Rethinking classification of prematurity: a new clinical algorithm that improves etiologic assignment of preterm births.
BACKGROUND
There is a need for a better etiologic classification of preterm births and for tools to help to determine the possible etiologies of these births.


OBJECTIVE
Having previously developed the Barcelona Etiology of Prematurity (BEP) algorithm, based on a new classification for preterm births, we sought to validate this algorithm in clinical studies whereby doctors retrospectively assigned the etiology of preterm birth according to principal cause and associated causes.


METHODS
In phase 1 of the study, 91 preterm neonates consecutively admitted to a tertiary hospital were etiologically classified by doctors using the BEP algorithm. In phase 2, another 29 cases, representing the full spectrum of standard clinical scenarios, were classified by 20 doctors randomly divided into two groups of 10: one group used the algorithm and the other did not.


RESULTS
In phase 1, the doctors were able to assign the etiology of all 91 clinical cases using the BEP algorithm, showing a 95.6% level of agreement with the etiologies set by the authors. In phase 2, for the 572 total evaluations, the group that used the BEP algorithm had significantly fewer errors in assigning the principal cause of prematurity than the group that did not use the algorithm (4.51 vs. 16.20%, respectively; p < 0.0001), and also demonstrated a higher level of correlation in assigning the associated causes.


CONCLUSIONS
The proposed classification may be used to retrospectively categorize the etiology of preterm births, and the BEP algorithm facilitates this task enabling greater accuracy and precision in clinical data.
Neonatal Unit, Hospital Universitari Sant Joan de Déu, Barcelona, Spain. abalaguer@csc.uic.es
Albert
A
Balaguer
Javier
J
Alvarez-Serra
Marti
M
Iriondo
María Dolores
MD
Gómez-Roig
Xavier
X
Krauel
D000465:Algorithms; D002965:Classification / Q000379:methods*; D005260:Female; D005865:Gestational Age; D006801:Humans; D007231:Infant, Newborn; D007234:Infant, Premature / Q000502:physiology; D008297:Male; D009359:Neonatology / Q000379:methods / Q000706:statistics & numerical data; D011247:Pregnancy; D047928:Premature Birth / Q000145:classification* / Q000175:diagnosis / Q000209:etiology*; D011361:Professional Competence / Q000706:statistics & numerical data; D012189:Retrospective Studies; D013850:Thinking; D013997:Time Factors