Comparison of physician-certified verbal autopsy with computer-coded verbal autopsy for cause of death assignment in hospitalized patients in low- and middle-income countries: Systematic review

Jordana Leitao, Nikita Desai, Lukasz Aleksandrowicz, Peter Byass, Pierre Miasnikof, Stephen Tollman, Dewan Alam, Ying Lu, Suresh K. Rathi, Abhishek Singh, Wilson Suraweera, Faujdar Ram, Prabhat Jha

Research output: Contribution to journalArticle

Abstract

Background: Computer-coded verbal autopsy (CCVA) methods to assign causes of death (CODs) for medically unattended deaths have been proposed as an alternative to physician-certified verbal autopsy (PCVA). We conducted a systematic review of 19 published comparison studies (from 684 evaluated), most of which used hospital-based deaths as the reference standard. We assessed the performance of PCVA and five CCVA methods: Random Forest, Tariff, InterVA, King-Lu, and Simplified Symptom Pattern.Methods: The reviewed studies assessed methods' performance through various metrics: sensitivity, specificity, and chance-corrected concordance for coding individual deaths, and cause-specific mortality fraction (CSMF) error and CSMF accuracy at the population level. These results were summarized into means, medians, and ranges.Results: The 19 studies ranged from 200 to 50,000 deaths per study (total over 116,000 deaths). Sensitivity of PCVA versus hospital-assigned COD varied widely by cause, but showed consistently high specificity. PCVA and CCVA methods had an overall chance-corrected concordance of about 50% or lower, across all ages and CODs. At the population level, the relative CSMF error between PCVA and hospital-based deaths indicated good performance for most CODs. Random Forest had the best CSMF accuracy performance, followed closely by PCVA and the other CCVA methods, but with lower values for InterVA-3.Conclusions: There is no single best-performing coding method for verbal autopsies across various studies and metrics. There is little current justification for CCVA to replace PCVA, particularly as physician diagnosis remains the worldwide standard for clinical diagnosis on live patients. Further assessments and large accessible datasets on which to train and test combinations of methods are required, particularly for rural deaths without medical attention.

Original languageEnglish (US)
Article number22
JournalBMC Medicine
Volume12
Issue number1
DOIs
StatePublished - Feb 4 2014

Fingerprint

Cause of Death
Autopsy
Physicians
Mortality
Population
Sensitivity and Specificity

Keywords

  • Causes of death
  • Computer-coded verbal autopsy
  • InterVA
  • King and Lu
  • Physician-certified verbal autopsy
  • Random forest
  • Simplified symptom pattern
  • Tariff
  • Validity
  • Verbal autopsy

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Comparison of physician-certified verbal autopsy with computer-coded verbal autopsy for cause of death assignment in hospitalized patients in low- and middle-income countries : Systematic review. / Leitao, Jordana; Desai, Nikita; Aleksandrowicz, Lukasz; Byass, Peter; Miasnikof, Pierre; Tollman, Stephen; Alam, Dewan; Lu, Ying; Rathi, Suresh K.; Singh, Abhishek; Suraweera, Wilson; Ram, Faujdar; Jha, Prabhat.

In: BMC Medicine, Vol. 12, No. 1, 22, 04.02.2014.

Research output: Contribution to journalArticle

Leitao, J, Desai, N, Aleksandrowicz, L, Byass, P, Miasnikof, P, Tollman, S, Alam, D, Lu, Y, Rathi, SK, Singh, A, Suraweera, W, Ram, F & Jha, P 2014, 'Comparison of physician-certified verbal autopsy with computer-coded verbal autopsy for cause of death assignment in hospitalized patients in low- and middle-income countries: Systematic review', BMC Medicine, vol. 12, no. 1, 22. https://doi.org/10.1186/1741-7015-12-22
Leitao, Jordana ; Desai, Nikita ; Aleksandrowicz, Lukasz ; Byass, Peter ; Miasnikof, Pierre ; Tollman, Stephen ; Alam, Dewan ; Lu, Ying ; Rathi, Suresh K. ; Singh, Abhishek ; Suraweera, Wilson ; Ram, Faujdar ; Jha, Prabhat. / Comparison of physician-certified verbal autopsy with computer-coded verbal autopsy for cause of death assignment in hospitalized patients in low- and middle-income countries : Systematic review. In: BMC Medicine. 2014 ; Vol. 12, No. 1.
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abstract = "Background: Computer-coded verbal autopsy (CCVA) methods to assign causes of death (CODs) for medically unattended deaths have been proposed as an alternative to physician-certified verbal autopsy (PCVA). We conducted a systematic review of 19 published comparison studies (from 684 evaluated), most of which used hospital-based deaths as the reference standard. We assessed the performance of PCVA and five CCVA methods: Random Forest, Tariff, InterVA, King-Lu, and Simplified Symptom Pattern.Methods: The reviewed studies assessed methods' performance through various metrics: sensitivity, specificity, and chance-corrected concordance for coding individual deaths, and cause-specific mortality fraction (CSMF) error and CSMF accuracy at the population level. These results were summarized into means, medians, and ranges.Results: The 19 studies ranged from 200 to 50,000 deaths per study (total over 116,000 deaths). Sensitivity of PCVA versus hospital-assigned COD varied widely by cause, but showed consistently high specificity. PCVA and CCVA methods had an overall chance-corrected concordance of about 50{\%} or lower, across all ages and CODs. At the population level, the relative CSMF error between PCVA and hospital-based deaths indicated good performance for most CODs. Random Forest had the best CSMF accuracy performance, followed closely by PCVA and the other CCVA methods, but with lower values for InterVA-3.Conclusions: There is no single best-performing coding method for verbal autopsies across various studies and metrics. There is little current justification for CCVA to replace PCVA, particularly as physician diagnosis remains the worldwide standard for clinical diagnosis on live patients. Further assessments and large accessible datasets on which to train and test combinations of methods are required, particularly for rural deaths without medical attention.",
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AU - Aleksandrowicz, Lukasz

AU - Byass, Peter

AU - Miasnikof, Pierre

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AU - Alam, Dewan

AU - Lu, Ying

AU - Rathi, Suresh K.

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N2 - Background: Computer-coded verbal autopsy (CCVA) methods to assign causes of death (CODs) for medically unattended deaths have been proposed as an alternative to physician-certified verbal autopsy (PCVA). We conducted a systematic review of 19 published comparison studies (from 684 evaluated), most of which used hospital-based deaths as the reference standard. We assessed the performance of PCVA and five CCVA methods: Random Forest, Tariff, InterVA, King-Lu, and Simplified Symptom Pattern.Methods: The reviewed studies assessed methods' performance through various metrics: sensitivity, specificity, and chance-corrected concordance for coding individual deaths, and cause-specific mortality fraction (CSMF) error and CSMF accuracy at the population level. These results were summarized into means, medians, and ranges.Results: The 19 studies ranged from 200 to 50,000 deaths per study (total over 116,000 deaths). Sensitivity of PCVA versus hospital-assigned COD varied widely by cause, but showed consistently high specificity. PCVA and CCVA methods had an overall chance-corrected concordance of about 50% or lower, across all ages and CODs. At the population level, the relative CSMF error between PCVA and hospital-based deaths indicated good performance for most CODs. Random Forest had the best CSMF accuracy performance, followed closely by PCVA and the other CCVA methods, but with lower values for InterVA-3.Conclusions: There is no single best-performing coding method for verbal autopsies across various studies and metrics. There is little current justification for CCVA to replace PCVA, particularly as physician diagnosis remains the worldwide standard for clinical diagnosis on live patients. Further assessments and large accessible datasets on which to train and test combinations of methods are required, particularly for rural deaths without medical attention.

AB - Background: Computer-coded verbal autopsy (CCVA) methods to assign causes of death (CODs) for medically unattended deaths have been proposed as an alternative to physician-certified verbal autopsy (PCVA). We conducted a systematic review of 19 published comparison studies (from 684 evaluated), most of which used hospital-based deaths as the reference standard. We assessed the performance of PCVA and five CCVA methods: Random Forest, Tariff, InterVA, King-Lu, and Simplified Symptom Pattern.Methods: The reviewed studies assessed methods' performance through various metrics: sensitivity, specificity, and chance-corrected concordance for coding individual deaths, and cause-specific mortality fraction (CSMF) error and CSMF accuracy at the population level. These results were summarized into means, medians, and ranges.Results: The 19 studies ranged from 200 to 50,000 deaths per study (total over 116,000 deaths). Sensitivity of PCVA versus hospital-assigned COD varied widely by cause, but showed consistently high specificity. PCVA and CCVA methods had an overall chance-corrected concordance of about 50% or lower, across all ages and CODs. At the population level, the relative CSMF error between PCVA and hospital-based deaths indicated good performance for most CODs. Random Forest had the best CSMF accuracy performance, followed closely by PCVA and the other CCVA methods, but with lower values for InterVA-3.Conclusions: There is no single best-performing coding method for verbal autopsies across various studies and metrics. There is little current justification for CCVA to replace PCVA, particularly as physician diagnosis remains the worldwide standard for clinical diagnosis on live patients. Further assessments and large accessible datasets on which to train and test combinations of methods are required, particularly for rural deaths without medical attention.

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KW - King and Lu

KW - Physician-certified verbal autopsy

KW - Random forest

KW - Simplified symptom pattern

KW - Tariff

KW - Validity

KW - Verbal autopsy

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