Literature DB >> 31010417

Correction to: Postoperative delirium in critically ill surgical patients: incidence, risk factors, and predictive scores.

Onuma Chaiwat1,2, Mellada Chanidnuan3, Worapat Pancharoen3, Kittiya Vijitmala4, Praniti Danpornprasert4, Puriwat Toadithep3, Chayanan Thanakiattiwibun5.   

Abstract

Following publication of the original article [1], the authors reported a missing data on Table 1 in their paper. The original article [1] has been updated.

Entities:  

Year:  2019        PMID: 31010417      PMCID: PMC6477709          DOI: 10.1186/s12871-019-0732-8

Source DB:  PubMed          Journal:  BMC Anesthesiol        ISSN: 1471-2253            Impact factor:   2.217


Correction to: BMC Anesthesiol (2019) 19:39 https://doi.org/10.1186/s12871-019-0694-x Following publication of the original article [1], the authors reported a missing data on Table 1 in their paper. The original article [1] has been updated.
Table 1

Baseline characteristics of delirious and non-delirious patients

VariablesDelirium (n = 61)No delirium (n = 189)P-value
Demographic data
 Age (years)72.7 ± 11.461.4 ± 16.8< 0.001
  ≥ 6055 (90.2%)114 (60.3%)< 0.001
 Gender
  Male31 (50.8%)90 (47.6%)0.768
 Comorbidities
  Hypertension50 (81.9%)105 (55.6%)< 0.001
  DM26 (42.6%)37 (19.6%)0.001
  Cardiac disease21 (34.4%)43 (22.8%)0.091
  Previous stroke15 (24.6%)18 (9.5%)0.004
  Modified IQCODE score ≥ 3.4210 (16.39%)6 (3.2%)0.001
  ESRD or CKD stage 4–510 (16.4%)24 (12.7%)0.520
  Cirrhosis3 (4.9%)9 (4.8%)1.000
 Smoking history pack year41.9 ± 27.124.4 ± 21.60.155
  ≥ 30 pack year10 (16.4%)20 (10.6%)0.259
 Current alcohol consumption6 (9.8%)11 (5.8%)0.378
 Coma17 (27.9%)16 (8.5%)< 0.001
Intraoperative data
 Emergency Surgery34 (55.74%)74 (39.2%)0.026
 Vascular surgery20 (32.8%)32 (16.9%)0.011
 Non-vascular surgery
  Intra-abdominal23 (37.7%)65 (34.4%)0.646
  Orthopedic3 (4.9%)26 (13.8%)0.068
  Gynecological3 (4.9%)23 (12.2%)0.147
  Other12 (19.7%)43 (22.8%)0.723
 Operation time193.5 ± 162.6234.8 ± 178.90.111
 Intraoperative blood loss (mL)250 (60–700)400 (100–1400)0.079
 Intraoperative PRC transfusion (mL)264 (0–663)0 (0–1023)0.865
 Hypoxia3 (4.9%)7 (3.7%)0.710
 Intraoperative hypotension50 (82.0%)146 (77.3%)0.480
ICU admission
 Hemoglobin (mg/dL)10.5 ± 2.510.9 ± 2.10.156
 Blood sugar at admission (mg/dL)155.6 ± 42.1152.2 ± 46.30.611
 Serum albumin (mg/dL)2.7 ± 0.62.8 ± 0.70.168
 BUN/Cr ratio > 2022 (36.1%)48 (25.4%)0.139
 Sepsis24 (39.3%)37 (19.6%)0.003
 APACHE II score12.1 ± 4.88.3 ± 3.9< 0.001
 SOFA score5.8 ± 3.43.4 ± 2.7< 0.001
 Mechanical ventilation54 (88.5%)131 (69.2%)0.002
Medication
 Preoperative-Benzodiazepine8 (13.1%)10 (5.3%)0.049
 Inraoperative-Benzodiazepine32 (53.3%)93 (47.7%)0.658
 Benzodiazepine (Pre and Postoperative)27 (44.3%)36 (19.1%)< 0.001
 Preoperative-Opioid5 (8.2%)18 (9.5%)1.000
 Intraoperative-Opioid58 (96.7%)176 (94.1%)0.740
 Opioid (Pre and Postoperative)59 (96.7%)184 (97.4%)1.000
 Postoperative-Propofol use20 (32.8)31 (16.4%)0.010

ESRD End stage renal disease, CKD Chronic kidney disease, DM Diabetes mellitus, Modified IQCODE score Modified informant questionnaire on cognitive decline in the elderly score, BUN Blood urea nitrogen, Cr Creatinine, APACHE II score Acute physiology and chronic health evaluation II score, SOFA score Sequential organ failure assessment score

Data presented as mean ± SD or median (IQR) or N (%)

The correct Table 1 is shown below. Baseline characteristics of delirious and non-delirious patients ESRD End stage renal disease, CKD Chronic kidney disease, DM Diabetes mellitus, Modified IQCODE score Modified informant questionnaire on cognitive decline in the elderly score, BUN Blood urea nitrogen, Cr Creatinine, APACHE II score Acute physiology and chronic health evaluation II score, SOFA score Sequential organ failure assessment score Data presented as mean ± SD or median (IQR) or N (%)
  1 in total

1.  Postoperative delirium in critically ill surgical patients: incidence, risk factors, and predictive scores.

Authors:  Onuma Chaiwat; Mellada Chanidnuan; Worapat Pancharoen; Kittiya Vijitmala; Praniti Danpornprasert; Puriwat Toadithep; Chayanan Thanakiattiwibun
Journal:  BMC Anesthesiol       Date:  2019-03-20       Impact factor: 2.217

  1 in total
  1 in total

1.  Predicting brain function status changes in critically ill patients via Machine learning.

Authors:  Chao Yan; Cheng Gao; Ziqi Zhang; Wencong Chen; Bradley A Malin; E Wesley Ely; Mayur B Patel; You Chen
Journal:  J Am Med Inform Assoc       Date:  2021-10-12       Impact factor: 7.942

  1 in total

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