Prognostic prediction models for clinical outcomes in patients diagnosed with visceral leishmaniasis : protocol for a systematic review
© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ..
INTRODUCTION: Visceral leishmaniasis (VL) is a neglected tropical disease responsible for many thousands of preventable deaths each year. Symptomatic patients often struggle to access effective treatment, without which death is the norm. Risk prediction tools support clinical teams and policymakers in identifying high-risk patients who could benefit from more intensive management pathways. Investigators interested in using their clinical data for prognostic research should first identify currently available models that are candidates for validation and possible updating. Addressing these needs, we aim to identify, summarise and appraise the available models predicting clinical outcomes in VL patients.
METHODS AND ANALYSIS: We will include studies that have developed, validated or updated prognostic models predicting future clinical outcomes in patients diagnosed with VL. Systematic reviews and meta-analyses that include eligible studies are also considered for review. Conference abstracts and educational theses are excluded. Data extraction, appraisal and reporting will follow current methodological guidelines. Ovid Embase; Ovid MEDLINE; the Web of Science Core Collection, SciELO and LILACS are searched from database inception to 1 March 2023 using terms developed for the identification of prediction models, and with no language restriction. Screening, data extraction and risk of bias assessment will be performed in duplicate with discordance resolved by a third independent reviewer. Risk of bias will be assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Tables and figures will compare and contrast key model information, including source data, participants, model development and performance measures, and risk of bias. We will consider the strengths, limitations and clinical applicability of the identified models.
ETHICS AND DISSEMINATION: Ethics approval is not required for this review. The systematic review and all accompanying data will be submitted to an open-access journal. Findings will also be disseminated through the research group's website (www.iddo.org/research-themes/visceral-leishmaniasis) and social media channels.
PROSPERO REGISTRATION NUMBER: CRD42023417226.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
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| Enthalten in: |
BMJ open - 13(2023), 10 vom: 24. Okt., Seite e075597 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Wilson, James [VerfasserIn] |
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| Links: |
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| Themen: |
INFECTIOUS DISEASES |
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| Anmerkungen: |
Date Completed 27.10.2023 Date Revised 10.02.2024 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1136/bmjopen-2023-075597 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ. | ||
| 520 | |a INTRODUCTION: Visceral leishmaniasis (VL) is a neglected tropical disease responsible for many thousands of preventable deaths each year. Symptomatic patients often struggle to access effective treatment, without which death is the norm. Risk prediction tools support clinical teams and policymakers in identifying high-risk patients who could benefit from more intensive management pathways. Investigators interested in using their clinical data for prognostic research should first identify currently available models that are candidates for validation and possible updating. Addressing these needs, we aim to identify, summarise and appraise the available models predicting clinical outcomes in VL patients | ||
| 520 | |a METHODS AND ANALYSIS: We will include studies that have developed, validated or updated prognostic models predicting future clinical outcomes in patients diagnosed with VL. Systematic reviews and meta-analyses that include eligible studies are also considered for review. Conference abstracts and educational theses are excluded. Data extraction, appraisal and reporting will follow current methodological guidelines. Ovid Embase; Ovid MEDLINE; the Web of Science Core Collection, SciELO and LILACS are searched from database inception to 1 March 2023 using terms developed for the identification of prediction models, and with no language restriction. Screening, data extraction and risk of bias assessment will be performed in duplicate with discordance resolved by a third independent reviewer. Risk of bias will be assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Tables and figures will compare and contrast key model information, including source data, participants, model development and performance measures, and risk of bias. We will consider the strengths, limitations and clinical applicability of the identified models | ||
| 520 | |a ETHICS AND DISSEMINATION: Ethics approval is not required for this review. The systematic review and all accompanying data will be submitted to an open-access journal. Findings will also be disseminated through the research group's website (www.iddo.org/research-themes/visceral-leishmaniasis) and social media channels | ||
| 520 | |a PROSPERO REGISTRATION NUMBER: CRD42023417226 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a INFECTIOUS DISEASES | |
| 650 | 4 | |a Neglected Diseases | |
| 650 | 4 | |a Prognosis | |
| 650 | 4 | |a Protocols & guidelines | |
| 650 | 4 | |a STATISTICS & RESEARCH METHODS | |
| 650 | 4 | |a Systematic Review | |
| 700 | 1 | |a Chowdhury, Forhad |e verfasserin |4 aut | |
| 700 | 1 | |a Hassan, Shermarke |e verfasserin |4 aut | |
| 700 | 1 | |a Harriss, Elinor K |e verfasserin |4 aut | |
| 700 | 1 | |a Alves, Fabiana |e verfasserin |4 aut | |
| 700 | 1 | |a Dahal, Prabin |e verfasserin |4 aut | |
| 700 | 1 | |a Stepniewska, Kasia |e verfasserin |4 aut | |
| 700 | 1 | |a Guérin, Philippe J |e verfasserin |4 aut | |
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