Status epilepticus severity score as a predictor for the length of stay at hospital for acute-phase treatment in convulsive status epilepticus

Determinants of Outcomes in Convulsive Status Epilepticus: An Observational Study at a Tertiary Care Center in North India

Objective Status epilepticus (SE) presents a critical neurological emergency associated with high morbidity and mortality rates worldwide. However, the determinants influencing outcomes in SE within specific regional contexts remain less explored, especially within North India. Understanding the factors influencing the prognosis of SE in this region is crucial for tailored therapeutic approaches and improved patient outcomes.  Materials and methods This observational study was conducted at Jawaharlal Nehru Medical College, Aligarh, India, from December 1, 2020, to November 31, 2022. Patients who presented with convulsive SE lasting more than five minutes or repetitive and discrete seizures with impaired consciousness between the interictal period for at least 30 minutes were included in the study. Their clinical and biochemical variables at presentation were assessed and correlated with the outcome. Results Out of the 110 patients included in the study, males represented 59.1% (n=65), outnumbering females, who comprised 40.9% (n=45). Favourable outcome was observed in 66.36% (n=73) of patients, and unfavourable outcome was observed in 33.63% (n=37). The mean time interval between seizure onset to the patient's arrival at the hospital was 5.30 ± 4.96 hours, and the mean time interval between seizure onset to the point of seizure control was 7.10 ± 6.38 hours. On analysing the factors associated with unfavourable outcome, the type of seizure at onset (p=0.021), Glasgow Coma Scale (GCS) of <=12 at presentation (p<0.001), presence of refractory seizure (p<0.001), presence of abnormal epileptiform discharges on electroencephalography (p=0.001), Status Epilepticus Severity Score (STESS) of >2 (p<0.001), serum lactate levels (p<0.001), duration of hospital stay (p=0.004), time interval between seizure onset to hospital arrival (p<0.001) and time interval between seizure onset to the point of seizure control (p<0.001) showed significant association. However, on analysing the independent risk factors of unfavourable outcome using multivariate logistic regression, only duration of hospital stay (p<0.001, odds ratio (OR): 1.205, 95% confidence interval (CI): 1.046-1.389), and GCS of less than or equal to 12 at presentation (p<0.001, OR: 12.354, 95% CI: 2.974-51.319) showed significant association. Conclusions Our study highlighted key clinical and time-related parameters influencing the outcome of convulsive SE. Understanding these factors is crucial for better treatment and improved patient outcomes. Further research is essential for refining interventions in this complex condition.

A study on predicting the length of hospital stay for Chinese patients with ischemic stroke based on the XGBoost algorithm

BACKGROUND

The incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients' LOS.

METHODS

A total of 18,195 ischemic stroke patients' electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1-7 days, 8-14 days and more than 14 days. After preprocessing the data and feature selection, the XGBoost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the XGBoost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature importance.

RESULTS

Compared with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke patients.

CONCLUSIONS

The XGBoost algorithm was an appropriate machine learning method for predicting the LOS of patients with ischemic stroke. Based on the prediction model, an intelligent medical management prediction system could be developed to predict the LOS based on ischemic stroke patients' electronic medical records.

Factors Associated With Prolonged Length of Stay in Patients Hospitalized With Generalized Convulsive Status Epilepticus in the United States

Objective

Generalized convulsive status epilepticus (GCSE) is a severe complication of epilepsy, which typically requires extended hospitalization, resulting in substantial resource utilization, hospital expenditures, and patient costs. In this nationwide analysis, we examined hospital length of stay (LOS) patterns for GCSE, and the factors that influence prolonged LOS.

Methods

We extracted data for adult patients (age 18 years and above) with a primary discharge diagnosis of GCSE from the National Inpatient Sample (NIS) from 2006-2014, the largest all-payer inpatient care database in the United States. We computed LOS (≤1, 2-6, and ≥7 days), overall, and across pre-specified patient-related, hospital-related, and healthcare system-related variables available in the NIS. We identified factors independently associated with prolonged hospitalization (2 or more days), using a multivariable logistic regression model.

Results

Of 57,832 discharged with a primary diagnosis of GCSE, 6,133 (10.7%) had a LOS ≤1 day, 27,327 (7.3%) stayed for 2-6 days, and 24,372 (42.1%) stayed for ≥7 days. After adjusting for confounders, patients who were older, female, Black, and Hispanic, who underwent continuous EEG video monitoring, were Medicare beneficiaries, had medical comorbidities, or were admitted to large/urban hospitals, were all significantly more likely to have prolonged LOS.

Conclusion

Over 40% of patients hospitalized for GCSE in the United States spend at least a week in the hospital. Efforts to shorten hospitalization for GCSE may need to primarily focus on patient groups with select sociodemographic and clinical characteristics.