• 沒有找到結果。

time(day) to discontinuation

400 300

200 100

0

cum ula ti ve s ur vi va l

1.0 .9 .8 .7 .6 .5 .4 .3 .2 .1 0.0

drug

ZIP RIS QUE OLA LOD CLO ARI AMI

Overall comparison statistic= 17.961, df.=7, p=0.0121 Figure 1 Life-table survival curve of antipsychotics in 2005

4. Discussion

There were many factors affecting doctors’ prescrip-tion pattern, such as lack of efficacy, tolerability issue ow-ing to side effect, the official policy by National Health Insurance, and coping strategy developed by hospitals.

Patient’s condition such as poor disease insight compro-mised drug compliance and patients dropped out later.

Even under the influences of so many factors, it was said

that treatment continuation was a good indicator for treat-ment effect.[11, 12] It provided a convenient way to han-dle the complicated effects of antipsychotics’ advantages and disadvantages. We tried to use pharmacy prescrip-tion database from HIS to compare the outcome of differ-ent antipsychotics’ effect on patidiffer-ents.

Selection bias was inevitable in such study design and could not be managed by statistics. Those patients pre-scribed with clozapine were quite different from those

with aripiprazole in clinical setting. There were more males in refractory schizophrenic patients and it might ex-plain the gender difference between clozapine and other antipsychotics. The ratio of female to male was about two in patients prescribed with ziprasidone and aripipra-zole. It might be due to the least likely causibility of hy-perprolactinemia by these two antipsychotics. However, when stratifying the age group and gender, we could have separated the different reasons for prescription antipsy-chotics. It could be further developed as a clinical deci-sion support system. For examples: a 27 year-old female diagnosed as schizophrenia for years came to outpatient service for her relapsing psychotic symptoms, which an-tipsychotic was preferred? With this clinical decision support system, the doctor chose “gender= female” and

“age group= 20-30” on the screen and the computer would show him that those with the same characteristic taken dif-ferent antipsychotics have difdif-ferent survival curves. He could make decision by himself referring this system fi-nally.

Further efforts were needed. We calculated the treatment duration by start-date and end-date. However, there were patients who take some antipsychotic at first, change to another antipsychotic later, and take the original antipsychotic finally. In this kind of situation, we overly estimated the treatment duration and hence this system gave us an optimistic suggestion. It might compromise patients’ best interest. We would try to review every visit of patients and separate those discontinuited visits in later studies.

Some might recommend to do the same thing from NHI database. The availability of different antipsy-chotics of interested differed from hospitals to hospitals.

There might be only one choice in one hospital and six choices in the other hospitals. However, it was possible

if we limited our topic on some major mental hospitals.

The other five major mental hospitals had the same HIS and this result could be applied to other hospital easily.

We could also try to set up a clinical decision support system by using databases of six major mental hospital with the same HIS to get more objective results. .

5. Conclusion

We could see the brightening future to use pharmacy database to construct a clinical decision support system in schizophrenia outpatients, disregarding the underdevel-oped electric medical records. It would be more promis-ing if we further manage the data and collect databases from the other five major mental hospitals with the same HIS.

6. Reference

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