Clinical prediction model: Multisystem inflammatory syndrome in children versus Kawasaki disease.

Autor: Starnes LS; Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Hospital Medicine, Nashville, Tennessee, USA., Starnes JR; Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Cardiology, Nashville, Tennessee, USA., Stopczynski T; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA., Amarin JZ; Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Infectious Diseases, Nashville, Tennessee, USA.; Epidemiology Doctoral Program, Vanderbilt University School of Medicine, Tennessee, USA., Charnogursky C; Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Infectious Diseases, Nashville, Tennessee, USA., Hayek H; Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Infectious Diseases, Nashville, Tennessee, USA., Talj R; Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Infectious Diseases, Nashville, Tennessee, USA., Parra DA; Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Cardiology, Nashville, Tennessee, USA., Clark DE; Department of Medicine, School of Medicine, Division of Cardiovascular Medicine, Stanford University, Palo Alto, California, USA., Patrick AE; Department of Pediatrics, Vanderbilt University Medical Center, Division of Rheumatology, Nashville, Tennessee, USA., Katz SE; Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Infectious Diseases, Nashville, Tennessee, USA., Howard LM; Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Infectious Diseases, Nashville, Tennessee, USA., Peetluk L; Department of Medicine, Vanderbilt University Medical Center, Division of Epidemiology, Nashville, Tennessee, USA.; Optum Epidemiology, Massachusetts, Boston, USA., Rankin D; Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Infectious Diseases, Nashville, Tennessee, USA.; Vanderbilt Epidemiology PhD Program, School of Medicine, Vanderbilt University, Nashville, Tennessee, USA., Spieker AJ; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA., Halasa NB; Department of Pediatrics, Vanderbilt University Medical Center, Division of Pediatric Infectious Diseases, Nashville, Tennessee, USA.
Jazyk: angličtina
Zdroj: Journal of hospital medicine [J Hosp Med] 2024 Mar; Vol. 19 (3), pp. 175-184. Date of Electronic Publication: 2024 Jan 28.
DOI: 10.1002/jhm.13290
Abstrakt: Background: Multisystem inflammatory syndrome in children (MIS-C) is a rare but serious complication of severe acute respiratory syndrome coronavirus 2 infection. Features of MIS-C overlap with those of Kawasaki disease (KD).
Objective: The study objective was to develop a prediction model to assist with this diagnostic dilemma.
Methods: Data from a retrospective cohort of children hospitalized with KD before the coronavirus disease 2019 pandemic were compared to a prospective cohort of children hospitalized with MIS-C. A bootstrapped backwards selection process was used to develop a logistic regression model predicting the probability of MIS-C diagnosis. A nomogram was created for application to individual patients.
Results: Compared to children with incomplete and complete KD (N = 602), children with MIS-C (N = 105) were older and had longer hospitalizations; more frequent intensive care unit admissions and vasopressor use; lower white blood cell count, lymphocyte count, erythrocyte sedimentation rate, platelet count, sodium, and alanine aminotransferase; and higher hemoglobin and C-reactive protein (CRP) at admission. Left ventricular dysfunction was more frequent in patients with MIS-C, whereas coronary abnormalities were more common in those with KD. The final prediction model included age, sodium, platelet count, alanine aminotransferase, reduction in left ventricular ejection fraction, and CRP. The model exhibited good discrimination with AUC 0.96 (95% confidence interval: [0.94-0.98]) and was well calibrated (optimism-corrected intercept of -0.020 and slope of 0.99).
Conclusions: A diagnostic prediction model utilizing admission information provides excellent discrimination between MIS-C and KD. This model may be useful for diagnosis of MIS-C but requires external validation.
(© 2024 Society of Hospital Medicine.)
Databáze: MEDLINE
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