| Literature DB >> 26587506 |
Nadiah Wan-Arfah1, W Ahmad Wan Muhamad Amir2, Mustapha Muzaimi3, Mamat Mustafa4, Nyi Nyi Naing1.
Abstract
Entities:
Year: 2015 PMID: 26587506 PMCID: PMC4645789
Source DB: PubMed Journal: Iran J Public Health ISSN: 2251-6085 Impact factor: 1.429
Prognostic factors of mortality among first-ever stroke patients admitted in HUSM using multiple Cox Proportional Hazards Regression (n=430) (Final model)
| Gender | ||||
| 0 | 1 | |||
| −1.245 | 0.288 (0.140, 0.595) | −3.36 | 0.001 | |
| Fasting blood sugar (FBS) | 0.089 | 1.093 (1.045, 1.143) | 3.92 | <0.001 |
| Marital status | ||||
| 0 | 1 | |||
| −2.205 | 0.110 (0.045, 0.270) | −4.82 | <0.001 | |
| −2.152 | 0.116 (0.036, 0.374) | −3.61 | <0.001 | |
| −1.763 | 0.171 (0.021, 1.432) | −1.63 | 0.103 | |
| Diastolic blood pressure | 0.024 | 1.024 (1.010, 1.038) | 3.47 | 0.001 |
| Urea | 0.030 | 1.030 (1.001, 1.060) | 2.03 | 0.043 |
| Systolic blood pressure | −0.016 | 0.984 (0.975, 0.994) | −3.31 | 0.001 |
| Rheumatic heart disease | ||||
| 0 | 1 | |||
| 1.766 | 5.848 (1.982, 17.256) | 3.20 | 0.001 | |
| Smoking status | ||||
| 0 | 1 | |||
| 1.482 | 4.402 (1.921, 10.089) | 3.50 | <0.001 | |
| 1.442 | 4.230 (2.051, 8.726) | 3.90 | <0.001 | |
| Seizure/fit | ||||
| 0 | 1 | |||
| −0.983 | 0.374 (0.189, 0.740) | −2.83 | 0.005 | |
| Glasgow coma scale (GCS) | −0.311 | 0.733 (0.687, 0.782) | −9.35 | <0.001 |
| Usage of aspirin | ||||
| 0 | 1 | |||
| −0.572 | 0.564 (0.342, 0.932) | −2.24 | 0.025 | |
| Age at the time of diagnosis (years) | 0.041 | 1.042 (1.023, 1.062) | 4.33 | <0.001 |
HR hazard ratio, CI confidence interval
Backward stepwise cox proportional hazards regression model applied.
Two-way interaction and multicollinearity were unlikely.
The preliminary final model was properly specified.
Hazard function plot, Log-minus-log plot, Schoenfeld partial residuals plot, scaled and non-scaled Schoenfeld residuals test and C-statistics were applied to check model assumption.
Regression diagnostics were performed by Cox-Snell residual, Martingale residual, Deviance residual and influential analysis.
Influential outliers were identified by calculating the percent changes of regression coefficient. If the changes were less than 20%, the outlier was not influential.
Two influential outliers were detected and were decided to be removed from the model.