Dataset Information

Risk Factors for Intensive Care Unit Admission in Patients with Autoimmune Encephalitis.

ABSTRACT:

Background

Prevention and early recognition of critical illness in patients with autoimmune encephalitis (AE) is essential to achieve better outcome.

Aim of the study

To evaluate risk factors for intensive care unit (ICU) admission and its prognostic impact in patients with AE.

Patients and methods

A reclassification of patients hospitalized between 2011 and 2016 revealed 17 "definite" and 15 "probable" AE cases. Thirteen patients (41%) developed critical illness and required ICU admission. The underlying conditions were intractable seizures or status epilepticus (54%), altered mental state (39%), and respiratory failure (8%).

Results

ICU admission was associated with longer time from first symptoms to hospitalization (p?=?0.046). Regression analysis revealed that anemia on hospital admission and definite diagnosis of AE was associated with a higher risk of acquiring critical illness. At last follow-up after a median of 31?months (range 2.5-52.4), seven patients had died (23%) and 63% had a good outcome [modified Rankin Scale (mRS) 0-3]. Anemia was associated with poor prognosis (p?=?0.021), whereas development of critical illness did not impact mortality and functional outcome.

Conclusion

We confirmed the need for ICU care in a subgroup of patients and the prevailing objective is improved seizure control, and definite diagnosis of AE and anemia were identified as risk factors for development of critical illness. However, prognosis was not affected by ICU admission.

SUBMITTER: Harutyunyan G

PROVIDER: S-EPMC5532517 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Risk Factors for Intensive Care Unit Admission in Patients with Autoimmune Encephalitis.

Harutyunyan Gayane G Hauer Larissa L Dünser Martin W MW Moser Tobias T Pikija Slaven S Leitinger Markus M Novak Helmut F HF Aichhorn Wolfgang W Trinka Eugen E Sellner Johann J

Frontiers in immunology 20170728

<h4>Background</h4>Prevention and early recognition of critical illness in patients with autoimmune encephalitis (AE) is essential to achieve better outcome.<h4>Aim of the study</h4>To evaluate risk factors for intensive care unit (ICU) admission and its prognostic impact in patients with AE.<h4>Patients and methods</h4>A reclassification of patients hospitalized between 2011 and 2016 revealed 17 "definite" and 15 "probable" AE cases. Thirteen patients (41%) developed critical illness and requir ...[more]

PMID: 28804482

Similar Datasets

Project description:BackgroundOpioid overdose (OD) and related deaths have significantly increased in the United States over the last 2 decades. Existing studies have mostly focused on demographic and clinical risk factors in noncritical care settings. Social and behavioral determinants of health (SBDH) are infrequently coded in the electronic health record (EHR) and usually buried in unstructured EHR notes, reflecting possible gaps in clinical care and observational research. Therefore, SBDH often receive less attention despite being important risk factors for OD. Natural language processing (NLP) can alleviate this problem.ObjectiveThe objectives of this study were two-fold: First, we examined the usefulness of NLP for SBDH extraction from unstructured EHR text, and second, for intensive care unit (ICU) admissions, we investigated risk factors including SBDH for nonfatal OD.MethodsWe performed a cross-sectional analysis of admission data from the EHR of patients in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. We used patient admission data and International Classification of Diseases, Ninth Revision (ICD-9) diagnoses to extract demographics, nonfatal OD, SBDH, and other clinical variables. In addition to obtaining SBDH information from the ICD codes, an NLP model was developed to extract 6 SBDH variables from EHR notes, namely, housing insecurity, unemployment, social isolation, alcohol use, smoking, and illicit drug use. We adopted a sequential forward selection process to select relevant clinical variables. Multivariable logistic regression analysis was used to evaluate the associations with nonfatal OD, and relative risks were quantified as covariate-adjusted odds ratios (aOR).ResultsThe strongest association with nonfatal OD was found to be drug use disorder (aOR 8.17, 95% CI 5.44-12.27), followed by bipolar disorder (aOR 2.69, 95% CI 1.68-4.29). Among others, major depressive disorder (aOR 2.57, 95% CI 1.12-5.88), being on a Medicaid health insurance program (aOR 2.26, 95% CI 1.43-3.58), history of illicit drug use (aOR 2.09, 95% CI 1.15-3.79), and current use of illicit drugs (aOR 2.06, 95% CI 1.20-3.55) were strongly associated with increased risk of nonfatal OD. Conversely, Blacks (aOR 0.51, 95% CI 0.28-0.94), older age groups (40-64 years: aOR 0.65, 95% CI 0.44-0.96; >64 years: aOR 0.16, 95% CI 0.08-0.34) and those with tobacco use disorder (aOR 0.53, 95% CI 0.32-0.89) or alcohol use disorder (aOR 0.64, 95% CI 0.42-1.00) had decreased risk of nonfatal OD. Moreover, 99.82% of all SBDH information was identified by the NLP model, in contrast to only 0.18% identified by the ICD codes.ConclusionsThis is the first study to analyze the risk factors for nonfatal OD in an ICU setting using NLP-extracted SBDH from EHR notes. We found several risk factors associated with nonfatal OD including SBDH. SBDH are richly described in EHR notes, supporting the importance of integrating NLP-derived SBDH into OD risk assessment. More studies in ICU settings can help health care systems better understand and respond to the opioid epidemic.

Dataset Information

Risk Factors for Intensive Care Unit Admission in Patients with Autoimmune Encephalitis.

Background

Aim of the study

Patients and methods

Results

Conclusion

Publications

Risk Factors for Intensive Care Unit Admission in Patients with Autoimmune Encephalitis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets