| Summary: | 碩士 === 長庚大學 === 資訊工程研究所 === 96 === There are few examples about the data collection and analysis of patients with in-hospital cardiac arrest (IHCA) in Taiwan in past years. The administrator of hospital cannot monitor the whole procedure of cardiopulmonary resuscitation and find the root cause of event. This will influence seriously on performance and outcome of in-hospital resuscitation. This study has examined the feasibility of a web-based registry system on in-hospital resuscitation using the Utstein style in an oriental country. It provides a comprehensive and standardized method for on-line registry of data collection, allowing individual hospitals to track each index case and providing datasheets for quality improvement.
We collect resuscitation patient in medical center last past three years. After data clearing, 806 cases remain. The resuscitation patient with return of spontaneous circulation (ROSC) is 68%, and the case of survival after 24 hour exceeds 50%. These results are better performance than international research. An initial rhythm of VT/VF had a higher ROSC rate than those with PEA (80.2% versus 68.9%, P = 0.006) or asystole (80.2% versus 58.5%%, P < 0.001). In rate of survived to discharge analysis, VT/VF were associated with a better outcome than PEA (32.2% versus 15.7%, P < 0.001) or asystole(32.2% versus 15.7%, p < 0.001).
To the best of our knowledge, this is the first web-based registry system on in-hospital resuscitation using the Utstein style, to collect data prospectively and effectively via the implementation of an on-line access web database. In the flourishing era of modern internet technology, data collection and further analysis through web services will be more convenient and efficient than previously adopted methods of collecting from papers format or transmission by e-mail. Our study has demonstrated the feasibility of a web-based designed structure of in-hospital registry system. This system not only helps clinicians tracking CPR event more easily, but also facilitates the possibility of data analysis and comparison between different institutions. In the future, we will focus on data mining research. These technologies will help physician to detect high risk factor of cardiac arrest patient in order to improve resuscitation quality and increase survival rate.
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