Performance of four computer-coded verbal autopsy methods for cause of death assignment compared with physician coding on 24,000 deaths in low- and middle-income countriesReportar como inadecuado




Performance of four computer-coded verbal autopsy methods for cause of death assignment compared with physician coding on 24,000 deaths in low- and middle-income countries - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

BMC Medicine

, 12:20

Medicine for Global Health

Abstract

BackgroundPhysician-coded verbal autopsy PCVA is the most widely used method to determine causes of death CODs in countries where medical certification of death is uncommon. Computer-coded verbal autopsy CCVA methods have been proposed as a faster and cheaper alternative to PCVA, though they have not been widely compared to PCVA or to each other.

MethodsWe compared the performance of open-source random forest, open-source tariff method, InterVA-4, and the King-Lu method to PCVA on five datasets comprising over 24,000 verbal autopsies from low- and middle-income countries. Metrics to assess performance were positive predictive value and partial chance-corrected concordance at the individual level, and cause-specific mortality fraction accuracy and cause-specific mortality fraction error at the population level.

ResultsThe positive predictive value for the most probable COD predicted by the four CCVA methods averaged about 43% to 44% across the datasets. The average positive predictive value improved for the top three most probable CODs, with greater improvements for open-source random forest 69% and open-source tariff method 68% than for InterVA-4 62%. The average partial chance-corrected concordance for the most probable COD predicted by the open-source random forest, open-source tariff method and InterVA-4 were 41%, 40% and 41%, respectively, with better results for the top three most probable CODs. Performance generally improved with larger datasets. At the population level, the King-Lu method had the highest average cause-specific mortality fraction accuracy across all five datasets 91%, followed by InterVA-4 72% across three datasets, open-source random forest 71% and open-source tariff method 54%.

ConclusionsOn an individual level, no single method was able to replicate the physician assignment of COD more than about half the time. At the population level, the King-Lu method was the best method to estimate cause-specific mortality fractions, though it does not assign individual CODs. Future testing should focus on combining different computer-coded verbal autopsy tools, paired with PCVA strengths. This includes using open-source tools applied to larger and varied datasets especially those including a random sample of deaths drawn from the population, so as to establish the performance for age- and sex-specific CODs.

KeywordsCauses of death Computer-coded verbal autopsy CCVA InterVA-4 King-Lu Physician-certified verbal autopsy PCVA Random forest Tariff method Validation Verbal autopsy AbbreviationsCCVAcomputer-coded verbal autopsy

CODcause of death

CSMFcause-specific mortality fraction

HCEhealth care experience

ICD-10International Classification of Diseases-10

IHMEInstitute for Health Metrics and Evaluation

KLKing-Lu verbal autopsy method

MDSMillion Death Study

ORFopen-source random forest

OTMopen-source tariff method

PCCCpartial chance-corrected concordance

PCVAphysician-certified verbal autopsy

PPVpositive predictive value

VAverbal autopsy.

Electronic supplementary materialThe online version of this article doi:10.1186-1741-7015-12-20 contains supplementary material, which is available to authorized users.

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Autor: Nikita Desai - Lukasz Aleksandrowicz - Pierre Miasnikof - Ying Lu - Jordana Leitao - Peter Byass - Stephen Tollman - Paul 

Fuente: https://link.springer.com/







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