Dataset Information

Predicting Inpatient Mortality Among Encephalitis Patients: A Novel Admission Risk Score.

ABSTRACT:

Background

Identifying underlying commonalities among all-cause encephalitis cases can be extraordinarily useful in predicting meaningful risk factors associated with inpatient mortality.

Methods

A retrospective cohort of patients with encephalitis was derived from a clinical chart review of adult patients (age ≥18 years) across 16 different hospitals in Houston, Texas, between January 2005 and July 2015. Clinical features at admission were assessed for their correlation with inpatient mortality and used to derive a final risk score prediction tool.

Results

The study included a total of 273 adult patients with all-cause encephalitis, 27 (9.9%) of whom died during hospitalization. A limited number of clinical features were substantially different between patients who survived and those who died (Charlson score, Glasgow coma scale [GCS], immunosuppression, fever on admission, multiple serologic studies, and abnormal imaging). A final multivariable logistic model was derived with the following risk factors, which were transformed into a scoring system: 1 point was assigned to the presence of a Charlson score >2, thrombocytopenia, or cerebral edema, and 2 points for a GCS value <8. Patients were then classified into different risk groups for inpatient mortality: 0 points (0%), 1 point (7%), 2 points (10.9%), 3 points (36.8%), and ≥4 points (81.8%).

Conclusions

The risk score developed from this study shows a high predictive value. This can be highly beneficial in alerting care providers to key clinical risk factors associated with in-hospital mortality in adults with encephalitis.

SUBMITTER: Hansen MA

PROVIDER: S-EPMC7651585 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Predicting Inpatient Mortality Among Encephalitis Patients: A Novel Admission Risk Score.

Hansen Michael A MA Samannodi Mohammed S MS Hasbun Rodrigo R

Open forum infectious diseases 20201007 11

<h4>Background</h4>Identifying underlying commonalities among all-cause encephalitis cases can be extraordinarily useful in predicting meaningful risk factors associated with inpatient mortality.<h4>Methods</h4>A retrospective cohort of patients with encephalitis was derived from a clinical chart review of adult patients (age ≥18 years) across 16 different hospitals in Houston, Texas, between January 2005 and July 2015. Clinical features at admission were assessed for their correlation with inpa ...[more]

PMID: 33204757

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Project description:Mortality rates among hospitalized children in many government hospitals in sub-Saharan Africa are high. Pediatric emergency services in these hospitals are often sub-optimal. Timely recognition of critically ill children on arrival is key to improving service delivery. We present a simple risk score to predict inpatient mortality among hospitalized children. Between April 2010 and June 2011, the Uganda Malaria Surveillance Project (UMSP), in collaboration with the National Malaria Control Program (NMCP), set up an enhanced sentinel site malaria surveillance program for children hospitalized at four public hospitals in different districts: Tororo, Apac, Jinja and Mubende. Clinical data collected through March 2013, representing 50249 admissions were used to develop a mortality risk score (derivation data set). One year of data collected subsequently from the same hospitals, representing 20406 admissions, were used to prospectively validate the performance of the risk score (validation data set). Using a backward selection approach, 13 out of 25 clinical parameters recognizable on initial presentation, were selected for inclusion in a final logistic regression prediction model. The presence of individual parameters was awarded a score of either 1 or 2 based on regression coefficients. For each individual patient, a composite risk score was generated. The risk score was further categorized into three categories; low, medium, and high. Patient characteristics were comparable in both data sets. Measures of performance for the risk score included the receiver operating characteristics curves and the area under the curve (AUC), both demonstrating good and comparable ability to predict deathusing both the derivation (AUC =0.76) and validation dataset (AUC =0.74). Using the derivation and validation datasets, the mortality rates in each risk category were as follows: low risk (0.8% vs. 0.7%), moderate risk (3.5% vs. 3.2%), and high risk (16.5% vs. 12.6%), respectively. Our analysis resulted in development of a risk score that ably predicted mortality risk among hospitalized children. While validation studies are needed, this approach could be used to improve existing triage systems.

Dataset Information

Predicting Inpatient Mortality Among Encephalitis Patients: A Novel Admission Risk Score.

Background

Methods

Results

Conclusions

Publications

Predicting Inpatient Mortality Among Encephalitis Patients: A Novel Admission Risk Score.

Similar Datasets

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