| Title |
Prediction of in-hospital stroke mortality in critical care unit
|
|---|---|
| Published in |
SpringerPlus, July 2016
|
| DOI | 10.1186/s40064-016-2687-2 |
| Pubmed ID | |
| Authors |
Wei-Min Ho, Jr-Rung Lin, Hui-Hsuan Wang, Chia-Wei Liou, Ku-Chou Chang, Jiann-Der Lee, Tsung-Yi Peng, Jen-Tsung Yang, Yeu-Jhy Chang, Chien-Hung Chang, Tsong-Hai Lee |
| Abstract |
Critical stroke causes high morbidity and mortality. We examined if variables in the early stage of critical stroke could predict in-hospital mortality. We recruited 611 ischemic and 805 hemorrhagic stroke patients who were admitted within 24 h after the symptom onset. Data were analyzed with independent t test and Chi square test, and then with multivariate logistic regression analysis. In ischemic stroke, National Institutes of Health Stroke Scale (NIHSS) score (OR 1.08; 95 % CI 1.06-1.11; P < 0.01), white blood cell count (OR 1.11; 95 % CI 1.05-1.18; P < 0.01), systolic blood pressure (BP) (OR 0.49; 95 % CI 0.26-0.90; P = 0.02) and age (OR 1.03; 95 % CI 1.00-1.05; P = 0.03) were associated with in-hospital mortality. In hemorrhagic stroke, NIHSS score (OR 1.12; 95 % CI 1.09-1.14; P < 0.01), systolic BP (OR 0.25; 95 % CI 0.15-0.41; P < 0.01), heart disease (OR 1.94; 95 % CI 1.11-3.39; P = 0.02) and creatinine (OR 1.16; 95 % CI 1.01-1.34; P = 0.04) were related to in-hospital mortality. Nomograms using these significant predictors were constructed for easy and quick evaluation of in-hospital mortality. Variables in acute stroke can predict in-hospital mortality and help decision-making in clinical practice using nomogram. |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | 2% |
| Unknown | 44 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Postgraduate | 5 | 11% |
| Researcher | 5 | 11% |
| Other | 4 | 9% |
| Student > Master | 4 | 9% |
| Student > Ph. D. Student | 4 | 9% |
| Other | 6 | 13% |
| Unknown | 17 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 10 | 22% |
| Computer Science | 5 | 11% |
| Engineering | 3 | 7% |
| Neuroscience | 2 | 4% |
| Social Sciences | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 22 | 49% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 July 2016.
All research outputs
#21,623,829
of 24,135,931 outputs
Outputs from SpringerPlus
#1,481
of 1,858 outputs
Outputs of similar age
#317,931
of 360,797 outputs
Outputs of similar age from SpringerPlus
#195
of 233 outputs
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