The Coming Hip and Femur Fracture Bundle: A New Inpatient Risk Stratification Tool for Care Providers

Introduction: In response to increasing health-care costs, Centers for Medicare & Medicaid Services has initiated several programs to transition from a fee-for-service model to a value-based care model. One such voluntary program is Bundled Payments for Care Improvement Advanced (BPCI Advanced) which includes all hip and femur fractures that undergo operative fixation. The purpose of this study was to analyze the current cost and resource utilization of operatively fixed (nonarthroplasty) hip and femur fracture procedure bundle patients at a single level 1 trauma center within the framework of a risk stratification tool (Score for Trauma Triage in the Geriatric and Middle-Aged [STTGMA]) to identify areas of high utilization before our hospitals transition to bundle period. Materials and Methods: A cohort of Medicare-eligible patients discharged with the Diagnosis-Related Group (DRG) codes 480 to 482 (hip and femur fractures requiring surgical fixation) from a level 1 trauma center between October 2014 and September 2016 was evaluated and assigned a trauma triage risk score (STTGMA score). Patients were stratified into groups based on these scores to create a minimal-, low-, moderate-, and high-risk cohort. Length of stay (LOS), discharge location, need for Intensive Care Unit (ICU)/Step Down Unit (SDU) care, inpatient complications, readmission within 90 days, and inpatient admission costs were recorded. Results: One hundred seventy-three patients with a mean age of 81.5 (10.1) years met inclusion criteria. The mean LOS was 8.0 (4.2) days, with high-risk patients having 4 days greater LOS than lower risk patients. The mean number of total complications was 0.9 (0.8) with a significant difference between risk groups ( P = .029). The mean total cost of admission for the entire cohort of patients was US$25,446 (US$9725), with a nearly US$9000 greater cost for high-risk patients compared to the low-risk patients. High-cost areas of care included room/board, procedure, and radiology. Discussion: High-risk patients were more likely to have longer and more costly admissions with average index admission costs nearly US$9000 more than the lower risk patient cohorts. These high-risk patients were also more likely to develop inpatient complications and require higher levels of care. Conclusion: This analysis of a 2-year cohort of patients who would qualify for the BPCI Advanced hip and femur procedure bundle demonstrates that the STTGMA tool can be used to identify high-risk patients who fall outside the bundle

Admitting Service Affects Cost and Length of Stay of Hip Fracture Patients

Introduction: The purpose of this study was to analyze the effect of the admitting service on cost of care for hip fracture patients by comparing the cost difference between patients admitted to the medicine service versus those admitted to a surgical service. Methods: A 2-year cohort of patients 55 years or older who were admitted to a single level 1 trauma center with an operative hip fracture were included. Patient demographics, comorbidities, admitting service, complications, and hospital length of stay were recorded for each patient. Cost of hospitalization, discharge disposition, and 30-day readmissions were collected. Patients who were admitted to the medicine service (medicine cohort) were compared to those admitted to a surgery service (surgery cohort). Multivariate regression models controlling for age, Charlson comorbidity index (CCI), and American Society of Anesthesiology (ASA) scores were used to evaluate hospitalization costs with a P value of <.05 as significant. Results: Two hundred twenty-five hip fracture patients were included; 143 (63.6%) patients were admitted to a surgical service, while 82 (36.4%) were admitted to the medicine service. Patients admitted to medicine service had greater CCI and ASA scores, longer lengths of stay, and more complications than those patients admitted to surgery service. Linear regression model controlling for age, CCI, ASA score, and time to surgery demonstrates that patients admitted to a surgical service will have 2.0-day (95% confidence interval [CI]: 0.561-3.503; P = .007) shorter admissions with a US$4215 reduction in cost (95$314-US$8116; P = .034) compared to patients admitted to the medicine service. Discussions: In our urban safety net hospital, hip fracture patients admitted to medicine service had longer lengths of stay and higher total hospitalization costs than patients who were admitted to surgery service. Conclusions: This study highlights that the admitting service should be an area of focus for hospitals when developing programs to provide effective and cost-conscious care to hip fracture patients

How Does Frailty Factor Into Mortality Risk Assessment of a Middle-Aged and Geriatric Trauma Population?

Introduction: Frailty in elderly trauma populations has been correlated with an increased risk of morbidity and mortality. The Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA) is a validated mortality risk score that evaluates 4 major physiologic criteria: age, comorbidities, vital signs, and anatomic injuries. The aim of this study was to investigate whether the addition of additional frailty variables to the STTGMA tool would improve risk stratification of a middle-aged and elderly trauma population. Methods: A total of 1486 patients aged 55 years and older who met the American College of Surgeons Tier 1 to 3 criteria and/or who had orthopedic or neurosurgical traumatic consultations in the emergency department between September 2014 and September 2016 were included. The STTGMA ORIGINAL and STTGMA FRAILTY scores were calculated. Additional “frailty variables” included preinjury assistive device use (disability), independent ambulatory status (functional independence), and albumin level (nutrition). The ability of the STTGMA ORIGINAL and the STTGMA FRAILTY models to predict inpatient mortality was compared using area under the receiver operating characteristic curves (AUROCs). Results: There were 23 high-energy inpatient mortalities (4.7%) and 20 low-energy inpatient mortalities (2.0%). When the STTGMA ORIGINAL model was used, the AUROC in the high-energy and low-energy cohorts was 0.926 and 0.896, respectively. The AUROC for STTGMA FRAILTY for the high-energy and low-energy cohorts was 0.905 and 0.937, respectively. There was no significant difference in predictive capacity for inpatient mortality between STTGMA ORIGINAL and STTGMA FRAILTY for both the high-energy and low-energy cohorts. Conclusion: The original STTGMA tool accounts for important frailty factors including cognition and general health status. These variables combined with other major physiologic variables such as age and anatomic injuries appear to be sufficient to adequately and accurately quantify inpatient mortality risk. The addition of other common frailty factors that account for does not enhance the STTGMA tool’s predictive capabilities

Does Use of Oral Anticoagulants at the Time of Admission Affect Outcomes Following Hip Fracture

Purpose: The purpose of this study was to compare hospital quality outcomes in patients over the age of 60 undergoing fixation of hip fracture based on their anticoagulation status. Materials and Methods: Patients aged 60 and older with isolated hip fracture injuries treated operatively at 1 academic medical center between October 2014 and September 2016 were analyzed. Patients on the following medications were included in the anticoagulation cohort: warfarin, clopidogrel, aspirin 325 mg, rivaroxaban, apixaban, dabigatran, and dipyridamole/aspirin. We compared outcome measures including time to surgery, length of stay (LOS), transfusion rate, blood loss, procedure time, complication rate, need for intensive care unit (ICU)/step-down unit (SDU) care, discharge disposition, and cost of admission. Outcomes were controlled for age, Charlson comorbidity index (CCI), and anesthesia type. Results: A total of 479 hip fracture patients met the inclusion criteria, with 367 (76.6%) patients in the nonanticoagulated cohort and 112 (23.4%) patients in the anticoagulated cohort. The mean LOS and time to surgery were longer in the anticoagulated cohort (8.3 vs 7.3 days, P = .033 and 1.9 vs 1.6 days, P = .010); however, after controlling for age, CCI, and anesthesia type, these differences were no longer significant. Surgical outcomes were equivalent with similar procedure times, blood loss, and need for transfusion. The mean number of complications developed and inpatient mortality rate in the 2 cohorts were similar; however, more patients in the anticoagulated cohort required ICU/SDU-level care (odds ratio = 2.364, P = .001, controlled for age, CCI, and anesthesia). There was increased utilization of post-acute care in the anticoagulated cohort, with only 10.7% of patients discharged home compared to 19.9% of the nonanticoagulated group ( P = .026). Lastly, there was no difference in cost of care. Conclusion: This study highlights that anticoagulation status alone does not independently put patients at increased risk with respect to LOS, surgical outcomes, and cost of hospitalization