Aim: Super-refractory status epilepticus (SRSE) is a status epilepticus (SE) that continues or recurs >= 24 h after the onset of anesthesia. We aimed to identify the predictors of progression to SRSE and the risk of 30-day mortality in patients with SRSE by using a machine learning technique.Methods: We reviewed consecutive SE episodes in patients aged >= 14 years at Baggiovara Civil Hospital (Modena, Italy) from 2013 to 2021. A classification and regression tree analysis was performed to develop a predictive model of progression to SRSE in SE patients. In SRSE patients, a multivariate analysis was conducted to identify predictors of 30-day mortality.Results: We included 705 patients, 16% of whom (113/705) progressed to SRSE. Acute symptomatic hypoxic etiology and age <= 68.5 years predicted the highest risk (87.1%) of progression to SRSE. Etiology other than acute symptomatic hypoxic and absence of NCSE predicted the lowest risk (3.6%) of progression to SRSE. The predictive model was accurate in 96.1% of patients not evolving to SRSE and in 48.7% of those evolving to SRSE. Among patients with SRSE, 46.9% (53/113) died within 30 days compared to 25.2% (149/592) of patients without SRSE (p < 0.001). Among patients with SRSE, older age was associated with increased 30-day mortality (odds ratio 1.075; 95% confidence interval: 1.031-1.112; p = 0.001).Conclusions: Acute symptomatic hypoxic etiology and younger age are major predictors of progression to SRSE. In patients with SRSE, older age is associated with increased risk of short-term mortality.

Predicting the progression to super-refractory status epilepticus: A machine-learning study / Brigo, Francesco; Turcato, Gianni; Lattanzi, Simona; Orlandi, Niccolò; Turchi, Giulia; Zaboli, Arian; Giovannini, Giada; Meletti, Stefano. - In: JOURNAL OF THE NEUROLOGICAL SCIENCES. - ISSN 0022-510X. - 443:(2022), pp. N/A-N/A. [10.1016/j.jns.2022.120481]

Predicting the progression to super-refractory status epilepticus: A machine-learning study

Meletti, Stefano
2022-01-01

Abstract

Aim: Super-refractory status epilepticus (SRSE) is a status epilepticus (SE) that continues or recurs >= 24 h after the onset of anesthesia. We aimed to identify the predictors of progression to SRSE and the risk of 30-day mortality in patients with SRSE by using a machine learning technique.Methods: We reviewed consecutive SE episodes in patients aged >= 14 years at Baggiovara Civil Hospital (Modena, Italy) from 2013 to 2021. A classification and regression tree analysis was performed to develop a predictive model of progression to SRSE in SE patients. In SRSE patients, a multivariate analysis was conducted to identify predictors of 30-day mortality.Results: We included 705 patients, 16% of whom (113/705) progressed to SRSE. Acute symptomatic hypoxic etiology and age <= 68.5 years predicted the highest risk (87.1%) of progression to SRSE. Etiology other than acute symptomatic hypoxic and absence of NCSE predicted the lowest risk (3.6%) of progression to SRSE. The predictive model was accurate in 96.1% of patients not evolving to SRSE and in 48.7% of those evolving to SRSE. Among patients with SRSE, 46.9% (53/113) died within 30 days compared to 25.2% (149/592) of patients without SRSE (p < 0.001). Among patients with SRSE, older age was associated with increased 30-day mortality (odds ratio 1.075; 95% confidence interval: 1.031-1.112; p = 0.001).Conclusions: Acute symptomatic hypoxic etiology and younger age are major predictors of progression to SRSE. In patients with SRSE, older age is associated with increased risk of short-term mortality.
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Predicting the progression to super-refractory status epilepticus: A machine-learning study / Brigo, Francesco; Turcato, Gianni; Lattanzi, Simona; Orlandi, Niccolò; Turchi, Giulia; Zaboli, Arian; Giovannini, Giada; Meletti, Stefano. - In: JOURNAL OF THE NEUROLOGICAL SCIENCES. - ISSN 0022-510X. - 443:(2022), pp. N/A-N/A. [10.1016/j.jns.2022.120481]
Brigo, Francesco; Turcato, Gianni; Lattanzi, Simona; Orlandi, Niccolò; Turchi, Giulia; Zaboli, Arian; Giovannini, Giada; Meletti, Stefano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1294467
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