Evidence map›Paper›PMID 38605292›Full record

ArticleBMC infectious diseases2024

Influence of initial misdiagnosis on mortality in patients with bacteraemia: propensity score matching and propensity score weighting analyses.

Anna M Eikenboom, Merel M C Lambregts, Mark G J de Boer, Saskia le Cessie

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Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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2 citing papers in PubMed, 0 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Anna M EikenboomDepartment of Infectious Diseases, Leiden University Medical Centre (LUMC), Leiden, The Netherlands.
Merel M C LambregtsDepartment of Infectious Diseases, Leiden University Medical Centre (LUMC), Leiden, The Netherlands.
Mark G J de BoerDepartment of Infectious Diseases, Leiden University Medical Centre (LUMC), Leiden, The Netherlands. M.G.J.de_Boer@LUMC.nl.
Saskia le CessieDepartment of Clinical Epidemiology, Leiden University Medical Centre (LUMC), Leiden, The Netherlands. S.le_Cessie@lumc.nl.
Leiden University Medical Center · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe diagnostic process is a key element of medicine but it is complex and prone to errors. Infectious diseases are one of the three categories of diseases in which diagnostic errors can be most harmful to patients. In this study we aimed to estimate the effect of initial misdiagnosis of the source of infection in patients with bacteraemia on 14 day mortality using propensity score methods to adjust for confounding.

methodsData from a previously described longitudinal cohort of patients diagnosed with monobacterial bloodstream infection (BSI) at the Leiden University Medical Centre (LUMC) between 2013 and 2015 were used. Propensity score matching and inversed probability of treatment weighting (IPTW) were applied to correct for confounding. The average treatment effect on the treated (ATT), which in this study was the average effect of initial misdiagnosis on the misdiagnosed (AEMM), was estimated. Methodological issues that were encountered when applying propensity score methods were addressed by performing additional sensitivity analyses. Sensitivity analyses consisted of varying caliper in propensity score matching and using different truncated weights in inversed probability of treatment weighting.

resultsData of 887 patients were included in the study. Propensity scores ranged between 0.015 and 0.999 and 80 patients (9.9%) had a propensity score > 0.95. In the matched analyses, 35 of the 171 misdiagnosed patients died within 14 days (20.5%), versus 10 of the 171 correctly diagnosed patients (5.8%), yielding a difference of 14.6% (7.6%; 21.6%). In the total group of patients, the observed percentage of patients with an incorrect initial diagnosis that died within 14 days was 19.8% while propensity score reweighting estimated that their probability of dying would have been 6.5%, if they had been correctly diagnosed (difference 13.3% (95% CI 6.9%;19.6%)). After adjustment for all variables that showed disbalance in the propensity score a difference of 13.7% (7.4%; 19.9%) was estimated. Sensitivity analyses yielded similar results. However, performing weighted analyses without truncation yielded unstable results.

conclusionThus, we observed a substantial increase of 14 day mortality in initially misdiagnosed patients. Furthermore, several patients received propensity scores extremely close to one and were almost sure to be initially misdiagnosed.

Indexed as

BacteremiaDiagnostic ErrorsHumansPropensity ScoreBacteraemiaIPTWMisdiagnosisMortalityPropensity score matching

Identifiers

PMID38605292
PMCPMC11007998
OpenAlexW4394718535

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.