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

doi:10.1186/1741-7015-12-22

Abstract

BackgroundComputer-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.MethodsThe 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.ResultsThe 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.ConclusionsThere 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.

Highlights

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 included eight studies evaluating the performance of PCVA, seven studies evaluating InterVA, and one study for each of the KL, Random Forest (RF), Tariff and Simplified Symptom Pattern (SSP) methods
PCVA and CCVA methods differ in their performance of coding hospital-based deaths, and there is no single best-performing method

Summary

Introduction

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). Verbal autopsy (VA) has been increasingly used in LMICs to define causes of death (CODs). VA entails an interview with a relative or close associate of the deceased, using a questionnaire to elicit information on the signs, symptoms and chronological sequence of events during the final illness leading to death. COD surveys have mostly informed specific research needs in small populations, and have largely focused on child or maternal deaths [4]. There is interest in the use of VA for large-scale nationally representative COD surveys, such as the ongoing Indian Million Death Study (MDS) [5,6] and others in Africa [7]

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Discussion

Conclusion