We investigate the factors that influence the variance in hospital charges and inpatient care for kidney transplant cases in the US. Using the AHRQ\'s (Agency for Healthcare Research and Quality) HCUP\'s (Hospital Cost and Utilization Project) NIS (National Inpatient Sample) database, we find that variance in hospital charges and inpatient care is driven by patient demographics and hospital variables. We find that variance in hospital charges and inpatient care is determined by patient-specific factors including age, gender, race, and income, and hospital factors such as size, type, and location. Our results provide a deeper understanding of the non-clinical factors that impact hospital charges and inpatient care for kidney transplant patients.

Given recent congressional interest in codifying price transparency regulations, it is important to understand the extent to which newly available price transparency data capture true underlying procedure-level prices. To that end, we compared the prices for maternity services negotiated between a large payer and 26 hospitals in Mississippi across 2 separate price transparency data sources: payer and hospital. The degree of file overlap is low, with only 16.3% of hospital-billing code observations appearing in both data sources. However, for the observations that overlap, pricing concordance is high: Corresponding prices have a correlation coefficient of 0.975, 77.4% match to the penny, and 84.4% are within 10%. Exact price matching rates are greater than 90% for 3 of the 4 service lines included in this study. Taken together, these results suggest that although administrative misalignment exists between payers and hospitals, there is a measure of signal amid the price transparency noise.

UNASSIGNED: Pulmonary hypertension (PH) is a condition where the blood pressure increases in the pulmonary arteries, leading to reduced oxygen delivery to the body\'s tissues due to increased blood flow resistance. This condition can result in right ventricular hypertrophy, low cardiac output, and ischemia. In this study, the authors aim to investigate the impact of group II PH (GIIPH) on patients with congestive heart failure who were admitted with ST elevation myocardial infarction (STEMI) through a retrospective cohort study.
UNASSIGNED: Using the National Inpatient Sample (NIS) database from 2017 to 2020, a retrospective cross-sectional study of adult patients with a principal diagnosis of STEMI with a secondary diagnosis with or without GIIPH according to ICD-10 (International Classification of Disease, 10th edition) codes. Several demographics, including age, race, and gender, were analyzed. The primary endpoint was mortality, while the secondary endpoints included cardiogenic shock, mechanical intubation, length of stay in days, and patient charge in dollars. Multivariate logistic regression model analysis was used to adjust for confounders, with a P value less than 0.05 considered statistically significant.
UNASSIGNED: The study included 26,925 patients admitted with a STEMI, 95 of whom had GIIPH. The mean age for patients with and without PH was 66.6 and 67.5 years, respectively. In the PH group, 37% were females compared to 34% in the non-PH group. The in-hospital mortality rate was higher in the PH group (31.6% vs. 9.6%, P<0.001, adjusted odds ratio (aOR) =3.33, P=0.02). The rates and adjusted odds of cardiogenic shock and mechanical ventilation were higher in the PH groups (aOR =1.15 and 2.14, respectively) but not statistically significant. Patients with PH had a longer length of stay and a higher total charge.
UNASSIGNED: GIIPH was associated with worse clinical and economic outcomes in heart failure patients admitted with STEMI.

BACKGROUND: Graft-versus-host disease (GVHD) is a recognized complication among individuals undergoing bone marrow transplantation (BMT). There is a requirement for supplementary data regarding the in-patient outcomes of GVHD in individuals who have undergone BMT. Our analysis seeks to assess the healthcare burden and outcomes associated with GVHD in hospitalized patients who have undergone BMT.
METHODS: In this retrospective study, we used data from the National Inpatient Sample (NIS) database spanning from 2016 to 2019. Utilizing ICD-10 codes, we distinguished hospitalizations related to BMT and grouped them into two categories: those with GVHD and those without GVHD. Our areas of focus included in-hospital mortality, length of stay, charges, and associations related to GVHD. Unadjusted odds ratios/coefficients were computed through univariable analysis, followed by adjusted odds ratios (aORs)/coefficients from multivariable analysis that considered potential confounding factors.
RESULTS: From 2016 to 2019, data were collected from 13,999 hospitalizations with bone marrow transplants. Among them, 836 had GVHD cases. Patient characteristics showed slight differences in mean age and demographics between the two groups, with GVHD patients having a mean age of 51.61 years and higher percentages of males and whites. Analyzing outcomes, patients with GVHD experienced significantly longer hospital stays (41.4 days vs. 21.3 days) and higher total hospital charges ($824,058 vs. $335,765). Adjusting for confounding factors, GVHD posed a substantial risk. The aOR for mortality in GVHD hospitalizations was 7.20 (95% CI: 5.54-9.36, p < .001). The coefficient for the length of stay was 19.36 days (95% CI: 17.29-21.42, p < .001), and the coefficient for total hospital charges was $453,733 (95% CI: $396,577 to $510,889, p < .001) in GVHD cases. Furthermore, GVHD in patients was associated with elevated risks of various medical conditions. The aORs for sepsis, pneumonia, acute respiratory failure, intubation and mechanical ventilation, Clostridium difficile infection, and acute kidney injury (AKI) in GVHD patients were 2.79 (95% CI: 2.28-3.41, p < .001), 3.30 (95% CI: 2.57-4.24, p < .001), 5.10 (95% CI: 4.01-6.49, p < .001), 4.88 (95% CI: 3.75-6.34, p < .001), 1.45 (95% CI: 1.13-1.86, p = .003), and 3.57 (95% CI: 2.97-4.29, p < .001).
CONCLUSIONS: GVHD in individuals undergoing BMT is linked to elevated mortality rates, prolonged hospitalization, and higher healthcare costs. Moreover, they face a significantly increased risk of developing complications, such as sepsis, pneumonia, acute respiratory failure, C. difficile infection, and AKI. These results underscore the critical need for vigilant monitoring and effective GVHD management to improve patient outcomes and reduce the complications associated with BMT. Nevertheless, further prospective studies are essential to obtain a more profound understanding and a comprehensive assessment of outcomes in these hospitalized patients.

Trauma activation fees are intended to help trauma centers cover the costs of providing lifesaving care at all times, but they have fallen under greater scrutiny because of a lack of regulation and wide variability in charges. We leveraged the federal Hospital Price Transparency rule to systematically describe trauma activation fees as captured in the Turquoise Health database for all Level I-III trauma centers nationally and across payer types. As of April 18, 2023, a total of 38 percent of US trauma centers published trauma activation fees. These fees varied widely by payer type. The minimum fee charged was $40 (for a Medicaid contract); the maximum fees charged were $28,356 (self-pay) and $28,893 (commercial payers). Trauma centers that were larger, metropolitan, located in the West, and associated with proprietary (investor-owned, for-profit) hospitals had higher trauma activation fees. Proprietary hospitals posted fees that were 60 percent higher than those published by public, nonfederal hospitals. Unmerited variation in trauma activation fees may suggest that the current funding strategy is equitable neither for trauma centers nor for the severely injured patients who rely on them for lifesaving care.

Background: Heart failure, a major public health concern, significantly contributes to hospital admissions. This study evaluates the impact of malnutrition on both patient and hospital outcomes in heart failure admissions, with a specific focus on variations in outcomes based on the severity of malnutrition. Methods: Utilizing the National Inpatient Sample (NIS) database, this retrospective cohort study included adult patients admitted with a principal diagnosis of heart failure. Malnutrition was identified using the well-validated ICD 10 codes. We compared outcomes between patients with and without malnutrition, focusing on mortality, length of stay (LOS), hospital charges, cardiac arrest, and cardiogenic shock. Results: Out of 1,110,085 heart failure patients, 36,522 (3.29%) were malnourished. Malnourished patients exhibited significantly higher adjusted in-hospital mortality rates (aOR 3.32; 95% CI 3.03-3.64), longer LOS (mean increase of 4.67 days; p < 0.001), and higher hospital charges (mean increase of USD 77,416.9; p < 0.01). Increased rates of cardiac arrest (aOR 2.39; 95% CI 1.99-2.86; p < 0.001) and cardiogenic shock (aOR 3.74; 95% CI 3.40-4.12; p < 0.001) were also noted in malnourished patients. Severely malnourished patients faced worse outcomes compared to those with mild to moderate malnutrition. Conclusions: Heart failure patients with malnutrition experience higher mortality rates, longer hospital stays, increased hospitalization charges, and greater complication rates, including cardiac arrest and cardiogenic shock, compared to non-malnourished patients. Outcomes deteriorate with the increasing severity of malnutrition. Timely and individualized nutritional interventions may significantly improve outcomes for heart failure admissions.

We use this study to analyze the trends in in-hospital length of stay, total hospital charges, and mortality among adult patients with a primary diagnosis of adult-onset still\'s disease (AOSD). We used the 2016-2019 National Inpatient Sample (NIS) database to conduct a retrospective study on adult AOSD patients (≥18 years old). We analyzed data on baseline patient and hospital characteristics and determined trends in in-hospital mortality, length of stay (LOS), and total hospital charges (TOTCHG). Univariate and multivariate linear and logistic regression analyses were performed to identify factors that independently affected these outcomes. Among the 1615 AOSD hospitalizations, the mean LOS was 7.34 days and the mean TOTCHG was 68,415.31 USD. Macrophage activating syndrome (MAS), disseminated intravascular coagulation (DIC), and a large hospital size were shown to statistically increase the LOS and TOTCHG, while a Native American background was shown to statistically decrease both. The mean in-hospital mortality was 0.929%, with age being the only independent predictor. Our findings reveal an increase in the economic burden of AOSD hospitalizations despite declining admissions and mortality rates. Complications, like MAS and DIC, were found to significantly contribute to this burden despite treatment advancements. Our study indicates the importance of investigating new strategies to prevent these complications.

OBJECTIVE: To investigate the association of diabetes with postoperative outcomes in patients undergoing primary total hip arthroplasty (THA).
METHODS: Retrospective cohort study using data from the US National Inpatient Sample (NIS).
METHODS: Study cohort was hospitalisations for primary THA in the USA, identified from the 2016-2020 NIS.
METHODS: We identified 2 467 215 adults in the 2016-2020 NIS who underwent primary THA using International Classification of Diseases, 10th Revision codes. Primary THA hospitlizations were analysed as the overall group and also stratified by the underlying primary diagnosis for THA.
METHODS: Outcome measures of interest were the length of hospital stay>the median, total hospital charges>the median, inpatient mortality, non-routine discharge, need for blood transfusion, prosthetic fracture, prosthetic dislocation and postprocedural infection, including periprosthetic joint infection, deep surgical site infection and postprocedural sepsis.
RESULTS: Among 2 467 215 patients who underwent primary THA, the mean age was 68.7 years, 58.3% were female, 85.7% were white, 61.7% had Medicare payer and 20.4% had a Deyo-Charlson index (adjusted to exclude diabetes mellitus) of 2 or higher. 416 850 (17%) patients had diabetes. In multivariable-adjusted logistic regression in the overall cohort, diabetes was associated with higher odds of a longer hospital stay (adjusted OR (aOR) 1.38; 95% CI 1.35 to 1.41), higher total charges (aOR 1.11; 95% CI 1.09 to 1.13), non-routine discharge (aOR 1.18; 95% CI 1.15 to 1.20), the need for blood transfusion (aOR 1.19; 95% CI 1.15 to 1.23), postprocedural infection (aOR 1.62; 95% CI 1.10 to 2.40) and periprosthetic joint infection (aOR 1.91; 95% CI 1.12 to 3.24). We noted a lack of some associations in the avascular necrosis and inflammatory arthritis cohorts (p>0.05).
CONCLUSIONS: Diabetes was associated with increased healthcare utilisation, blood transfusion and postprocedural infection risk following primary THA. Optimisation of diabetes with preoperative medical management and/or institution of specific postoperative pathways may improve these outcomes. Larger studies are needed in avascular necrosis and inflammatory arthritis cohorts undergoing primary THA.

BACKGROUND: Sleeve gastrectomy has become a gold standard in addressing medically refractory obesity. Robotic platforms are becoming more utilized, however, data on its cost-effectiveness compared to laparoscopy remain controversial (1-3). At NYU Langone Health, many of the bariatric surgeons adopted robotic surgery as part of their practices starting in 2021. We present a retrospective cost analysis of laparoscopic sleeve gastrectomy (LSG) vs. robotic sleeve gastrectomy (RSG) at New York University (NYU) Langone Health campuses.
METHODS: All adult patients ages 18-65 who underwent LSG or RSG from 202 to 2023 at NYU Langone Health campuses (Manhattan, Long Island, and Brooklyn) were evaluated via electronic medical records and MBSAQIP 30-day follow-up data. Patients with prior bariatric surgery were excluded. Complication-related ICD-10/CPT codes are collected and readmission costs will be estimated from ICD codes using the lower limit of CMS transparent NYU standard charges (3). Direct charge data for surgery and length of stay cost data were also obtained. Statistical T-test and chi-squared analysis were used to compare groups.
RESULTS: Direct operating cost data at NYU Health Campuses demonstrated RSG was associated with 4% higher total charges, due to higher OR charges, robotic-specific supplies, and more post-op ED visits.
CONCLUSIONS: RSG was associated with higher overall hospital charges compared to LSG, though there are multiple contributing factors. More research is needed to identify cost saving measures. This study is retrospective in nature, and does not include indirect costs nor reimbursement. Direct operating costs, per contractual agreement with suppliers, are only given as percentages. Data are limited to 30-day follow-up.

BACKGROUND: The cost-to-charge ratio reflects the markup of hospital services. A lower cost-to-charge ratio indicates lower costs and/or greater charges. This study examines factors associated with cost-to-charge ratio trends to determine whether decreasing cost-to-charge ratio is associated with worse surgical outcomes.
METHODS: The Florida Agency for Healthcare Administration Inpatient database (2018-2020) was queried for common surgical procedures and linked to the Distressed Communities Index, RAND Corporation Hospital data, Center for Medicare Services Cost Reports, and American Hospital Association data. Only hospitals with monotonically increasing or decreasing cost-to-charge ratio were included in the study. Univariable analysis compared these hospitals. Using patient-level data, interpretable machine learning predicted cost-to-charge ratio trend while identifying influential factors.
RESULTS: The cohort had 67 hospitals (27 increasing cost-to-charge ratio and 40 decreasing cost-to-charge ratio) with 35,661 surgeries. Decreasing cost-to-charge ratio hospitals were more often proprietarily owned (78% vs 33%, P = .01) and had greater mean total charges ($134,349 ± $114,510 vs $77,185 ± $82,027, P < .01) with marginally greater mean estimated costs ($14,863 ± $12,343 vs $14,458 ± $15,440, P < .01). Patients from decreasing cost-to-charge ratio hospitals had greater rates of most comorbidities (P < .05) but no difference in mortality or overall complications. Machine-learning models revealed charges rather than clinical factors as most influential in cost-to-charge ratio trend prediction.
CONCLUSIONS: Decreasing cost-to-charge ratio hospitals charge vastly more despite minimally greater estimated costs and no difference in outcomes. Although differences in case-mix existed, charges were the predominant differentiators. Patient clinical factors had far less of an impact.