Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis: an individual participant data meta-analysis

Jeroen Hoogland; Toshihiko Takada; Maarten van Smeden; Maroeska M Rovers; An I de Sutter; Daniel Merenstein; Laurent Kaiser; Helena Liira; Paul Little; Heiner C Bucher; Karel G M Moons; Johannes B Reitsma; Roderick P Venekamp

doi:10.1186/s41512-023-00154-0

Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis: an individual participant data meta-analysis

Jeroen Hoogland
, Toshihiko Takada
, Maarten van Smeden
, Maroeska M Rovers
, An I de Sutter
, Daniel Merenstein
, Laurent Kaiser
, Helena Liira
, Paul Little
, Heiner C Bucher
, Karel G M Moons
, Johannes B Reitsma
, Roderick P Venekamp

Julius Center for Health Sciences and Primary Care, Netherlands
Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584CG Utrecht, The Netherlands; Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584CG Utrecht, The Netherlands; Department of Translational Neuroscience...
Department of Epidemiology and Data Science, Amsterdam UMC, PO Box 7057, 1007 MB Amsterdam, the Netherlands
Neurosurgical Centre Amsterdam, Amsterdam Medical Centre, Amsterdam University Medical Centres (UMC), University of Amsterdam, Amsterdam, the Netherlands.
Department of General Medicine, Adelaide, Australia
Shirakawa Satellite for Teaching And Research (STAR)
Department of Biofunctional Imaging, Fukushima Medical University, Fukushima, Japan; and.
Radboud Institute for Health Sciences (RIHS), 6525 EZ Nijmegen, Netherlands
Radboud University Medical Center, Radboud Institute for Health Sciences, IQ healthcare, Nijmegen; and Radboud University Medical Center, Department of Primary and Community Care, Nijmegen, the Netherlands.
Department of Public Health and Primary Care, Primary Care Unit, University of Cambridge, Cambridge, United Kingdom.
Center for Medical Genetics, Ghent University Hospital and Ghent University, Ghent, Belgium.
Department of Family Medicine, Hamilton, Canada
Georgetown University Medical Center, Washington, United States
Department of Medicine, Department of Surgery, Tokyo University School of Medicine, Tokyo, Japan.
Division of Infectious Diseases
Geneva University Hospital and Geneva University, Geneva, Switzerland.
Department of General Practice and General Practice Research Unit, Oslo, Norway
Netherlands School of Primary
Aboriginal and Rural Health Care
School of Women's and Infants' Health, The University of Western Australia, Perth, Western Australia, 6009, Australia.
Department of General Practice and Primary Health Care, Helsinki, Finland
Department of Virology, University of Helsinki and Helsinki University Hospital, University of Helsinki, 00014 Helsinki, Finland.
Primary Care & Population Sciences Unit
Aldermoor Health Centre
National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital, Southampton NHS Foundation Trust, Southampton, UK.
Clinical Epidemiology Division, Stockholm, Sweden
Center for Clinical Transfusion Research, Sanquin Research, Leiden, Netherlands; Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands.
Neurologic Clinic and Policlinic, University Hospital Basel, University of Basel, Basel, Switzerland/Charles University and General University Hospital, Prague, Czech Republic.

Research output: Contribution to journal › Article › Academic › peer-review

6 Downloads (Pure)

Abstract

BACKGROUND: A previous individual participant data meta-analysis (IPD-MA) of antibiotics for adults with clinically diagnosed acute rhinosinusitis (ARS) showed a marginal overall effect of antibiotics, but was unable to identify patients that are most likely to benefit from antibiotics when applying conventional (i.e. univariable or one-variable-at-a-time) subgroup analysis. We updated the systematic review and investigated whether multivariable prediction of patient-level prognosis and antibiotic treatment effect may lead to more tailored treatment assignment in adults presenting to primary care with ARS.

METHODS: An IPD-MA of nine double-blind placebo-controlled trials of antibiotic treatment (n=2539) was conducted, with the probability of being cured at 8-15 days as the primary outcome. A logistic mixed effects model was developed to predict the probability of being cured based on demographic characteristics, signs and symptoms, and antibiotic treatment assignment. Predictive performance was quantified based on internal-external cross-validation in terms of calibration and discrimination performance, overall model fit, and the accuracy of individual predictions.

RESULTS: Results indicate that the prognosis with respect to risk of cure could not be reliably predicted (c-statistic 0.58 and Brier score 0.24). Similarly, patient-level treatment effect predictions did not reliably distinguish between those that did and did not benefit from antibiotics (c-for-benefit 0.50).

CONCLUSIONS: In conclusion, multivariable prediction based on patient demographics and common signs and symptoms did not reliably predict the patient-level probability of cure and antibiotic effect in this IPD-MA. Therefore, these characteristics cannot be expected to reliably distinguish those that do and do not benefit from antibiotics in adults presenting to primary care with ARS.

Original language	English
Article number	16
Journal	Diagnostic and prognostic research
Volume	7
Issue number	1
DOIs	https://doi.org/10.1186/s41512-023-00154-0
Publication status	Published - 5 Sept 2023
Externally published	Yes

Access to Document

10.1186/s41512-023-00154-0

Prognosis-and-prediction-of-antibiotic-benefit-in-adults-with-clinically-diagnosed-acute-rhinosinusitis.pdfFinal published version, 1.34 MBLicence: Taverne

Cite this

Hoogland, J., Takada, T., van Smeden, M., Rovers, M. M., de Sutter, A. I., Merenstein, D., Kaiser, L., Liira, H., Little, P., Bucher, H. C., Moons, K. G. M., Reitsma, J. B., & Venekamp, R. P. (2023). Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis: an individual participant data meta-analysis. Diagnostic and prognostic research, 7(1), Article 16. https://doi.org/10.1186/s41512-023-00154-0

@article{3ec354cd86bd4210bfbcc30520bde92a,

title = "Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis: an individual participant data meta-analysis",

abstract = "BACKGROUND: A previous individual participant data meta-analysis (IPD-MA) of antibiotics for adults with clinically diagnosed acute rhinosinusitis (ARS) showed a marginal overall effect of antibiotics, but was unable to identify patients that are most likely to benefit from antibiotics when applying conventional (i.e. univariable or one-variable-at-a-time) subgroup analysis. We updated the systematic review and investigated whether multivariable prediction of patient-level prognosis and antibiotic treatment effect may lead to more tailored treatment assignment in adults presenting to primary care with ARS.METHODS: An IPD-MA of nine double-blind placebo-controlled trials of antibiotic treatment (n=2539) was conducted, with the probability of being cured at 8-15 days as the primary outcome. A logistic mixed effects model was developed to predict the probability of being cured based on demographic characteristics, signs and symptoms, and antibiotic treatment assignment. Predictive performance was quantified based on internal-external cross-validation in terms of calibration and discrimination performance, overall model fit, and the accuracy of individual predictions.RESULTS: Results indicate that the prognosis with respect to risk of cure could not be reliably predicted (c-statistic 0.58 and Brier score 0.24). Similarly, patient-level treatment effect predictions did not reliably distinguish between those that did and did not benefit from antibiotics (c-for-benefit 0.50).CONCLUSIONS: In conclusion, multivariable prediction based on patient demographics and common signs and symptoms did not reliably predict the patient-level probability of cure and antibiotic effect in this IPD-MA. Therefore, these characteristics cannot be expected to reliably distinguish those that do and do not benefit from antibiotics in adults presenting to primary care with ARS.",

author = "Jeroen Hoogland and Toshihiko Takada and \{van Smeden\}, Maarten and Rovers, \{Maroeska M\} and \{de Sutter\}, \{An I\} and Daniel Merenstein and Laurent Kaiser and Helena Liira and Paul Little and Bucher, \{Heiner C\} and Moons, \{Karel G M\} and Reitsma, \{Johannes B\} and Venekamp, \{Roderick P\}",

note = "{\textcopyright} 2023. BioMed Central Ltd., part of Springer Nature.",

year = "2023",

month = sep,

day = "5",

doi = "10.1186/s41512-023-00154-0",

language = "English",

volume = "7",

journal = "Diagnostic and prognostic research",

issn = "2397-7523",

number = "1",

}

Hoogland, J, Takada, T, van Smeden, M, Rovers, MM, de Sutter, AI, Merenstein, D, Kaiser, L, Liira, H, Little, P, Bucher, HC, Moons, KGM, Reitsma, JB & Venekamp, RP 2023, 'Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis: an individual participant data meta-analysis', Diagnostic and prognostic research, vol. 7, no. 1, 16. https://doi.org/10.1186/s41512-023-00154-0

TY - JOUR

T1 - Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis

T2 - an individual participant data meta-analysis

AU - Hoogland, Jeroen

AU - Takada, Toshihiko

AU - van Smeden, Maarten

AU - Rovers, Maroeska M

AU - de Sutter, An I

AU - Merenstein, Daniel

AU - Kaiser, Laurent

AU - Liira, Helena

AU - Little, Paul

AU - Bucher, Heiner C

AU - Moons, Karel G M

AU - Reitsma, Johannes B

AU - Venekamp, Roderick P

PY - 2023/9/5

Y1 - 2023/9/5

N2 - BACKGROUND: A previous individual participant data meta-analysis (IPD-MA) of antibiotics for adults with clinically diagnosed acute rhinosinusitis (ARS) showed a marginal overall effect of antibiotics, but was unable to identify patients that are most likely to benefit from antibiotics when applying conventional (i.e. univariable or one-variable-at-a-time) subgroup analysis. We updated the systematic review and investigated whether multivariable prediction of patient-level prognosis and antibiotic treatment effect may lead to more tailored treatment assignment in adults presenting to primary care with ARS.METHODS: An IPD-MA of nine double-blind placebo-controlled trials of antibiotic treatment (n=2539) was conducted, with the probability of being cured at 8-15 days as the primary outcome. A logistic mixed effects model was developed to predict the probability of being cured based on demographic characteristics, signs and symptoms, and antibiotic treatment assignment. Predictive performance was quantified based on internal-external cross-validation in terms of calibration and discrimination performance, overall model fit, and the accuracy of individual predictions.RESULTS: Results indicate that the prognosis with respect to risk of cure could not be reliably predicted (c-statistic 0.58 and Brier score 0.24). Similarly, patient-level treatment effect predictions did not reliably distinguish between those that did and did not benefit from antibiotics (c-for-benefit 0.50).CONCLUSIONS: In conclusion, multivariable prediction based on patient demographics and common signs and symptoms did not reliably predict the patient-level probability of cure and antibiotic effect in this IPD-MA. Therefore, these characteristics cannot be expected to reliably distinguish those that do and do not benefit from antibiotics in adults presenting to primary care with ARS.

AB - BACKGROUND: A previous individual participant data meta-analysis (IPD-MA) of antibiotics for adults with clinically diagnosed acute rhinosinusitis (ARS) showed a marginal overall effect of antibiotics, but was unable to identify patients that are most likely to benefit from antibiotics when applying conventional (i.e. univariable or one-variable-at-a-time) subgroup analysis. We updated the systematic review and investigated whether multivariable prediction of patient-level prognosis and antibiotic treatment effect may lead to more tailored treatment assignment in adults presenting to primary care with ARS.METHODS: An IPD-MA of nine double-blind placebo-controlled trials of antibiotic treatment (n=2539) was conducted, with the probability of being cured at 8-15 days as the primary outcome. A logistic mixed effects model was developed to predict the probability of being cured based on demographic characteristics, signs and symptoms, and antibiotic treatment assignment. Predictive performance was quantified based on internal-external cross-validation in terms of calibration and discrimination performance, overall model fit, and the accuracy of individual predictions.RESULTS: Results indicate that the prognosis with respect to risk of cure could not be reliably predicted (c-statistic 0.58 and Brier score 0.24). Similarly, patient-level treatment effect predictions did not reliably distinguish between those that did and did not benefit from antibiotics (c-for-benefit 0.50).CONCLUSIONS: In conclusion, multivariable prediction based on patient demographics and common signs and symptoms did not reliably predict the patient-level probability of cure and antibiotic effect in this IPD-MA. Therefore, these characteristics cannot be expected to reliably distinguish those that do and do not benefit from antibiotics in adults presenting to primary care with ARS.

U2 - 10.1186/s41512-023-00154-0

DO - 10.1186/s41512-023-00154-0

M3 - Article

C2 - 37667327

SN - 2397-7523

VL - 7

JO - Diagnostic and prognostic research

JF - Diagnostic and prognostic research

IS - 1

M1 - 16

ER -

Prognosis and prediction of antibiotic benefit in adults with clinically diagnosed acute rhinosinusitis: an individual participant data meta-analysis

Abstract

Access to Document

Fingerprint

Cite this