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. 2013 Apr;18(2):216-27.
doi: 10.1007/s12028-012-9785-y.

Calculating the risk benefit equation for aggressive treatment of non-convulsive status epilepticus

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Calculating the risk benefit equation for aggressive treatment of non-convulsive status epilepticus

Matthew Ferguson et al. Neurocrit Care. 2013 Apr.

Abstract

Objective: To address the question: does non-convulsive status epilepticus warrant the same aggressive treatment as convulsive status epilepticus?

Methods: We used a decision model to evaluate the risks and benefits of treating non-convulsive status epilepticus with intravenous anesthetics and ICU-level aggressive care. We investigated how the decision to use aggressive versus non-aggressive management for non-convulsive status epilepticus impacts expected patient outcome for four etiologies: absence epilepsy, discontinued antiepileptic drugs, intraparenchymal hemorrhage, and hypoxic ischemic encephalopathy. Each etiology was defined by distinct values for five key parameters: baseline mortality rate of the inciting etiology; efficacy of non-aggressive treatment in gaining control of seizures; the relative contribution of seizures to overall mortality; the degree of excess disability expected in the case of delayed seizure control; and the mortality risk of aggressive treatment.

Results: Non-aggressive treatment was favored for etiologies with low morbidity and mortality such as absence epilepsy and discontinued antiepileptic drugs. The risk of aggressive treatment was only warranted in etiologies where there was significant risk of seizure-induced neurologic damage. In the case of post-anoxic status epilepticus, expected outcomes were poor regardless of the treatment chosen. The favored strategy in each case was determined by strong interactions of all five model parameters.

Conclusions: Determination of the optimal management approach to non-convulsive status epilepticus is complex and is ultimately determined by the inciting etiology.

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Figures

Fig. 1
Fig. 1
A. Decision model structure. The model represents the possible outcomes for patients under both management strategies. Moving from left to right, the model considers two management options for patients with NCSE, aggressive and non-aggressive treatment. After a treatment is chosen, a patient may either survive or die with probabilities determined by baseline mortality risk and treatment strategy. Survivors may end up in 3 distinct disability classes with the distribution among classes depending again on management strategy and inciting etiology for NCSE: minimal or no disability (QOL = 1), moderate disability dysfunction (QOL = 0.75), or severe neurologic disability (QOL = 0.11). B. Modeling mortality and disability in NCSE. This figure qualitatively depicts the relationships between parameter values in the decision model. Actual parameter values assumed in the analysis for each etiology are given in Table 1. (a) The expected mortality for each inciting etiology when complicated by NCSE is divided into contributions from the inciting etiology and NCSE. Neither of the treatment options reduces mortality due to etiology. Non-aggressive treatment is assumed to reduce, but not abolish mortality due to NCSE, while aggressive treatment abolishes mortality due to NCSE while introducing mortality due to aggressive treatment. (b) Long-term neurologic disability among survivors is divided into classes of mild/no disability, moderate disability, and severe disability with the initial distribution among disability classes determined by inciting etiology of NCSE. The disability distribution among survivors of aggressive treatment is unchanged (i.e., we assume that aggressive treatment rapidly abolishes NCSE, hence there is no added morbidity due to prolonged seizure activity), while the outcomes among survivors of non-aggressive treatment are shifted toward more severe levels of disability due to deleterious effects of delayed control of NCSE. Whether aggressive versus non-aggressive treatment is optimal in any given case depends on the magnitudes of and interactions between these effects
Fig. 2
Fig. 2
One-way sensitivity analysis. From top to bottom, the etiology progresses from least to most severe: absence epilepsy (Absence), discontinued antiepileptic drugs (dEAD), intraparenchymal hemorrhage (IPH), and hypoxic ischemic encephalopathy (HIE). Across the columns, the parameter which is varied changes, from left to right: baseline mortality, efficacy of non-aggressive treatment, impact of etiology, disability due to delay and mortality of aggressive treatment. The solid line represents non-aggressive treatment and the dotted line represents aggressive treatment. The y axis shows the expected QOL. The circles represent our base-case values
Fig. 3
Fig. 3
Two-way sensitivity analysis. From top to bottom, the etiologies progress from least to most severe: absence epilepsy (Absence), discontinued antiepileptic drugs (dEAD), intraparenchymal hemorrhage (IPH), hypoxic ischemic encephalopathy (HIE). Across the columns, the parameters that vary are, from left to right: Impact of etiology versus disability due to delay, efficacy of non-aggressive treatment versus disability due to delay, efficacy of non-aggressive treatment versus mortality of aggressive treatment, disability due to delay versus mortality of aggressive treatment and impact of etiology versus mortality of aggressive treatment. The gray space represents the parameter space where non-aggressive treatment is favored and the white space represents the parameter space where aggressive treatment is favored. Note that the two-way sensitivity analysis does not quantify the expected QOL, instead shows which treatment option has a higher expected QOL, meaning the relative sizes of gray and white regions in Fig. 3 do not indicate that aggressive or non-aggressive treatment is favored by a larger margin, but that it is favored over a wider range of the two varied parameters

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