Validation of Childhood Pneumonia Prognostic Models for Use in Emergency Care Settings

被引:8
作者
Antoon, James W. [1 ,6 ]
Nian, Hui [2 ]
Ampofo, Krow [3 ]
Zhu, Yuwei [2 ]
Sartori, Laura F. [1 ,4 ]
Johnson, Jakobi [1 ]
Arnold, Donald H. [1 ]
Stassun, Justine [1 ]
Pavia, Andrew T. [3 ]
Grijalva, Carlos G. [5 ]
Williams, Derek J. [1 ]
机构
[1] Vanderbilt Univ, Dept Pediat, Sch Med, Nashville, TN USA
[2] Vanderbilt Univ, Dept Biostat, Sch Med, Nashville, TN USA
[3] Univ Utah, Dept Pediat, Sch Med, Nashville, TN USA
[4] Univ Penn, Dept Pediat, Sch Med, Philadelphia, PA USA
[5] Vanderbilt Univ, Dept Hlth Policy & Biomed Informat, Med Ctr, Nashville, TN USA
[6] 2200 Childrens Way, Nashville, TN 37232 USA
关键词
pediatrics; pneumonia; prognostic model; validation; CLINICAL DECISION-SUPPORT; SEVERITY; RATES; TOOL;
D O I
10.1093/jpids/piad054
中图分类号
R51 [传染病];
学科分类号
100401 ;
摘要
Background Unwarranted variation in disposition decisions exist among children with pneumonia. We validated three prognostic models for predicting pneumonia severity among children in the emergency department (ED) and hospital. Methods We performed a two-center, prospective study of children 6 months to <18 years presenting to the ED with pneumonia from January 2014 to May 2019. We evaluated three previously developed disease-specific prognostic models which use demographic, clinical, and diagnostic predictor variables, with each model estimating risk for Very Severe (mechanical ventilation or shock), Severe (ICU without very severe features), and Moderate/Mild (Hospitalization without severe features or ED discharge) pneumonia. Predictive accuracy was measured using discrimination (concordance or c-statistic) and re-calibration. Results There were 1088 children included in one or more of the three models. Median age was 3.6 years and the majority of children were male (53.7%) and identified as non-Hispanic White (63.7%). The distribution for the ordinal severity outcome was mild or moderate (79.1%), severe (15.9%), and very severe (4.9%). The three models each demonstrated excellent discrimination (C-statistic range across models [0.786-0.803]) with no appreciable degradation in predictive accuracy from the derivation cohort. Conclusions All three prognostic models accurately identified risk for three clinically meaningful levels of pneumonia severity and demonstrated very good predictive performance. Physiologic variables contributed the most to model prediction. Application of these objective tools may help standardize and improve disposition and other management decisions for children with pneumonia.
引用
收藏
页码:451 / 458
页数:8
相关论文
共 25 条
[1]   Validation and Utility Testing of Clinical Prediction Models Time to Change the Approach [J].
Adibi, Amin ;
Sadatsafavi, Mohsen ;
Ioannidis, John P. A. .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2020, 324 (03) :235-236
[2]   Sensitivity of the Pediatric Early Warning Score to Identify Patient Deterioration [J].
Akre, Mari ;
Finkelstein, Marsha ;
Erickson, Mary ;
Liu, Meixia ;
Vanderbilt, Laurel ;
Billman, Glenn .
PEDIATRICS, 2010, 125 (04) :E763-E769
[3]  
[Anonymous], 2019, R: A language and environment for statistical computing
[4]   Variation in Emergency Department Admission Rates in US Children's Hospitals [J].
Bourgeois, Florence T. ;
Monuteaux, Michael C. ;
Stack, Anne M. ;
Neuman, Mark I. .
PEDIATRICS, 2014, 134 (03) :539-545
[5]   Variability in Processes of Care and Outcomes Among Children Hospitalized With Community-acquired Pneumonia [J].
Brogan, Thomas V. ;
Hall, Matthew ;
Williams, Derek J. ;
Neuman, Mark I. ;
Grijalva, Carlos G. ;
Farris, Reid W. D. ;
Shah, Samir S. .
PEDIATRIC INFECTIOUS DISEASE JOURNAL, 2012, 31 (10) :1036-1041
[6]   Safety and efficacy of CURB65-guided antibiotic therapy in community-acquired pneumonia [J].
Chalmers, James D. ;
Singanayagam, Aran ;
Akram, Ahsan R. ;
Choudhury, Gourab ;
Mandal, Pallavi ;
Hill, Adam T. .
JOURNAL OF ANTIMICROBIAL CHEMOTHERAPY, 2011, 66 (02) :416-423
[7]   SMART-COP: A tool for predicting the need for intensive respiratory or vasopressor support in community-acquired pneumonia [J].
Charles, Patrick G. P. ;
Wolfe, Rory ;
Whitby, Michael ;
Fine, Michael J. ;
Fuller, Andrew J. ;
Stirling, Robert ;
Wright, Alistair A. ;
Ramirez, Julio A. ;
Christiansen, Keryn J. ;
Waterer, Grant W. ;
Pierce, Robert J. ;
Armstrong, John G. ;
Korman, Tony M. ;
Holmes, Peter ;
Obrosky, D. Scott ;
Peyrani, Paula ;
Johnson, Barbara ;
Hooy, Michelle ;
Grayson, M. Lindsay .
CLINICAL INFECTIOUS DISEASES, 2008, 47 (03) :375-384
[8]   Use of Traumatic Brain Injury Prediction Rules With Clinical Decision Support\ [J].
Dayan, Peter S. ;
Ballard, Dustin W. ;
Tham, Eric ;
Hoffman, Jeff M. ;
Swietlik, Marguerite ;
Deakyne, Sara J. ;
Alessandrini, Evaline A. ;
Tzimenatos, Leah ;
Bajaj, Lalit ;
Vinson, David R. ;
Mark, Dustin G. ;
Offerman, Steve R. ;
Chettipally, Uli K. ;
Paterno, Marilyn D. ;
Schaeffer, Molly H. ;
Wang, Jun ;
Casper, T. Charles ;
Goldberg, Howard S. ;
Grundmeier, Robert W. ;
Kuppermann, Nathan .
PEDIATRICS, 2017, 139 (04)
[9]   Impact of an Electronic Clinical Decision Support Tool for Emergency Department Patients With Pneumonia [J].
Dean, Nathan C. ;
Jones, Barbara E. ;
Jones, Jason P. ;
Ferraro, Jeffrey P. ;
Post, Herman B. ;
Aronsky, Dominik ;
Vines, Caroline G. ;
Allen, Todd L. ;
Haug, Peter J. .
ANNALS OF EMERGENCY MEDICINE, 2015, 66 (05) :511-520
[10]   A new framework to enhance the interpretation of external validation studies of clinical prediction models [J].
Debray, Thomas P. A. ;
Vergouwe, Yvonne ;
Koffijberg, Hendrik ;
Nieboer, Daan ;
Steyerberg, Ewout W. ;
Moons, Karel G. M. .
JOURNAL OF CLINICAL EPIDEMIOLOGY, 2015, 68 (03) :280-289