RT Book, Section
A1 Liu, Vincent
A2 Schmidt, Gregory A.
A2 Kress, John P.
A2 Douglas, Ivor S.
SR Print(0)
ID 1201799249
T1 Severity Scoring and Risk Prediction
T2 Hall, Schmidt and Wood’s Principles of Critical Care, 5<sup>th</sup> Edition
YR 2023
FD 2023
PB McGraw Hill
PP New York, NY
SN 9781264264353
LK accesssurgery.mhmedical.com/content.aspx?aid=1201799249
RD 2024/09/10
AB Over  the  last  five  decades,  critical  care  researchers  and  clinicians  have  amassed  a  great  body  of  pathophysiologic  and  clinical  knowledge  that  has  advanced  the  care  of  critically  ill  patients  through  the  use  of  data.  These  data  have  informed  the  development  of  severity-of-illness  scores  designed  to  risk  stratify  the  outcomes  of  critically  ill  patients.The  most  well-known  severity-of-illness  scores  are  the  Acute  Physiology  and  Chronic  Health  Evaluation  (APACHE),  the  Mortality  Probability  Models  (MPM),  Simplified  Acute  Physiology  Score  (SAPS),  and  Sequential  Organ  Failure  Assessment  (SOFA).  Most  of  these  have  been  updated  over  time  and  evaluated  in  diverse  populations.The  primary  uses  of  these  scores  have  been  for  clinical  investigation  (to  compare  severity  of  illness  between  patient  cohorts)  and  ICU  performance  assessment  (to  compare  process  and  outcome  metrics  over  time  or  between  health  care  settings).An  emerging  frontier  in  the  care  of  critically  ill  patients  seeks  to  use  real-time  risk  prediction  tools  to  inform  bedside  clinical  decisions  that  can  mitigate  further  patient  deterioration,  identify  patients  at  risk  for  organ  failure,  and  inform  longer-term  outcomes  following  intensive  care.The  use  of  innovative  statistical,  machine  learning,  and  artificial  intelligence  algorithms  that  leverage  detailed  data  from  electronic  health  records  and  physiologic  monitors  is  expected  to  continue  to  produce  significant  advances  in  clinicians’  capabilities  to  risk  stratify,  phenotype,  and  intervene  in  critical  illness.However,  a  number  of  key  challenges  related  to  risk  prediction  accuracy  and  bias;  clinician  training  and  workflow  integration;  and  patient  engagement  remain  largely  unsolved.  Thus,  as  of  today,  only  a  paucity  of  these  predictive  models  have  produced  robust  evidence  of  benefit  in  critical  care  outcomes.