Quantifying Clinicians' Diagnostic Uncertainty When Making Initial Treatment Decisions for Microbial Keratitis
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved..
PURPOSE: There is a need to understand physicians' diagnostic uncertainty in the initial management of microbial keratitis (MK). This study aimed to understand corneal specialists' diagnostic uncertainty by establishing risk thresholds for treatment of MK that could be used to inform a decision curve analysis for prediction modeling.
METHODS: A cross-sectional survey of corneal specialists with at least 2 years clinical experience was conducted. Clinicians provided the percentage risk at which they would always or never treat MK types (bacterial, fungal, herpetic, and amoebic) based on initial ulcer sizes and locations (<2 mm 2 central, <2 mm 2 peripheral, and >8 mm 2 central).
RESULTS: Seventy-two of 99 ophthalmologists participated who were 50% female with an average of 14.7 (SD = 10.1) years of experience, 60% in academic practices, and 38% outside the United States. Clinicians reported they would "never" and "always" treat a <2 mm 2 central MK infection if the median risk was 0% and 20% for bacterial (interquartile range, IQR = 0-5 and 5-50), 4.5% and 27.5% for herpetic (IQR = 0-10 and 10-50), 5% and 50% for fungal (IQR = 0-10 and 20-75), and 5% and 50.5% for amoebic (IQR = 0-20 and 32-80), respectively. Mixed-effects models showed lower thresholds to treat larger and central infections ( P < 0.001, respectively), and thresholds to always treat differed between MK types for the United States ( P < 0.001) but not international clinicians.
CONCLUSIONS: Risk thresholds to treat differed by practice locations and MK types, location, and size. Researchers can use these thresholds to understand when a clinician is uncertain and to create decision support tools to guide clinicians' treatment decisions.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2023 |
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Erschienen: |
2023 |
Enthalten in: |
Zur Gesamtaufnahme - volume:42 |
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Enthalten in: |
Cornea - 42(2023), 11 vom: 01. Nov., Seite 1408-1413 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Hicks, Patrice M [VerfasserIn] |
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Anmerkungen: |
Date Completed 20.12.2023 Date Revised 20.03.2024 published: Print-Electronic Citation Status MEDLINE |
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doi: |
10.1097/ICO.0000000000003159 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM347712843 |
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245 | 1 | 0 | |a Quantifying Clinicians' Diagnostic Uncertainty When Making Initial Treatment Decisions for Microbial Keratitis |
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520 | |a Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved. | ||
520 | |a PURPOSE: There is a need to understand physicians' diagnostic uncertainty in the initial management of microbial keratitis (MK). This study aimed to understand corneal specialists' diagnostic uncertainty by establishing risk thresholds for treatment of MK that could be used to inform a decision curve analysis for prediction modeling | ||
520 | |a METHODS: A cross-sectional survey of corneal specialists with at least 2 years clinical experience was conducted. Clinicians provided the percentage risk at which they would always or never treat MK types (bacterial, fungal, herpetic, and amoebic) based on initial ulcer sizes and locations (<2 mm 2 central, <2 mm 2 peripheral, and >8 mm 2 central) | ||
520 | |a RESULTS: Seventy-two of 99 ophthalmologists participated who were 50% female with an average of 14.7 (SD = 10.1) years of experience, 60% in academic practices, and 38% outside the United States. Clinicians reported they would "never" and "always" treat a <2 mm 2 central MK infection if the median risk was 0% and 20% for bacterial (interquartile range, IQR = 0-5 and 5-50), 4.5% and 27.5% for herpetic (IQR = 0-10 and 10-50), 5% and 50% for fungal (IQR = 0-10 and 20-75), and 5% and 50.5% for amoebic (IQR = 0-20 and 32-80), respectively. Mixed-effects models showed lower thresholds to treat larger and central infections ( P < 0.001, respectively), and thresholds to always treat differed between MK types for the United States ( P < 0.001) but not international clinicians | ||
520 | |a CONCLUSIONS: Risk thresholds to treat differed by practice locations and MK types, location, and size. Researchers can use these thresholds to understand when a clinician is uncertain and to create decision support tools to guide clinicians' treatment decisions | ||
650 | 4 | |a Journal Article | |
700 | 1 | |a Singh, Karandeep |e verfasserin |4 aut | |
700 | 1 | |a Prajna, N Venkatesh |e verfasserin |4 aut | |
700 | 1 | |a Lu, Ming-Chen |e verfasserin |4 aut | |
700 | 1 | |a Niziol, Leslie M |e verfasserin |4 aut | |
700 | 1 | |a Greenwald, Miles F |e verfasserin |4 aut | |
700 | 1 | |a Verkade, Angela |e verfasserin |4 aut | |
700 | 1 | |a Amescua, Guillermo |e verfasserin |4 aut | |
700 | 1 | |a Farsiu, Sina |e verfasserin |4 aut | |
700 | 1 | |a Woodward, Maria A |e verfasserin |4 aut | |
700 | 0 | |a Corneal Ulcer Study Group |e verfasserin |4 aut | |
700 | 1 | |a Amescua, Guillermo |e investigator |4 oth | |
700 | 1 | |a Ahmed, Masih |e investigator |4 oth | |
700 | 1 | |a Al-Mohtaseb, Zaina |e investigator |4 oth | |
700 | 1 | |a Alvarez-Melloni, Diana |e investigator |4 oth | |
700 | 1 | |a Amin, Sejal |e investigator |4 oth | |
700 | 1 | |a Ayalew, Menen |e investigator |4 oth | |
700 | 1 | |a Balasubramanian, Ashwin |e investigator |4 oth | |
700 | 1 | |a Chamberlain, Winston |e investigator |4 oth | |
700 | 1 | |a Chan, Matilda |e investigator |4 oth | |
700 | 1 | |a Chan, Elsie |e investigator |4 oth | |
700 | 1 | |a Chaudhary, Meenu |e investigator |4 oth | |
700 | 1 | |a Chia, Thomas |e investigator |4 oth | |
700 | 1 | |a Chodosh, James |e investigator |4 oth | |
700 | 1 | |a Christy, Josephine |e investigator |4 oth | |
700 | 1 | |a Clements, John |e investigator |4 oth | |
700 | 1 | |a Dart, John |e investigator |4 oth | |
700 | 1 | |a Dastjerdi, Mohammad |e investigator |4 oth | |
700 | 1 | |a Denny, Matthew |e investigator |4 oth | |
700 | 1 | |a Elghobaier, Mohamed |e investigator |4 oth | |
700 | 1 | |a Estopinal, Chris |e investigator |4 oth | |
700 | 1 | |a Gandhi, Preethika |e investigator |4 oth | |
700 | 1 | |a Greenwald, Miles F |e investigator |4 oth | |
700 | 1 | |a Gokhale, Nikhil |e investigator |4 oth | |
700 | 1 | |a Hernandez, Natalie |e investigator |4 oth | |
700 | 1 | |a Hovakimyan, Anna |e investigator |4 oth | |
700 | 1 | |a Hwang, Frank |e investigator |4 oth | |
700 | 1 | |a Hwang, David |e investigator |4 oth | |
700 | 1 | |a Jaeschke, Tomas |e investigator |4 oth | |
700 | 1 | |a Jhanji, Vishal |e investigator |4 oth | |
700 | 1 | |a Karas, Faris |e investigator |4 oth | |
700 | 1 | |a Karp, Carol |e investigator |4 oth | |
700 | 1 | |a Kattana, Lakshmi |e investigator |4 oth | |
700 | 1 | |a Keenan, Jeremy |e investigator |4 oth | |
700 | 1 | |a Khandelwal, Sumitra |e investigator |4 oth | |
700 | 1 | |a Kim, Tyson |e investigator |4 oth | |
700 | 1 | |a Koo, Ellen |e investigator |4 oth | |
700 | 1 | |a Koreishi, Aaleya |e investigator |4 oth | |
700 | 1 | |a Li, Jennifer |e investigator |4 oth | |
700 | 1 | |a Lietman, Tom |e investigator |4 oth | |
700 | 1 | |a Macsai, Marian |e investigator |4 oth | |
700 | 1 | |a Martinez, Jaime |e investigator |4 oth | |
700 | 1 | |a JodMehta |e investigator |4 oth | |
700 | 1 | |a Mimouni, Michael |e investigator |4 oth | |
700 | 1 | |a Moss, Adam |e investigator |4 oth | |
700 | 1 | |a Nanji, Afshan |e investigator |4 oth | |
700 | 1 | |a Nataneli, Nathan |e investigator |4 oth | |
700 | 1 | |a Nussbaumer, Jennifer |e investigator |4 oth | |
700 | 1 | |a Panday, Vasudha |e investigator |4 oth | |
700 | 1 | |a Pflugfelder, Stephen |e investigator |4 oth | |
700 | 1 | |a Pradhan, Sayali |e investigator |4 oth | |
700 | 1 | |a Prajna, N Venkatesh |e investigator |4 oth | |
700 | 1 | |a Rao, Naveen |e investigator |4 oth | |
700 | 1 | |a Redd, Travis |e investigator |4 oth | |
700 | 1 | |a Reddy, Satya |e investigator |4 oth | |
700 | 1 | |a Sabater, Alfonso |e investigator |4 oth | |
700 | 1 | |a Schallhorn, Julie |e investigator |4 oth | |
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700 | 1 | |a Tuli, Sonal |e investigator |4 oth | |
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700 | 1 | |a Van Dooren, Bart |e investigator |4 oth | |
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700 | 1 | |a Wozniak, Rachel |e investigator |4 oth | |
700 | 1 | |a YeanYaw, Choong |e investigator |4 oth | |
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