Rhabdomyosarcoma is the most common soft‐tissue sarcoma in children and adolescents and accounts for 3% of all pediatric tumors. Subtypes include alveolar, spindle cell, embryonal, mixed‐type, pleomorphic, and rhabdomyosarcoma with ganglionic differentiation. The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database was queried for patients diagnosed with any type of rhabdomyosarcoma between 1973 and 2014. Patient demographics, tumor characteristics, and incidence were studied with χ2 analysis. Survival was modeled with Kaplan–Meier survival curves and Cox proportional hazards models were used to assess the effect of age and gender on survival. Pleomorphic subtype had higher grade and larger sized tumors compared to other subtypes (p < 0.05). Pleomorphic and alveolar rhabdomyosarcoma had the worst overall survival with a 26.6% and 28.9% 5‐year survival, respectively. Embryonal rhabdomyosarcoma had the highest 5‐year survival rate (73.9%). Tumor size was negatively correlated with survival months, indicating patients with larger tumors had shorter survival times (p < 0.05). Presence of higher‐grade tumors and metastatic disease at presentation were negatively correlated with survival months (p < 0.05). No significant differences in the survival were found between gender or race between all of the subtypes (p > 0.05). This study highlights key differences in the demographic and survival rates of the different types of rhabdomyosarcoma that can be used for more tailored patient counseling. We also demonstrate that large, population‐level databases provide sufficient data that can be used in the analysis of rare tumors. © 2019 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 37:2226–2230, 2019

Level III, prognostic study.

BackgroundChronic heart failure (CHF), which affects >5 million Americans, accounts for >1 million hospitalizations annually. As a part of the Hospital Readmission Reduction Program, the Affordable Care Act requires that the Centers for Medicare and Medicaid Services reduce payments to hospitals with excess readmissions. This study sought to develop a scale that reliably predicts readmission rates among patients with CHF.MethodsThe State Inpatient Database (2006–2011) was utilized, and discharge data including demographic and clinical characteristics on 642,448 patients with CHF from California and New York (derivation cohort) and 365,359 patients with CHF from Florida and Washington (validation cohort) were extracted. The Readmission After Heart Failure (RAHF) scale was developed to predict readmission risk.ResultsThe 30-day readmission rates were 9.42 and 9.17% (derivation and validation cohorts, respectively). Age <65 years, male gender, first income quartile, African American race, race other than African American or Caucasian, Medicare, Medicaid, self-pay/no insurance, drug abuse, renal failure, chronic pulmonary disorder, diabetes, depression, and fluid and electrolyte disorder were associated with higher readmission risk after hospitalization for CHF. The RAHF scale was created and explained the 95% of readmission variability within the validation cohort. The RAHF scale was then used to define the following three levels of risk for readmission: low (RAHF score <12; 7.58% readmission rate), moderate (RAHF score 12–15; 9.78% readmission rate), and high (RAHF score >15; 12.04% readmission rate). The relative risk of readmission was 1.67 for the high-risk group compared with the low-risk group.ConclusionThe RAHF scale reliably predicts a patient’s 30-day CHF readmission risk based on demographic and clinical factors present upon initial admission. By risk-stratifying patients, using models such as the RAHF scale, strategies tailored to each patient can be implemented to improve patient outcomes and reduce health care costs.

BackgroundCOPD affects over 13 million Americans, and accounts for over half a million hospitalizations annually. The Hospital Readmission Reduction Program, established by the Affordable Care Act requires the Centers for Medicare and Medicaid Services to reduce payments to hospitals with excess readmissions for COPD as of 2015. This study sought to develop a predictive readmission scale to identify COPD patients at higher readmission risk.MethodsDemographic and clinical data on 339,389 patients from New York and California (derivation cohort) and 258,113 patients from Washington and Florida (validation cohort) were abstracted from the State Inpatient Database (2006–2011), and the Readmission After COPD Exacerbation (RACE) Scale was developed to predict 30-day readmission risk.ResultsThirty-day COPD readmission rates were 7.54% for the derivation cohort and 6.70% for the validation cohort. Factors including age 40–65 years (odds ratio [OR] 1.17; 95% CI, 1.12–1.21), male gender (OR 1.16; 95% CI, 1.13–1.19), African American (OR 1.11; 95% CI, 1.06–1.16), 1st income quartile (OR 1.10; 95% CI, 1.06–1.15), 2nd income quartile (OR 1.06; 95% CI, 1.02–1.10), Medicaid insured (OR 1.83; 95% CI, 1.73–1.93), Medicare insured (OR 1.45; 95% CI, 1.38–1.52), anemia (OR 1.05; 95% CI, 1.02–1.09), congestive heart failure (OR 1.06; 95% CI, 1.02–1.09), depression (OR 1.18; 95% CI, 1.14–1.23), drug abuse (OR 1.17; 95% CI, 1.09–1.25), and psychoses (OR 1.19; 95% CI, 1.13–1.25) were independently associated with increased readmission rates, P<0.01. When the devised RACE scale was applied to both cohorts together, it explained 92.3% of readmission variability.ConclusionThe RACE Scale reliably predicts an individual patient’s 30-day COPD readmission risk based on specific factors present at initial admission. By identifying these patients at high risk of readmission with the RACE Scale, patient-specific readmission-reduction strategies can be implemented to improve patient care as well as reduce readmissions and health care expenditures.

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Data derived from patients in the New York and California State Inpatient Database were reliably able to explain readmission variability for patients in the Florida and Washington State Inpatient Database at a rate of 89.1% based on known preoperative risk factors. Risk-stratification models, such as the Readmission After Total Hip Replacement Risk Scale, can identify high-risk patients for readmission and permit implementation of patient-specific readmission-reduction strategies to reduce readmissions and health care expenditures.

Background: Total knee arthroplasty (TKA) is one of the most common orthopaedic procedures performed in the U.S. The purpose of this study was to develop and verify a scale to preoperatively stratify a patient’s risk of being readmitted to the hospital following a TKA. Methods: Discharge data on 433,638 patients from New York and California (derivation cohort) and 269,934 patients from Florida and Washington (validation cohort) who underwent TKA were collected from the State Inpatient Database, a part of the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality (2006 to 2011). Demographic and clinical characteristics of patients were abstracted and analyzed to develop the Readmission After Total Knee Arthroplasty (RATKA) Scale. Results: Overall 30-day readmission rates in the derivation and validation cohorts were 5.11% and 4.98%, respectively. The following factors were significantly associated with increased 30-day readmission rates in the derivation cohort: age of 41 to 50 years (odds ratio [OR] = 1.13), age of 71 to 80 years (OR = 1.21), age of 81 to 90 years (OR = 1.70), male sex (OR = 1.19), African-American race (OR = 1.37), “other” race/ethnicity (OR = 1.08), Medicaid payer (OR = 1.43), Medicare payer (OR = 1.27), anemia (OR = 1.19), chronic obstructive pulmonary disease (OR = 1.29), coagulopathy (OR = 1.22), congestive heart failure (OR = 1.64), diabetes (OR = 1.19), fluid and electrolyte disorder (OR = 1.25), hypertension (OR = 1.10), liver disease (OR = 1.27), renal failure (OR = 1.33), and rheumatoid arthritis (OR = 1.14). These factors were used to create the RATKA Scale. The RATKA score was then used to define 3 levels of risk for readmission: low (RATKA score of <13; 3.7% readmission rate), moderate (RATKA score of 13 to 16; 5.4% readmission rate), and high (RATKA score of >16; 7.6% readmission rate). The relative risk of readmission was 2.06 for the high-risk group compared with the low-risk group. Conclusions: The RATKA Scale derived from patient data from the derivation cohort was reliably able to explain readmission variability after TKA for patients in the validation cohort at a rate of >95%. Models such as the RATKA Scale will enable identification of the risk of readmission following TKA based on a patient’s risk profile prior to surgery. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

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