Evidence map›Paper›PMID 36691117›Full record

Observational studyBMJ open2022

Risk factors for mortality in a hospitalised neonatal cohort in Botswana.

Eimear Kitt, Molly Hayes, Morgan Congdon, Lance Ballester, Kgotlaetsile B Sewawa, Unami Mulale, Loeto Mazhani, Tonya Arscott-Mills, Andrew Steenhoff, Susan Coffin

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.4field-weighted citation impact, top 8% 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

7 citing papers in PubMed, 10 citations in OpenAlex.

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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

10 authors at 2 institutions in 2 countries.

Eimear KittDivision of Infectious Diseases, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA kitte@chop.edu.ORCID 0000-0003-0142-2726
Molly HayesChildren's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Morgan CongdonDepartment of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Lance BallesterBiostatistics and Data Management Core, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Kgotlaetsile B SewawaDepartment of Paediatric & Adolescent Health, Faculty of Medicine, University of Botswana, Gaborone, South-East District, Botswana.
Unami MulaleDepartment of Paediatric & Adolescent Health, Faculty of Medicine, University of Botswana, Gaborone, South-East District, Botswana.
Loeto MazhaniDepartment of Paediatric & Adolescent Health, Faculty of Medicine, University of Botswana, Gaborone, South-East District, Botswana.
Tonya Arscott-MillsChildren's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Andrew SteenhoffDivision of Infectious Diseases, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Susan CoffinDivision of Infectious Diseases, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Children's Hospital of Philadelphia · USUniversity of Botswana · BW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesA disproportionate number of neonatal deaths occur in low/middle-income countries, with sepsis a leading contributor of mortality. In this study, we investigate risk factors for mortality in a cohort of high-risk hospitalised neonates in Botswana. Independent predictors for mortality for infants experiencing either a sepsis or a non-sepsis-related death are described.

methodsThis is a prospective observational cohort study with infants enrolled from July to October 2018 at the neonatal unit (NNU) of Princess Marina Hospital (PMH) in Gaborone, Botswana. Data on demographic, clinical and unit-specific variables were obtained. Neonates were followed to death or discharge, including transfer to another hospital. Death was determined to be infectious versus non-infectious based on primary diagnosis listed on day of death by lead clinician on duty.

resultsOur full cohort consisted of 229 patients. The overall death rate was 227 per 1000 live births, with cumulative proportion of deaths of 22.7% (n=47). Univariate analysis revealed that sepsis, extremely low birth weight (ELBW) status, hypoxic ischaemic encephalopathy, critical illness and infants born at home were associated with an increased risk of all-cause mortality. Our multivariate model revealed that critical illness (HR 3.07, 95% CI 1.56 to 6.03) and being born at home (HR 4.82, 95% CI 1.76 to 13.19) were independently associated with all-cause mortality. Low birth weight status was independently associated with a decreased risk of mortality (HR 0.24, 95% CI 0.11 to 0.53). There was a high burden of infection in the cohort with more than half of infants (140, 61.14%) diagnosed with sepsis at least once during their NNU admission. Approximately 20% (n=25) of infants with sepsis died before discharge. Our univariate subanalysis of the sepsis cohort revealed that ELBW and critical illness were associated with an increased risk of death. These findings persisted in the multivariate model with HR 3.60 (95% CI 1.11 to 11.71) and HR 2.39 (95% CI 1 to 5.77), respectively.

conclusionsHigh rates of neonatal mortality were noted. Urgent interventions are needed to improve survival rates at PMH NNU and to prioritise care for critically ill infants at time of NNU admission, particularly those born at home and/or of ELBW.

Indexed as

Critical IllnessSepsisBotswanaCohort StudiesHumansInfantInfant MortalityInfant, NewbornRisk Factorsinfectious diseasesneonatal intensive & critical carepaediatric infectious disease & immunisation

Identifiers

PMID36691117
PMCPMC9454043
OpenAlexW4294770803

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.