The Role of Smoking and Body Mass Index in Mortality Risk Assessment for Geriatric Hip Fracture Patients

Meltzer-Bruhn, Ariana T.; Esper, Garrett W.; Herbosa, Christopher G.; Ganta, Abhishek; Egol, Kenneth A.; Konda, Sanjit R.

doi:10.7759/cureus.26666

Cited by 5 publications

(6 citation statements)

References 27 publications

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“…Patients were then stratified into risk quartiles based on the Score for Trauma Triage in the Geriatric and Middle Aged (STTGMA), a validated inpatient mortality risk assessment tool that has also shown efficacy in cost stratification. [5][6][7][8][9][10]12,13 Comparative analyses were conducted between high-risk quartiles (top 25% of patients based on the respective STTGMA score) for the prepandemic (October 2014 to January 2020) versus pandemic (February 2020 to January 2022) time periods and COVID1 versus COVID2 patients.…”

Section: Methodsmentioning

confidence: 99%

“…Previous work by this study group has investigated the Score for Trauma Triage in the Geriatric and Middle Aged (STTGMA), a validated risk-stratification tool for orthopaedic trauma patients. [4][5][6][7][8][9] Additional work by Konda et al 10 demonstrated the STTGMA tool's ability to adequately identify patients at high risk for poor outcomes, worse hospital quality measures, and higher costs of care. Therefore, this tool provides a platform to effectively identify patients at high risk for increased costs of care associated with their admission.…”

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confidence: 99%

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The Cost We Bear: Financial Implications for Hip Fracture Care Amidst the COVID-19 Pandemic

Konda

Esper

Meltzer-Bruhn

et al. 2023

J Am Acad Orthop Surg

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Introduction:The purpose of this study was to assess the impact of COVID-19 on the cost of hip fracture care in the geriatric/middle-aged cohort, hypothesizing the cost of care increased during the pandemic, especially in COVID1 patients. Methods: Between October 2014 and January 2022, 2,526 hip fracture patients older than 55 years were analyzed for demographics, injury details, COVID status on admission, hospital quality measures, and inpatient healthcare costs from the inpatient admission. Comparative analyses were conducted between: (1) All comers and high-risk patients in the prepandemic (October 2014 to January 2020) and pandemic (February 2020 to January 2022) cohorts and (2) COVID1 and COVID2 patients during the pandemic. Subanalysis assessed the difference in cost breakdown for patients in the overall cohorts, the high-risk quartiles, and between the prevaccine and postvaccine pandemic cohorts. Results: Although the total costs of admission for all patients, and specifically high-risk patients, were not notably higher during the pandemic, further breakdown showed higher costs for the emergency department, laboratory/pathology, radiology, and allied health services during the pandemic, which was offset by lower procedural costs. High-risk COVID1 patients had higher total costs than high-risk COVID2 patients (P , 0.001), most notably in roomand-board (P = 0.032) and allied health (P = 0.023) costs. Once the pandemic started, subgroup analysis demonstrated no change in the total cost in the prevaccine and postvaccine cohort. Conclusion:The overall inpatient cost of hip fracture care did not increase during the pandemic. Although individual subdivisions of cost signified increased resource utilization during the pandemic, this was offset by lower procedural costs. COVID1 patients, however, had notably higher total costs compared with COVID2 patients driven primarily by increased room-and-board costs. The overall cost of care for high-risk patients did not decrease after the widespread administration of the COVID-19 vaccine.

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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

The Cost We Bear: Financial Implications for Hip Fracture Care Amidst the COVID-19 Pandemic

Konda

Esper

Meltzer-Bruhn

et al. 2023

J Am Acad Orthop Surg

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“…Demographic features included age, body mass index, comorbidity profile as compiled by the Charlson comorbidity index (CCI), baseline ambulatory status, osteoporosis diagnosis (as determined by previous dual energy X-ray absorptiometry [DEXA] scan), and smoking status at the time of injury (current vs. not current). Additional recorded clinical data included the respective time from surgery to fixation failure, the Score for Trauma Triage in the Geriatric and Middle Aged (STTGMA), a validated tool for assessment of orthopedic trauma risk, and the Fracture Risk Assessment Tool (FRAX) tool for each patient 13 14 15 16 17) . The STTGMA score utilizes various clinical variables including demographic data (age, comorbidities), injury details (AIS [Abbreviated Injury Scale] for the head/neck and chest), and baseline functional status for determination of a mortality risk score.…”

Section: Methodsmentioning

confidence: 99%

Cut-Through versus Cut-Out: No Easy Way to Predict How Single Lag Screw Design Cephalomedullary Nails Used for Intertrochanteric Hip Fractures Will Fail?

Esper,

Fisher,

Anil

et al. 2023

Hip & Pelvis

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Purpose This study aims to compare patients in whom fixation failure occurred via cut-out (CO) or cut-through (CT) in order to determine patient factors and radiographic parameters that may be predictive of each mechanism. Materials and Methods This retrospective cohort study includes 18 patients with intertrochanteric (IT) hip fractures (AO/OTA classification 31A1.3) who underwent treatment using a single lag screw design intramedullary nail in whom fixation failure occurred within one year. All patients were reviewed for demographics and radiographic parameters including tip-to-apex distance (TAD), posteromedial calcar continuity, neck-shaft angle, lateral wall thickness, and others. Patients were grouped into cohorts based on the mechanism of failure, either lag screw CO or CT, and a comparison was performed. Results No differences in demographics, injury details, fracture classifications, or radiographic parameters were observed between CO/CT cohorts. Of note, a similar rate of post-reduction TAD>25 mm ( P =0.936) was observed between groups. A higher rate of DEXA (dual energy X-ray absorptiometry) confirmed osteoporosis (25.0% vs. 60.0%) was observed in the CT group, but without significance. Conclusion The mechanism of CT failure during intramedullary nail fixation of an IT fracture did not show an association with clinical data including patient demographics, reduction accuracy, or radiographic parameters. As reported in previous biomechanical studies, the main predictive factor for patients in whom early failure might occur via the CT effect mechanism may be related to bone quality; however, conduct of larger studies will be required in order to determine whether there is a difference in bone quality.

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“…As the original STTGMA tool utilized clinical data that is readily available on arrival to the emergency department, this study group continues to research additional accessible variables that may further improve our predictive capacity and refine the tool's efficacy. The original STTGMA tool included a patient's age and injury details, including the Abbreviated Injury Score (AIS) for the head/neck and chest, Glasgow Coma Scale (GCS), and comorbidity profile as defined by the Charlson Comorbidity Index (CCI) before additional research highlighted new variables such as a patient's baseline ambulatory status, American Society of Anesthesiologists (ASA) score, their COVID-19 status on hospital admission, baseline body mass index, smoking status at the time of injury, the season in which they sustain their injury [8][9][10][11][12]. More recently, STTGMA has become more adaptive to a patient's changing risk profile throughout their admission, now accounting for the occurrence of inpatient complications [13].…”

Section: Introductionmentioning

confidence: 99%

Impact of Poorly Controlled Diabetes and Glycosylated Hemoglobin Values in Geriatric Hip Fracture Mortality Risk Assessment

Merrell¹,

Esper²,

Ganta³

et al. 2023

Cureus

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The presence of poorly-controlled diabetes in the setting of geriatric hip fractures has been shown to increase all-cause mortality and worsen outcomes. This study aimed to assess whether the addition of a patient's glycated hemoglobin (A1c) value to a validated geriatric inpatient risk tool improves the predictive capacity of the risk tool. MethodsA cohort of 2430 patients >55 years old treated for low-energy mechanism hip fractures between October 2014 to November 2021 were reviewed for demographics (including diabetes diagnoses and their respective hemoglobin A1c values at the time of admission), injury details, hospital quality measures, and mortality. As past work demonstrated a hemoglobin A1c value above 8% to be the tipping point for worse outcomes, the baseline Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA) tool for inpatient mortality in hip fractures (STTGMAHIP_FX_SCORE -Score for Trauma Triage in the Geriatric and Middle-Aged Hip Fracture Score) was modified to include a patient's hemoglobin A1c using an 8% cutoff (STTGMAHIP_8%A1c -Score for Trauma Triage in the Geriatric and Middle-Aged Hip 8% Hemoglobin A1c Cutoff Score). The new model's predictive ability (as measured by the area under the receiver operating curves (AUROCs)) for inpatient mortality was compared to the baseline tool using DeLong's test. Risk quartiles were generated for the new tool, and comparative analyses were conducted on hospital quality measures and outcomes. ResultsFive hundred and sixty-five patients (23%) were noted to have diabetes mellitus, and 76 patients had an A1c above 8%. Patients with a hemoglobin A1c above 8% had a higher rate of inpatient complications and mortality through one year. The STTGMAHIP_8%A1c score significantly improved the predictive capacity for inpatient mortality compared to STTGMAHIP_FX_SCORE (0.786 vs. 0.672, p=0.0456). Upon analysis of the risk quartiles, the highest risk cohort was found to have a longer length of stay (p<0.001), with higher rates of inpatient (p<0.001) and 30-day mortality (p<0.001) and need for admission to the intensive care unit (p<0.001) as compared to the minimal risk cohort. Patients in the lowest risk quartile were most likely to be discharged home (p<0.001). ConclusionPatients who present with a hemoglobin A1c above 8% experienced significantly worse outcomes than those below 8%. The inclusion of a patient's hemoglobin A1c as a cutoff score improves the STTGMAHIP_FX_SCORE tool to predict mortality and risk stratify patient outcomes. While diabetes presents another medical challenge to manage, providers may utilize this new variable to better highlight at-risk diabetic patients.

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The Role of Smoking and Body Mass Index in Mortality Risk Assessment for Geriatric Hip Fracture Patients

Cited by 5 publications

References 27 publications

The Cost We Bear: Financial Implications for Hip Fracture Care Amidst the COVID-19 Pandemic

The Cost We Bear: Financial Implications for Hip Fracture Care Amidst the COVID-19 Pandemic

Cut-Through versus Cut-Out: No Easy Way to Predict How Single Lag Screw Design Cephalomedullary Nails Used for Intertrochanteric Hip Fractures Will Fail?

Impact of Poorly Controlled Diabetes and Glycosylated Hemoglobin Values in Geriatric Hip Fracture Mortality Risk Assessment

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