Automated Electronic Frailty Index-Identified Frailty Status and Associated Postsurgical Adverse Events.


Journal

JAMA network open
ISSN: 2574-3805
Titre abrégé: JAMA Netw Open
Pays: United States
ID NLM: 101729235

Informations de publication

Date de publication:
01 Nov 2023
Historique:
medline: 7 11 2023
pubmed: 6 11 2023
entrez: 6 11 2023
Statut: epublish

Résumé

Electronic frailty index (eFI) is an automated electronic health record (EHR)-based tool that uses a combination of clinical encounters, diagnosis codes, laboratory workups, medications, and Medicare annual wellness visit data as markers of frailty status. The association of eFI with postanesthesia adverse outcomes has not been evaluated. To examine the association of frailty, calculated as eFI at the time of the surgical procedure and categorized as fit, prefrail, or frail, with adverse events after elective noncardiac surgery. This cohort study was conducted at a tertiary care academic medical center in Winston-Salem, North Carolina. The cohort included patients 55 years or older who underwent noncardiac surgery of at least 1 hour in duration between October 1, 2017, and June 30, 2021. Frailty calculated by the eFI tool. Preoperative eFI scores were calculated based on available data 1 day prior to the procedure and categorized as fit (eFI score: ≤0.10), prefrail (eFI score: >0.10 to ≤0.21), or frail (eFI score: >0.21). The primary outcome was a composite of the following 8 adverse component events: 90-item Patient Safety Indicators (PSI 90) score, hospital-acquired conditions, in-hospital mortality, 30-day mortality, 30-day readmission, 30-day emergency department visit after surgery, transfer to a skilled nursing facility after surgery, or unexpected intensive care unit admission after surgery. Secondary outcomes were each of the component events of the composite. Of the 33 449 patients (median [IQR] age, 67 [61-74] years; 17 618 females [52.7%]) included, 11 563 (34.6%) were classified as fit, 15 928 (47.6%) as prefrail, and 5958 (17.8%) as frail. Using logistic regression models that were adjusted for age, sex, race and ethnicity, and comorbidity burden, patients with prefrail (odds ratio [OR], 1.24; 95% CI, 1.18-1.30; P < .001) and frail (OR, 1.71; 95% CI, 1.58-1.82; P < .001) statuses were more likely to experience postoperative adverse events compared with patients with a fit status. Subsequent adjustment for all other potential confounders or covariates did not alter this association. For every increase in eFI of 0.03 units, the odds of a composite of postoperative adverse events increased by 1.06 (95% CI, 1.03-1.13; P < .001). This cohort study found that frailty, as measured by an automatically calculated index integrated within the EHR, was associated with increased risk of adverse events after noncardiac surgery. Deployment of eFI tools may support screening and possible risk modification, especially in patients who undergo high-risk surgery.

Identifiants

pubmed: 37930697
pii: 2811392
doi: 10.1001/jamanetworkopen.2023.41915
pmc: PMC10628731
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2341915

Références

Eur J Intern Med. 2016 Jun;31:3-10
pubmed: 27039014
JAMA Surg. 2020 Jan 1;155(1):e194620
pubmed: 31721994
Am Surg. 2022 Aug 16;:31348221121547
pubmed: 35971786
Ann Vasc Surg. 2016 Aug;35:19-29
pubmed: 27263810
J Gerontol A Biol Sci Med Sci. 2020 Sep 25;75(10):1928-1934
pubmed: 32274501
Can J Anaesth. 2015 Feb;62(2):143-57
pubmed: 25420470
J Gerontol A Biol Sci Med Sci. 2019 Oct 4;74(11):1771-1777
pubmed: 30668637
JAMA Surg. 2022 May 1;157(5):e220172
pubmed: 35293969
J Clin Monit Comput. 2018 Oct;32(5):945-951
pubmed: 29214598
J Am Geriatr Soc. 2019 Aug;67(8):1559-1564
pubmed: 31045254
JAMA Surg. 2017 Feb 1;152(2):175-182
pubmed: 27893030
Am J Med. 2023 Apr;136(4):372-379.e5
pubmed: 36657557
Age Ageing. 2019 May 1;48(3):388-394
pubmed: 30778528
J Geriatr Oncol. 2021 Jun;12(5):851-854
pubmed: 33622653
Health Rep. 2013 Sep;24(9):10-7
pubmed: 24258362
J Gerontol A Biol Sci Med Sci. 2015 Nov;70(11):1427-34
pubmed: 26297656
BMJ. 2007 Oct 20;335(7624):806-8
pubmed: 17947786
N Engl J Med. 2018 Jun 28;378(26):2456-2458
pubmed: 29949490
BMC Anesthesiol. 2019 Nov 7;19(1):204
pubmed: 31699033
PLoS One. 2014 Nov 21;9(11):e113677
pubmed: 25415265
J Am Geriatr Soc. 2021 May;69(5):1357-1362
pubmed: 33469933
Ann Surg. 2021 Dec 1;274(6):e1230-e1237
pubmed: 32118596
Prev Chronic Dis. 2016 Sep 15;13:E128
pubmed: 27634778
Anesthesiology. 2020 Jan;132(1):82-94
pubmed: 31834870
Clin Geriatr Med. 2018 Feb;34(1):25-38
pubmed: 29129215
Lancet. 2018 May 5;391(10132):1775-1782
pubmed: 29706364
J Am Coll Surg. 2019 Apr;228(4):482-490
pubmed: 30885474

Auteurs

Ashish K Khanna (AK)

Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, North Carolina.
Outcomes Research Consortium, Cleveland, Ohio.

Vida Motamedi (V)

Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
Outcomes Research Consortium, Cleveland, Ohio.
Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee.

Bethany Bouldin (B)

Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
Outcomes Research Consortium, Cleveland, Ohio.

Timothy Harwood (T)

Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.

Nicholas M Pajewski (NM)

Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina.

Amit K Saha (AK)

Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, North Carolina.

Scott Segal (S)

Department of Anesthesiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
Perioperative Outcomes and Informatics Collaborative (POIC), Winston-Salem, North Carolina.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH