Evidence map›Paper›PMID 38800776›Full record

ArticleCureus2024

Retrospective Analysis of Risk Factors in Geriatric Hip Fracture Patients Predictive of Surgical Intensive Care Unit Admission.

Gerardo Trejo, Aiza Zia, Catherine Caronia, Abenamar Arrillaga, John Cuellar, Toyya A Pujol, Heather Reens, Florence LeFevre, Theresa Drucker, Sarah Eckardt and 2 more

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Gerardo TrejoFamily Medicine, Good Samaritan University Hospital, West Islip, USA.
Aiza ZiaTrauma, Good Samaritan University Hospital, West Islip, USA.
Catherine CaroniaPediatrics, Good Samaritan University Hospital, West Islip, USA.
Abenamar ArrillagaTrauma/Surgical Critical Care, Good Samaritan University Hospital, West Islip, USA.
John CuellarOrthopedic Surgery, Good Samaritan University Hospital, West Islip, USA.
Toyya A PujolStatistics, RAND Corporation, Cambridge, USA.
Heather ReensNursing, Molloy University, Rockville Centre, USA.
Florence LeFevreClinical Professional Development, North Shore University Hospital, Manhasset, USA.
Theresa DruckerNursing, Molloy University, Rockville Centre, USA.
Sarah EckardtPerformance Improvement, Huntington Hospital, Northwell Health, Huntington, USA.
Randeep S JawaDivision of Trauma Surgery, Stony Brook Medicine, Stony Brook, USA.
Patricia A EckardtNursing, Good Samaritan University Hospital, West Islip, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAlthough numerous risk factors and prediction models affecting morbidity and mortality in geriatric hip fracture patients have been previously identified, there are scant published data on predictors for perioperative Surgical Intensive Care Unit (SICU) admission in this patient population. Determining if a patient will need an SICU admission would not only allow for the appropriate allocation of resources and personnel but also permit targeted clinical management of these patients with the goal of improving morbidity and mortality outcomes. The purpose of this study was to identify specific risk factors predictive of SICU admission in a population of geriatric hip fracture patients. Unlike previous studies which have investigated predominantly demographic, comorbidity, and laboratory data, the present study also considered a frailty index and length of time from injury to presentation in the Emergency Department (ED).

methodsA total of 501 geriatric hip fracture patients admitted to a Level 1 trauma center were included in this retrospective, single-center, quantitative study from January 1, 2019, to December 31, 2022. Using a logistical regression analysis, more than 25 different variables were included in the regression model to identify values predictive of SICU admission. Predictive models of planned versus unplanned SICU admissions were also estimated. The discriminative ability of variables in the final models to predict SICU admission was assessed with receiver operating characteristic curves' area under the curve estimates.

resultsFrailty, serum lactate > 2, and presentation to the ED > 12 hours after injury were significant predictors of SICU admission overall (P = 0.03, 0.038, and 0.05 respectively). Additionally, the predictive model for planned SICU admission had no common significant predictors with unplanned SICU admission. Planned SICU admission significant predictors included an Injury Severity Score (ISS) of 15 and greater, a higher total serum protein, serum sodium <135, systolic blood pressure (BP) under 100, increased heart rate on admission to ED, thrombocytopenia (<120), and higher Anesthesia Society Association physical status classification (ASA) score (P = 0.007, 0.04, 0.05, 0.002, 0.041, 0.05, and 0.005 respectively). Each SICU prediction model (overall, planned, and unplanned) demonstrated sufficient discriminative ability with the area under the curve (AUC) values of 0.869, 0.601, and 0.866 respectively. Finally, mean hospital Length of Stay (LOS) and mortality were increased in SICU admissions when compared to non-SICU admissions.

conclusionOf the three risk factors predictive of SICU admission identified in this study, two have not been extensively studied previously in this patient population. Frailty has been associated with increased mortality and postoperative complications in hip fracture patients, but this is the first study to date to use a novel frailty index specifically designed and validated for use in hip fracture patients. The other risk factor, time from injury to presentation to the ED serves as an indicator for time a hip fracture patient spent without receiving medical attention. This risk factor has not been investigated heavily in the past as a predictor of SICU admissions in this patient population.

Indexed as

geriatricgeriatric hip fracturehip fracturemorbidity and mortalitysurgical intensive care unit (sicu)

Identifiers

PMID38800776
PMCPMC11121594

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.