Evidence map›Paper›PMID 29433509›Full record

ArticleAIDS research and therapy2018

Prognostic indicators in the World Health Organization's algorithm for seriously ill HIV-infected inpatients with suspected tuberculosis.

Rulan Griesel, Annemie Stewart, Helen van der Plas, Welile Sikhondze, Marc Mendelson, Gary Maartens

Open access · goldAbstract read
In one paragraph

Article in AIDS research and therapy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Tuberculosis deaths are predictable and preventable: Comprehensive assessment and clinical care is the key.Journal of clinical tuberculosis and other mycobacterial diseases · 2020
    Article
  12. Observational
  13. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Rulan GrieselDivision of Clinical Pharmacology, Department of Medicine, UCT Faculty of Health Sciences, Anzio Road, Observatory, Cape Town, 7925, South Africa.
Annemie StewartDivision of Clinical Pharmacology, Department of Medicine, UCT Faculty of Health Sciences, Anzio Road, Observatory, Cape Town, 7925, South Africa.
Helen van der PlasDivision of Infectious Diseases and HIV Medicine, Department of Medicine, University of Cape Town, Cape Town, South Africa.
Welile SikhondzeDivision of Infectious Diseases and HIV Medicine, Department of Medicine, University of Cape Town, Cape Town, South Africa.
Marc MendelsonDivision of Infectious Diseases and HIV Medicine, Department of Medicine, University of Cape Town, Cape Town, South Africa.
Gary MaartensDivision of Clinical Pharmacology, Department of Medicine, UCT Faculty of Health Sciences, Anzio Road, Observatory, Cape Town, 7925, South Africa. gary.maartens@uct.ac.za.
University of Cape Town · ZA

Funding

An evidence based algorithm for diagnosis of tuberculosis in HIV patientsR01AI096735 · NIAID · UNIVERSITY OF CAPE TOWN · PI MAARTENS, GARY, MENDELSON, MARC · 2011 to 2014
$369k
NIAID NIH HHS R01 AI096735
6 · The paper itself

Abstract

backgroundCriteria for the 2007 WHO algorithm for diagnosing tuberculosis among HIV-infected seriously ill patients are the presence of one or more danger signs (respiratory rate > 30/min, heart rate > 120/min, temperature > 39 °C, and being unable to walk unaided) and cough ≥ 14 days. Determining predictors of poor outcomes among HIV-infected inpatients presenting with WHO danger signs could result in improved treatment and diagnostic algorithms.

methodsWe conducted a prospective cohort study of inpatients presenting with any duration of cough and WHO danger signs to two regional hospitals in Cape Town, South Africa. The primary outcome was all-cause mortality up to 56 days post-discharge, and the secondary outcome a composite of any one of: hospital admission for > 7 days, died in hospital, transfer to a tertiary level or tuberculosis hospital. We first assessed the WHO danger signs as predictors of poor outcomes, then assessed the added value of other variables selected a priori for their ability to predict mortality in common respiratory opportunistic infections (CD4 count, body mass index (BMI), being on antiretroviral therapy (ART), hypotension, and confusion) by comparing the receiver operating characteristic (ROC) area under the curve (AUC) of the two multivariate models.

results484 participants were enrolled, median age 36, 66% women, 53% had tuberculosis confirmed on culture. The 56-day mortality was 13.2%. Inability to walk unaided, low BMI, low CD4 count, and being on ART were independently associated with poor outcomes. The multivariate model of the WHO danger signs showed a ROC AUC of 0.649 (95% CI 0.582-0.717) for predicting 56-day mortality, which improved to ROC AUC of 0.740 (95% CI 0.681-0.800; p = 0.004 for comparison between the two ROC AUCs) with the multivariate model including the a priori selected variables. Findings were similar in sub-analyses of participants with culture-positive tuberculosis and with cough duration ≥ 14 days.

conclusionThe study design prevented a rigorous evaluation of the prognostic value of the WHO danger signs. Our prognostic model could result in improved algorithms, but needs to be validated.

Indexed as

InpatientsAdolescentAdultAIDS-Related Opportunistic InfectionsAlgorithmsCoinfectionFemaleHIV InfectionsHumansMaleOutcome Assessment, Health CarePrognosisProspective StudiesROC CurveSeverity of Illness IndexTuberculosisBacterial pneumoniaHIVMortalityPneumocystis jirovecii pneumoniaPrognosisTuberculosisWHO algorithm

Identifiers

PMID29433509
PMCPMC5808414
OpenAlexW2794743410

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.