved/unchanged and worsened patients. Relative risk (RR) and chi-square statistics were employed to provide the risk of worsening after surgery for each total score. RESULTS The case series was composed of 746 patients (53.2% female; mean age 51.3 ± 17.1). The most common tumors were meningiomas (28.6%) and glioblastomas (24.1%). The mortality rate was 0.94%, the major complication rate was 9.1%, and the minor complication rate was 32.6%. Of 746 patients, 523 (70.1%) patients improved or remained unchanged, and 223 (29.9%) patients worsened. The following factors were found to be statistically significant predictors of the change in KPS scores: tumor size larger than 4 cm, cranial nerve manipulation, major brain vessel manipulation, posterior fossa location, and eloquent area involvement (Nagelkerke R2 = 0.286). A grading scale was obtained with scores ranging between 0 and 8. Worsened patients showed mean total scores that were significantly higher than the improved/unchanged scores (3.24 ± 1.55 vs 1.47 ± 1.58; p < 0.001). Finally, a grid was developed to show the risk of worsening after surgery for each total score: scores higher than 3 are suggestive of worse clinical outcome. CONCLUSIONS Through the evaluation of the 5 aforementioned parameters—the Big Five—the Milan Complexity Scale enables neurosurgeons to estimate the risk of a negative clinical course after brain tumor surgery and share these data with the patient. Furthermore, the Milan Complexity Scale could be used for research and educational purposes and better health system management.

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“…Patients with seizures originating in the frontal lobes are the second most common type with localizationrelated epilepsy reported in large surgical series, constituting about 20% of the total (1,2). As opposed to seizures of mesial temporal lobe origin, which are the most common and relatively uniform (3-3, seizures of frontal lobe origin are highly variable (5)(6)(7)(8).…”

mentioning

confidence: 99%

Intractable Seizures of Frontal Lobe Origin: Clinical Characteristics, Localizing Signs, and Results of Surgery

et al. 2000

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Summary: Purpose:We analyzed the clinical characteristics of seizures of frontal lobe (FL) origin with particular emphasis on establishing different categories and determining if these categories had any localizing or lateralizing value. In addition, results of surgery are reported.Methods: Seizure characteristics were established by historical review and electroencephalographic/videotape analysis of 449 seizures in 26 adult patients with refractory seizures of FL origin.Results: No outstanding risk factor was identified for seizures of FL origin. Seizures were frequent (7.1 per week), brief (mean duration, 48.3 seconds), and had a nocturnal preponderance in 58% of the patients. Status epilepticus was reported in 54%, and generalized convulsions as a prominent seizure type were reported in 26% of patients. The most common reported aura was a nonspecific sensation, often localized to the head (35%). Early forced head and eye deviation was not a consistent lateralizing sign, whereas late head and eye deviation always occurred contralateral to the site of seizure origin. Early asymmetric tonic posturing occurred consistently contralateral to the side of seizure origin. Clinical seizure patterns did not consistently localize to specific regions of the frontal lobe, although there were some noticeable trends: focal clonic seizures were associated with seizure origin in the frontal convexity; tonic seizures were most often associated with origin in the supplementary motor area but also occurred with origin in other parts of the frontal lobe; seizures resembling typical temporal lobe seizures with oroalimentary automatisms were observed with seizure origin in the orbitofrontal region; and seizures with hyperactive, frenetic automatisms were not associated with any specific region within the frontal lobes. Eighty percent of patients had favorable seizure outcome after surgery (class 1/11).Conclusion: Although certain clinical features are characteristic for seizures of frontal lobe origin and some have lateralizing value, they do not localize to specific areas within the FL. After careful presurgical evaluation, both lesional and nonlesional patients benefit from epilepsy surgery.

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Surgical and Neurological Complications in a Series of 708 Epilepsy Surgery Procedures

Abstract: Our data indicate that epilepsy surgery can be performed with an acceptable rate of resultant morbidity. The indications for epilepsy surgery, the learning curve determined, and the results from other series are discussed in the light of these figures.

Cited by 280 publications

References 31 publications

ASSESSMENT OF THE LEARNING CURVE IN HEALTH TECHNOLOGIES A Systematic Review

ASSESSMENT OF THE LEARNING CURVE IN HEALTH TECHNOLOGIES A Systematic Review

Predicting functional impairment in brain tumor surgery: the Big Five and the Milan Complexity Scale

Intractable Seizures of Frontal Lobe Origin: Clinical Characteristics, Localizing Signs, and Results of Surgery

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