PMID- 27213544
OWN - NLM
STAT- MEDLINE
DCOM- 20170502
LR  - 20181202
IS  - 1537-1948 (Electronic)
IS  - 0025-7079 (Linking)
VI  - 54
IP  - 11
DP  - 2016 Nov
TI  - Predicting Patients at Risk for 3-Day Postdischarge Readmissions, ED Visits, and 
      Deaths.
PG  - 1017-1023
AB  - BACKGROUND: Transitional care interventions can be utilized to reduce 
      post-hospital discharge adverse events (AEs). However, no methodology exists to 
      effectively identify high-risk patients of any disease across multiple hospital 
      sites and patient populations for short-term postdischarge AEs. OBJECTIVES: To 
      develop and validate a 3-day (72 h) AEs prediction model using electronic health 
      records data available at the time of an indexed discharge. RESEARCH DESIGN: 
      Retrospective cohort study of admissions between June 2012 and June 2014. 
      SUBJECTS: All adult inpatient admissions (excluding in-hospital deaths) from a 
      large multicenter hospital system. MEASURES: All-cause 3-day unplanned 
      readmissions, emergency department (ED) visits, and deaths (REDD). The REDD model 
      was developed using clinical, administrative, and socioeconomic data, with data 
      preprocessing steps and stacked classification. Patients were divided randomly 
      into training (66.7%), and testing (33.3%) cohorts to avoid overfitting. RESULTS: 
      The derivation cohort comprised of 64,252 admissions, of which 2782 (4.3%) 
      admissions resulted in 3-day AEs and 13,372 (20.8%) in 30-day AEs. The 
      c-statistic (also known as area under the receiver operating characteristic 
      curve) of 3-day REDD model was 0.671 and 0.664 for the derivation and validation 
      cohort, respectively. The c-statistic of 30-day REDD model was 0.713 and 0.711 
      for the derivation and validation cohort, respectively. CONCLUSIONS: The 3-day 
      REDD model predicts high-risk patients with fair discriminative power. The 
      discriminative power of the 30-day REDD model is also better than the previously 
      reported models under similar settings. The 3-day REDD model has been implemented 
      and is being used to identify patients at risk for AEs.
FAU - Agrawal, Deepak
AU  - Agrawal D
AD  - *Department of Industrial and Manufacturing Engineering, The Pennsylvania State 
      University, University Park daggerGeisinger Health System, Danville, PA.
FAU - Chen, Cheng-Bang
AU  - Chen CB
FAU - Dravenstott, Ronald W
AU  - Dravenstott RW
FAU - Stromblad, Christopher T B
AU  - Stromblad CT
FAU - Schmid, John Andrew
AU  - Schmid JA
FAU - Darer, Jonathan D
AU  - Darer JD
FAU - Devapriya, Priyantha
AU  - Devapriya P
FAU - Kumara, Soundar
AU  - Kumara S
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Med Care
JT  - Medical care
JID - 0230027
SB  - IM
MH  - Emergency Service, Hospital/*statistics & numerical data
MH  - Female
MH  - Humans
MH  - Length of Stay/statistics & numerical data
MH  - Male
MH  - Middle Aged
MH  - Models, Statistical
MH  - *Mortality
MH  - Patient Discharge/statistics & numerical data
MH  - Patient Readmission/*statistics & numerical data
MH  - Pennsylvania/epidemiology
MH  - Retrospective Studies
MH  - Risk Factors
MH  - Socioeconomic Factors
EDAT- 2016/05/24 06:00
MHDA- 2017/05/04 06:00
CRDT- 2016/05/24 06:00
PHST- 2016/05/24 06:00 [pubmed]
PHST- 2017/05/04 06:00 [medline]
PHST- 2016/05/24 06:00 [entrez]
AID - 10.1097/MLR.0000000000000574 [doi]
PST - ppublish
SO  - Med Care. 2016 Nov;54(11):1017-1023. doi: 10.1097/MLR.0000000000000574.