PMID- 35188942
OWN - NLM
STAT- MEDLINE
DCOM- 20220223
LR  - 20230928
IS  - 1549-8425 (Electronic)
IS  - 1549-8417 (Linking)
VI  - 18
IP  - 2
DP  - 2022 Mar 1
TI  - Neonatal Adverse Events' Trigger Tool Setup With Random Forest.
PG  - e585-e590
LID - 10.1097/PTS.0000000000000871 [doi]
AB  - OBJECTIVE: This study aimed to develop a trigger tool for detection of neonatal 
      adverse events (AEs) and to validate its effectiveness. STUDY DESIGN: Random 
      forest (RF) algorithm was used to build the predictive model by analyzing data 
      from the medical records of 782 neonates in our previous study. Thirteen 
      variables for each patient were used to predict neonatal AEs. Next, the critical 
      variables were selected based on recursive elimination of variables to form the 
      list of triggers. Then, a trigger tool with those triggers was established and 
      tested by reviewing medical records. The positive predictive value of individual 
      triggers and of the entire tool was evaluated. RESULTS: Data from 782 neonates, 
      including 297 patients with and 485 patients without AEs, were collected to build 
      the original RF model. Then, the 6 most important variables, including diarrhea, 
      antibiotic use, fever, death, skin damage, and suspected necrotizing 
      enterocolitis, were selected to establish a neonate-focused trigger tool. The 
      forest with the 6 variables predicted AEs with a sensitivity of 70.7%, a 
      specificity of 92.0%, and an error rate of 16.1%. In a validation study of the 
      trigger tool, 655 neonates with birth weights >/=1500 g were enrolled, and review 
      of their medical records revealed 1709 triggers and 1172 unique AEs. The 3 most 
      common AEs identified were skin damage, iatrogenic diarrhea, and environmental 
      factor-related fever. The total positive predictive value of the trigger tool was 
      0.686. CONCLUSIONS: The neonate-focused trigger tool developed using the RF 
      algorithm efficiently and reliably identifies AEs among hospitalized neonates 
      with birth weights >/=1500 g.
CI  - Copyright (c) 2021 Wolters Kluwer Health, Inc. All rights reserved.
FAU - Feng, Kun
AU  - Feng K
FAU - Zhang, Li
AU  - Zhang L
AD  - From the Department of Neonatology, Ministry of Education Key Laboratory of Child 
      Development and Disorders, National Clinical Research Center for Child Health and 
      Disorders (Chongqing), China International Science and Technology Cooperation 
      base of Child Development and Critical Disorders, Children's Hospital of 
      Chongqing Medical University.
FAU - He, Huayun
AU  - He H
AD  - From the Department of Neonatology, Ministry of Education Key Laboratory of Child 
      Development and Disorders, National Clinical Research Center for Child Health and 
      Disorders (Chongqing), China International Science and Technology Cooperation 
      base of Child Development and Critical Disorders, Children's Hospital of 
      Chongqing Medical University.
FAU - You, Xueqin
AU  - You X
FAU - Zhang, Qiannan
AU  - Zhang Q
AD  - From the Department of Neonatology, Ministry of Education Key Laboratory of Child 
      Development and Disorders, National Clinical Research Center for Child Health and 
      Disorders (Chongqing), China International Science and Technology Cooperation 
      base of Child Development and Critical Disorders, Children's Hospital of 
      Chongqing Medical University.
FAU - Wei, Hong
AU  - Wei H
AD  - From the Department of Neonatology, Ministry of Education Key Laboratory of Child 
      Development and Disorders, National Clinical Research Center for Child Health and 
      Disorders (Chongqing), China International Science and Technology Cooperation 
      base of Child Development and Critical Disorders, Children's Hospital of 
      Chongqing Medical University.
FAU - Hua, Ziyu
AU  - Hua Z
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - J Patient Saf
JT  - Journal of patient safety
JID - 101233393
SB  - IM
MH  - Humans
MH  - Infant, Newborn
MH  - *Medical Errors
MH  - Medical Records
MH  - *Patient Safety
MH  - Retrospective Studies
COIS- The authors disclose no conflict of interest.
EDAT- 2022/02/22 06:00
MHDA- 2022/02/24 06:00
CRDT- 2022/02/21 17:09
PHST- 2022/02/21 17:09 [entrez]
PHST- 2022/02/22 06:00 [pubmed]
PHST- 2022/02/24 06:00 [medline]
AID - 01209203-202203000-00041 [pii]
AID - 10.1097/PTS.0000000000000871 [doi]
PST - ppublish
SO  - J Patient Saf. 2022 Mar 1;18(2):e585-e590. doi: 10.1097/PTS.0000000000000871.