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Enregistrement W3190810782 · doi:10.1016/s1473-3099(21)00050-5

Effects of antibiotic resistance, drug target attainment, bacterial pathogenicity and virulence, and antibiotic access and affordability on outcomes in neonatal sepsis: an international microbiology and drug evaluation prospective substudy (BARNARDS)

2021· article· en· W3190810782 sur OpenAlexaff
Kathryn Thomson, Calie Dyer, Feiyan Liu, Kirsty Sands, Edward Portal, Maria J. Carvalho, M. Barrell, Ian Boostrom, Susanna Dunachie, Refath Farzana, Ana Ferreira, Francis Frayne, Brekhna Hassan, Lim Jones, Jordan Mathias, Rebecca Milton, Jessica Rees, Grace J Chan, Delayehu Bekele, Mahlet Abayneh, Sulagna Basu, Ranjan K. Nandy, Kenneth Iregbu, Fatima Modibbo, Stella Uwaezuoke, Rabaab Zahra, Haider Shirazi, Najeeb U Syed, Jean-Baptiste Mazarati, Aniceth Rucogoza, Lucie Gaju, Shaheen Mehtar, Andre Nyandwe Hamama Bulabula, J. G. Coen van Hasselt, Timothy R. Walsh, Samir K. Saha, Zabed Bin-Ahmed, Wazir Ahmed, Taslima Begum, Mitu Chowdhury, Shaila Sharmin, Chumki Rani Dey, Sowmitra Ranjan Chakraborty, Sadia Tasmin, Dipa Rema, Rashida Khatun, Liza Nath, Balkachew Nigatu, Katherine Cl Schaughency, Semaria Solomon, Zenebe Gebreyohanes, Rozina Ambachew, Oludare A. Odumade, Misgana Haileselassie, Abigail A. Russo, Redeat Workneh, Gesit Metaferia, Yahya Mohammed, Tefera Biteye, Alula M. Teklu, Wendimagegn Gezahegn, Partha Sarathi Chakravorty, Anuradha Mukherjee, Samarpan Roy, Anuradha Sinha, Sharmi Naha, Sukla Saha Malakar, Siddhartha Bose, Monaki Majhi, Subhasree Sahoo, Putul Mukherjee, Sumitra Kumari Routa, Chaitali Nandi, Pinaki Chattopadhyay, Fatima Z Modibbo, Dilichukwu Meduekwe, Khairiyya Muhammad, Queen Nsude, Ifeoma Ukeh, Mary-Joe Okenu, Chinenye Akpulu, Samuel Yakubu, Vivian Asunugwo, Folake Aina, Isibong Issy, Dolapo Adekeye, Adiele Eunice, Abdulmlik Amina, R Oyewole, I Oloton, BC Nnaji, M Umejiego, PN Anoke, Saheed O. Adebayo, GO Abegunrin, OB Omotosho, Risqot Garba Ibrahim, Blessing Njideka Igwe, M Abroko, K Balami, L Bayem, C H Anyanwu, Hidenori Haruna, J Okike, K Goroh, M Boi-Sunday, Augusta Ugafor, Maryam Makama, Kaniba Ndukwe, Anastesia Odama, Hadiza Yusuf, Patience Wachukwu, Kachalla Yahaya, Titus Kalade Colsons, Mercy Kura, Damilola Orebiyi, Chukwuemeka Mmadueke, Lamidi Audu, Nura Idris, Safiya Gambo, Jamila Ibrahim, Edwin Precious, Ashiru Hassan, Shamsudden Gwadabe, Adeola Adeleye Falola, Muhammad Aliyu, Amina Ibrahim, Aisha Mukaddas, Rashida Yakubu Khalid, Fatima Ibrahim Alkali, Fatima Mohammad Tukur, Surayya Mustapha Muhammad, Adeola Shittu, Murjanatu Bello, Muhammad Abubakar Hassan, Fatima Habib Sa ad, Aishatu Kassim, Adil Muhammad, Syed Najeeb Ullah, Muhammad Hilal Jan, Rubina Kamran, Jazba Saeed, Noreen Maqsood, Maria Zafar, Saraeen Sadiq, Sumble Ahsan, Madiha Tariq, Sidra Sajid, Hasma Mustafa, Anees-ur Rehman, Atif Muhammad, Gahssan Mehmood, Mahnoor Nisar, Shermeen Akif, Tahira Yasmeen, Sabir Nawaz, Anam Shanal Atta, Mian Laiq-ur-Rehman, Robina Kousar, Kalsoom Bibi, Kosar Waheed, Zainab Majeed, Ayesha Jalil, Espoir Kajibwami, Innocent Nzabahimana, Kankundiye Riziki, Brigette Uwamahoro, Rachel Uwera, Eugenie Nyiratuza, Muzungu Kumwami, Violette Uwitonze, Marie C Horanimpundu, Francine Nzeyimana, Prince Mitima, Angela Dramowski, Lauren Paterson, Mary Frans, Marvina Johnson, Eveline Swanepoel, Zoleka Bojana, Mieme du Preez, Johan GC van Hasselt, Robert Andrews, John E. Watkins, David Gillespie, Katie Taiyai, Nigel Kirby, Maria Nieto, Thomas Hender, Patrick G. Hogan, B Spiller, Julian Parkhill

Notice bibliographique

RevueThe Lancet Infectious Diseases · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueNeonatal and Maternal Infections
Établissements canadiensInstitute of Infection and Immunity
Organismes subventionnairesMedical Research CouncilNational Institute for Health and Care ResearchBill and Melinda Gates Foundation
Mots-clésSepsisNeonatal sepsisAntibiotic resistanceMedicineAntibioticsGentamicinAmpicillinDrug resistanceIntensive care medicineAntimicrobialInternal medicineMicrobiologyBiology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Sepsis is a major contributor to neonatal mortality, particularly in low-income and middle-income countries (LMICs). WHO advocates ampicillin-gentamicin as first-line therapy for the management of neonatal sepsis. In the BARNARDS observational cohort study of neonatal sepsis and antimicrobial resistance in LMICs, common sepsis pathogens were characterised via whole genome sequencing (WGS) and antimicrobial resistance profiles. In this substudy of BARNARDS, we aimed to assess the use and efficacy of empirical antibiotic therapies commonly used in LMICs for neonatal sepsis. METHODS: In BARNARDS, consenting mother-neonates aged 0-60 days dyads were enrolled on delivery or neonatal presentation with suspected sepsis at 12 BARNARDS clinical sites in Bangladesh, Ethiopia, India, Pakistan, Nigeria, Rwanda, and South Africa. Stillborn babies were excluded from the study. Blood samples were collected from neonates presenting with clinical signs of sepsis, and WGS and minimum inhibitory concentrations for antibiotic treatment were determined for bacterial isolates from culture-confirmed sepsis. Neonatal outcome data were collected following enrolment until 60 days of life. Antibiotic usage and neonatal outcome data were assessed. Survival analyses were adjusted to take into account potential clinical confounding variables related to the birth and pathogen. Additionally, resistance profiles, pharmacokinetic-pharmacodynamic probability of target attainment, and frequency of resistance (ie, resistance defined by in-vitro growth of isolates when challenged by antibiotics) were assessed. Questionnaires on health structures and antibiotic costs evaluated accessibility and affordability. FINDINGS: Between Nov 12, 2015, and Feb 1, 2018, 36 285 neonates were enrolled into the main BARNARDS study, of whom 9874 had clinically diagnosed sepsis and 5749 had available antibiotic data. The four most commonly prescribed antibiotic combinations given to 4451 neonates (77·42%) of 5749 were ampicillin-gentamicin, ceftazidime-amikacin, piperacillin-tazobactam-amikacin, and amoxicillin clavulanate-amikacin. This dataset assessed 476 prescriptions for 442 neonates treated with one of these antibiotic combinations with WGS data (all BARNARDS countries were represented in this subset except India). Multiple pathogens were isolated, totalling 457 isolates. Reported mortality was lower for neonates treated with ceftazidime-amikacin than for neonates treated with ampicillin-gentamicin (hazard ratio [adjusted for clinical variables considered potential confounders to outcomes] 0·32, 95% CI 0·14-0·72; p=0·0060). Of 390 Gram-negative isolates, 379 (97·2%) were resistant to ampicillin and 274 (70·3%) were resistant to gentamicin. Susceptibility of Gram-negative isolates to at least one antibiotic in a treatment combination was noted in 111 (28·5%) to ampicillin-gentamicin; 286 (73·3%) to amoxicillin clavulanate-amikacin; 301 (77·2%) to ceftazidime-amikacin; and 312 (80·0%) to piperacillin-tazobactam-amikacin. A probability of target attainment of 80% or more was noted in 26 neonates (33·7% [SD 0·59]) of 78 with ampicillin-gentamicin; 15 (68·0% [3·84]) of 27 with amoxicillin clavulanate-amikacin; 93 (92·7% [0·24]) of 109 with ceftazidime-amikacin; and 70 (85·3% [0·47]) of 76 with piperacillin-tazobactam-amikacin. However, antibiotic and country effects could not be distinguished. Frequency of resistance was recorded most frequently with fosfomycin (in 78 isolates [68·4%] of 114), followed by colistin (55 isolates [57·3%] of 96), and gentamicin (62 isolates [53·0%] of 117). Sites in six of the seven countries (excluding South Africa) stated that the cost of antibiotics would influence treatment of neonatal sepsis. INTERPRETATION: Our data raise questions about the empirical use of combined ampicillin-gentamicin for neonatal sepsis in LMICs because of its high resistance and high rates of frequency of resistance and low probability of target attainment. Accessibility and affordability need to be considered when advocating antibiotic treatments with variance in economic health structures across LMICs. FUNDING: The Bill & Melinda Gates Foundation.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,022

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,005
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,300
Écart entre enseignants0,289 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations131
Publié2021
Routes d'admission1
Résumé présentoui

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