Validation of an algorithm to evaluate the appropriateness of outpatient antibiotic prescribing using big data of Chinese diagnosis text
© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ..
OBJECTIVE: We aimed to evaluate the validity of an algorithm to classify diagnoses according to the appropriateness of outpatient antibiotic use in the context of Chinese free text.
SETTING AND PARTICIPANTS: A random sample of 10 000 outpatient visits was selected between January and April 2018 from a national database for monitoring rational use of drugs, which included data from 194 secondary and tertiary hospitals in China.
RESEARCH DESIGN: Diagnoses for outpatient visits were classified as tier 1 if associated with at least one condition that 'always' justified antibiotic use; as tier 2 if associated with at least one condition that only 'sometimes' justified antibiotic use but no conditions that 'always' justified antibiotic use; or as tier 3 if associated with only conditions that never justified antibiotic use, using a tier-fashion method and regular expression (RE)-based algorithm.
MEASURES: Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the classification algorithm, using classification made by chart review as the standard reference, were calculated.
RESULTS: The sensitivities of the algorithm for classifying tier 1, tier 2 and tier 3 diagnoses were 98.2% (95% CI 96.4% to 99.3%), 98.4% (95% CI 97.6% to 99.1%) and 100.0% (95% CI 100.0% to 100.0%), respectively. The specificities were 100.0% (95% CI 100.0% to 100.0%), 100.0% (95% CI 99.9% to 100.0%) and 98.6% (95% CI 97.9% to 99.1%), respectively. The PPVs for classifying tier 1, tier 2 and tier 3 diagnoses were 100.0% (95% CI 99.1% to 100.0%), 99.7% (95% CI 99.2% to 99.9%) and 99.7% (95% CI 99.6% to 99.8%), respectively. The NPVs were 99.9% (95% CI 99.8% to 100.0%), 99.8% (95% CI 99.7% to 99.9%) and 100.0% (95% CI 99.8% to 100.0%), respectively.
CONCLUSIONS: The RE-based classification algorithm in the context of Chinese free text had sufficiently high validity for further evaluating the appropriateness of outpatient antibiotic prescribing.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
|---|---|
| Enthalten in: |
BMJ open - 10(2020), 3 vom: 19. März, Seite e031191 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Zhao, Houyu [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Anti-Bacterial Agents |
|---|
| Anmerkungen: |
Date Completed 13.04.2021 Date Revised 13.04.2021 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1136/bmjopen-2019-031191 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM307846342 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM307846342 | ||
| 003 | DE-627 | ||
| 005 | 20231226201244.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1136/bmjopen-2019-031191 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1026.xml |
| 035 | |a (DE-627)NLM307846342 | ||
| 035 | |a (NLM)32198296 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Zhao, Houyu |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Validation of an algorithm to evaluate the appropriateness of outpatient antibiotic prescribing using big data of Chinese diagnosis text |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 13.04.2021 | ||
| 500 | |a Date Revised 13.04.2021 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. | ||
| 520 | |a OBJECTIVE: We aimed to evaluate the validity of an algorithm to classify diagnoses according to the appropriateness of outpatient antibiotic use in the context of Chinese free text | ||
| 520 | |a SETTING AND PARTICIPANTS: A random sample of 10 000 outpatient visits was selected between January and April 2018 from a national database for monitoring rational use of drugs, which included data from 194 secondary and tertiary hospitals in China | ||
| 520 | |a RESEARCH DESIGN: Diagnoses for outpatient visits were classified as tier 1 if associated with at least one condition that 'always' justified antibiotic use; as tier 2 if associated with at least one condition that only 'sometimes' justified antibiotic use but no conditions that 'always' justified antibiotic use; or as tier 3 if associated with only conditions that never justified antibiotic use, using a tier-fashion method and regular expression (RE)-based algorithm | ||
| 520 | |a MEASURES: Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the classification algorithm, using classification made by chart review as the standard reference, were calculated | ||
| 520 | |a RESULTS: The sensitivities of the algorithm for classifying tier 1, tier 2 and tier 3 diagnoses were 98.2% (95% CI 96.4% to 99.3%), 98.4% (95% CI 97.6% to 99.1%) and 100.0% (95% CI 100.0% to 100.0%), respectively. The specificities were 100.0% (95% CI 100.0% to 100.0%), 100.0% (95% CI 99.9% to 100.0%) and 98.6% (95% CI 97.9% to 99.1%), respectively. The PPVs for classifying tier 1, tier 2 and tier 3 diagnoses were 100.0% (95% CI 99.1% to 100.0%), 99.7% (95% CI 99.2% to 99.9%) and 99.7% (95% CI 99.6% to 99.8%), respectively. The NPVs were 99.9% (95% CI 99.8% to 100.0%), 99.8% (95% CI 99.7% to 99.9%) and 100.0% (95% CI 99.8% to 100.0%), respectively | ||
| 520 | |a CONCLUSIONS: The RE-based classification algorithm in the context of Chinese free text had sufficiently high validity for further evaluating the appropriateness of outpatient antibiotic prescribing | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Validation Study | |
| 650 | 4 | |a antibiotics | |
| 650 | 4 | |a drug utilisation | |
| 650 | 4 | |a electronic health records | |
| 650 | 4 | |a prescriptions | |
| 650 | 4 | |a validation | |
| 650 | 7 | |a Anti-Bacterial Agents |2 NLM | |
| 700 | 1 | |a Bian, Jiaming |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Liuyi |e verfasserin |4 aut | |
| 700 | 1 | |a Ying, Yingqiu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zeyu |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Xiaoying |e verfasserin |4 aut | |
| 700 | 1 | |a Zhuo, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Cao, Bin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Mei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhan, Siyan |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t BMJ open |d 2012 |g 10(2020), 3 vom: 19. März, Seite e031191 |w (DE-627)NLM215724372 |x 2044-6055 |7 nnns |
| 773 | 1 | 8 | |g volume:10 |g year:2020 |g number:3 |g day:19 |g month:03 |g pages:e031191 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1136/bmjopen-2019-031191 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 10 |j 2020 |e 3 |b 19 |c 03 |h e031191 | ||