Proximal Femur Fractures in the Elderly-A Novel Modality to Predict Mortality: The Neutrophil-to-Lymphocyte Ratio.

comorbidity femur fracture mortality neutrophil-to-lymphocyte ratio

Journal

Journal of clinical medicine
ISSN: 2077-0383
Titre abrégé: J Clin Med
Pays: Switzerland
ID NLM: 101606588

Informations de publication

Date de publication:
06 Jan 2023
Historique:
received: 24 11 2022
revised: 28 12 2022
accepted: 03 01 2023
entrez: 21 1 2023
pubmed: 22 1 2023
medline: 22 1 2023
Statut: epublish

Résumé

Background: The assessment and identification of elderly patients with proximal femur fractures (PFF) who are at high risk of postoperative mortality may influence the treatment decision-making process. The purpose of this study was to determine whether the neutrophil-to-lymphocyte ratio (NLR) could be used to predict postoperative mortality in the elderly population. Methods: A four-year retrospective cohort study of electronic medical records was conducted at a single tertiary care hospital between 2015 and 2018. Data from 1551 patients aged 65 years and older who underwent surgical treatment for PFF were collected and analyzed. The data included complete blood counts at admission, demographic information, underlying illnesses, type of surgery, and postoperative mortality and complications during the first year of follow-up. A survival analysis model was utilized. Results: The mean age was 90.76 ± 1.88 years, 1066 (68.7%) women. Forty-four (2.8%) patients experienced postoperative infection. A higher NLR0 was independently associated with higher all-cause mortality rates in patients who underwent surgical treatment for PFF (p = 0.041). Moreover, the mean NLR0 value was higher when the death occurred earlier after surgery (p < 0.001). Conclusions: When combined with other clinical and laboratory findings, NLR0 levels may serve as a potentially valuable, inexpensive, and reliable prognostic biomarker to improve risk stratification for elderly patients who are candidates for PFF surgery. Furthermore, with additional research, we could potentially develop a treatment algorithm to identify patients at high risk of postoperative mortality.

Identifiants

pubmed: 36675385
pii: jcm12020456
doi: 10.3390/jcm12020456
pmc: PMC9865272
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Omer Marom (O)

Orthopedic Department, Meir Medical Center, Kfar Saba, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv 4428164, Israel.

Inbar Paz (I)

Orthopedic Department, Meir Medical Center, Kfar Saba, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv 4428164, Israel.

David Segal (D)

Orthopedic Department, Meir Medical Center, Kfar Saba, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv 4428164, Israel.

Guy Topaz (G)

Orthopedic Department, Meir Medical Center, Kfar Saba, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv 4428164, Israel.

Nitzan Abelson (N)

Orthopedic Department, Meir Medical Center, Kfar Saba, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv 4428164, Israel.

Alex Tavdi (A)

Orthopedic Department, Meir Medical Center, Kfar Saba, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv 4428164, Israel.

Refael Behrbalk (R)

Orthopedic Department, Meir Medical Center, Kfar Saba, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv 4428164, Israel.

Ezequiel Palmanovich (E)

Orthopedic Department, Meir Medical Center, Kfar Saba, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv 4428164, Israel.

Nissim Ohana (N)

Orthopedic Department, Meir Medical Center, Kfar Saba, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv 4428164, Israel.

Eyal Yaacobi (E)

Orthopedic Department, Meir Medical Center, Kfar Saba, Affiliated to Sackler Faculty of Medicine, Tel-Aviv University, Tel Aviv 4428164, Israel.

Classifications MeSH