Risk-adjusted Models Research Articles

Frailty Among Bladder Augmentation Patients: Healthcare Utilization and Perioperative Outcomes

ObjectivesTo study the impact of frailty on healthcare utilization in patients undergoing benign pelvic reconstructive surgery; specifically, bladder augmentation. MethodsAmerican College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) was queried for adults undergoing bladder augmentation between 2005-2022. The Five-Item Frailty Index (FFI) was used to assign a score from 0 to 6. Healthcare resource utilization was defined by 4 metrics: prolonged length of stay (PLOS), 30-day postoperative readmissions (AR), discharge to continued care (i.e. non-home location) (DCC), overall healthcare resource utilization (HRU) which is a composite of the other 3 outcomes, and complications. Multivariable risk-adjusted regression models were generated. Results364 patients were included, the majority being white (71%), female (52%), with a median age of 49 years. After controlling for baseline variables, higher FFI score (≥2) was independently associated with PLOS (OR 1.90 [1.02-3.53], p=0.04), DCC (OR 2.76 [1.24-6.15], p=0.01), and greater overall HRU (OR 2.64 [1.29-5.40], p=0.008) but not AR (OR 2.27 [0.99-5.19], p=0.05). Higher frailty (FFI ≥2) was independently associated with experiencing any complication (OR 2.32 [1.16-4.64], p=0.02) as well as major complications (Clavien ≥3) (OR 2.56 [1.15-5.7] p=0.02). ConclusionsFrail adults undergoing bladder augmentation experience greater HRU and complications. This highlights the importance of frailty in benign pelvic reconstructive surgery and stresses the need for interventions to optimize frail patients.

Increased Risk of Acute Aortic Events following COVID-19 and Influenza Respiratory Viral Infections

Acute respiratory viral infections have been associated with an increased incidence of adverse cardiovascular events. However, it is unclear whether severe respiratory viral infections are associated with an increased risk of acute aortic syndromes (AAS). This study was designed to assess whether Coronavirus disease 2019 (COVID-19) and Influenza illnesses are associated with an increased incidence of subsequent AAS in the US population. We used the MarketScan database (2011-2021) to identify patients 18-99years of age without prior diagnosis of aortic pathology who were diagnosed with COVID-19 or Influenza. Identified patients were matched 1:1 by age and sex to control patients without COVID-19 or Influenza. The primary outcome was incidence of AAS (dissection, intramural hematoma, penetrating aortic ulcer, or aneurysm rupture) within 180-days of a viral infection. The association between infection and risk of developing an AAS was analyzed using multivariate Cox proportional hazards models. We identified 1,775,698 patients, including 779,229 (44%) with mild COVID-19, 42,141 (2%) with severe COVID-19, and 66,479 (4%) with Influenza that were matched to 887,849 (50%) control patients without COVID-19 or Influenza illnesses. A total of 164 patients experienced AAS within 6-months after diagnosis, which was highest among those after severe COVID-19. The predicted incidence of AAS was significantly higher among patients after severe COVID-19 (14.1 events/100,000 person-years), mild COVID-19 (13.3 events/100,000), and influenza (13.3 events/100,000) when compared to control patients (2.6 events/100,000). In risk-adjusted Cox regression models, severe COVID-19 (HR:5.4, 95% CI:2.8-10.4; P<0.01), mild COVID-19 (HR:5.1, 95% CI:3.3-7.7; P<0.01) and influenza (HR:5.1, 95% CI:2.6-9.7; P<0.01) diagnoses were associated with a significantly increased risk of AAS within 180-days when compared to matched controls. There is an increased risk of developing acute aortic event in the months following illness with Influenza or COVID-19. These data highlight the need to closely monitor at-risk patients following a viral respiratory infection.

Changes in Emergency Department Pediatric Readiness and Mortality.

High emergency department (ED) pediatric readiness is associated with improved survival, but the impact of changes to ED readiness is unknown. To evaluate the association of changes in ED pediatric readiness at US trauma centers between 2013 and 2021 with pediatric mortality. This retrospective cohort study was performed from January 1, 2012, through December 31, 2021, at EDs of trauma centers in 48 states and the District of Columbia. Participants included injured children younger than 18 years with admission or injury-related death at a participating trauma center, including transfers to other trauma centers. Data analysis was performed from May 2023 to January 2024. Change in ED pediatric readiness, measured using the weighted Pediatric Readiness Score (wPRS, range 0-100, with higher scores denoting greater readiness) from national assessments in 2013 and 2021. Change groups included high-high (wPRS ≥93 on both assessments), low-high (wPRS <93 in 2013 and wPRS ≥93 in 2021), high-low (wPRS ≥93 in 2013 and wPRS <93 in 2021), and low-low (wPRS <93 on both assessments). The primary outcome was lives saved vs lost, according to ED and in-hospital mortality. The risk-adjusted association between changes in ED readiness and mortality was evaluated using a hierarchical, mixed-effects logistic regression model based on a standardized risk-adjustment model for trauma, with a random slope-random intercept to account for clustering by the initial ED. The primary sample included 467 932 children (300 024 boys [64.1%]; median [IQR] age, 10 [4 to 15] years; median [IQR] Injury Severity Score, 4 [4 to 15]) at 417 trauma centers. Observed mortality by ED readiness change group was 3838 deaths of 144 136 children (2.7%) in the low-low ED group, 1804 deaths of 103 767 children (1.7%) in the high-low ED group, 1288 deaths of 64 544 children (2.0%) in the low-high ED group, and 2614 deaths of 155 485 children (1.7%) in the high-high ED group. After risk adjustment, high-readiness EDs (persistent or change to) had 643 additional lives saved (95% CI, -328 to 1599 additional lives saved). Low-readiness EDs (persistent or change to) had 729 additional preventable deaths (95% CI, -373 to 1831 preventable deaths). Secondary analysis suggested that a threshold of wPRS 90 or higher may optimize the number of lives saved. Among 716 trauma centers that took both assessments, the median (IQR) wPRS decreased from 81 (63 to 94) in 2013 to 77 (64 to 93) in 2021 because of reductions in care coordination and quality improvement. Although the findings of this study of injured children in US trauma centers were not statistically significant, they suggest that trauma centers should increase their level of ED pediatric readiness to reduce mortality and increase the number of pediatric lives saved after injury.

Development and Validation of the Hospital Medicine Safety Sepsis Initiative Mortality Model

When comparing outcomes after sepsis, it is essential to account for patient case mix to make fair comparisons. We developed a model to assess risk-adjusted 30-day mortality in the Michigan Hospital Medicine Safety sepsis initiative (HMS-Sepsis). Can HMS-Sepsis registry data adequately predict risk of 30-day mortality? Do performance assessments using adjusted vsunadjusted data differ? Retrospective cohort of community-onset sepsis hospitalizations in the HMS-Sepsis registry (April 2022-September 2023), with split derivation (70%) and validation (30%) cohorts. We fit a risk-adjustment model (HMS-Sepsis mortality model) incorporating acute physiologic, demographic, and baseline health data and assessed model performance using concordance (C) statistics, Brier scores, and comparisons of predicted vsobserved mortality by deciles of risk. We compared hospital performance (first quintile, middle quintiles, fifth quintile) using observed vsadjusted mortality to understand the extent to which risk adjustment impacted hospital performance assessment. Among 17,514 hospitalizations from 66 hospitals during the study period, 12,260 hospitalizations (70%) were used for model derivation and 5,254 hospitalizations (30%) were used for model validation. Thirty-day mortality for the total cohort was 19.4%. The final model included 13 physiologic variables, two physiologic interactions, and 16 demographic and chronic health variables. The most significant variables were age, metastatic solid tumor, temperature, altered mental status, and platelet count. The model C statistic was 0.82 for the derivation cohort, 0.81 for the validation cohort, and≥ 0.78 for all subgroups assessed. Overall calibration error was 0.0%, and mean calibration error across deciles of risk was 1.5%. Standardized mortality ratios yielded different assessments than observed mortality for 33.9%of hospitals. The HMS-Sepsis mortality model showed strong discrimination and adequate calibration and reclassified one-third of hospitals to a different performance category from unadjusted mortality. Based on its strong performance, the HMS-Sepsis mortality model may aid in fair hospital benchmarking, assessment of temporal changes, and observational causal inference analysis.

A Comparison of Variable Input Strategies used for Risk-adjustment Models of Antimicrobial Use

Background: External comparisons of antimicrobial use (AU) may be more informative if adjusted for encounter characteristics. Optimal methods to define input variables for encounter-level risk-adjustment models of AU are not established. Methods: This retrospective analysis of electronic health record data included 50 US hospitals in 2020-2021. We used NHSN definitions for all antibacterials days of therapy (DOT), including adult and pediatric encounters with at least 1 day present in inpatient locations. We assessed 4 methods to define input variables: 1) diagnosis-related group (DRG) categories by Yu et al., 2) adjudicated Elixhauser comorbidity categories by Goodman et al., 3) all Clinical Classification Software Refined (CCSR) diagnosis and procedure categories, and 4) adjudicated CCSR categories where codes not appropriate for AU risk-adjustment were excluded by expert consensus, requiring review of 867 codes over 4 months to attain consensus. Data were split randomly, stratified by bed size as follows: 1) training dataset including two-thirds of encounters among two-thirds of hospitals; 2) internal testing set including one-third of encounters within training hospitals, and 3) external testing set including the remaining one-third of hospitals. We used a gradient-boosted machine (GBM) tree-based model and two-staged approach to first identify encounters with zero DOT, then estimate DOT among those with &gt;0.5 probability of receiving antibiotics. Accuracy was assessed using mean absolute error (MAE) in testing datasets. Correlation plots compared model estimates and observed DOT among testing datasets. The top 20 most influential variables were defined using modeled variable importance. Results: Our datasets included 629,445 training, 314,971 internal testing, and 419,109 external testing encounters. Demographic data included 41% male, 59% non-Hispanic White, 25% non-Hispanic Black, 9% Hispanic, and 5% pediatric encounters. DRG was missing in 29% of encounters. MAE was lower in pediatrics as compared to adults, and lowest for models incorporating CCSR inputs (Figure 1). Performance in internal and external testing was similar, though Goodman/Elixhauser variable strategies were less accurate in external testing and underestimated long DOT outliers (Figure 2). Agnostic and adjudicated CCSR model estimates were highly correlated; their influential variables lists were similar (Figure 3). Conclusion: Larger numbers of CCSR diagnosis and procedure inputs improved risk-adjustment model accuracy compared with prior strategies. Variable importance and accuracy were similar for agnostic and adjudicated approaches. However, maintaining adjudications by experts would require significant time and potentially introduce personal bias. If findings are confirmed, the need for expert adjudication of input variables should be reconsidered.Disclosure: Elizabeth Dodds Ashley: Advisor- HealthTrackRx. David J Weber: Consultant on vaccines: Pfizer; DSMB chair: GSK; Consultant on disinfection: BD, GAMA, PDI, Germitec

Increased Patient Age as a Risk Factor Following Free Flap Reconstruction after Breast Cancer: A Single Institutional Review of 2,598 Cases.

Autologous breast reconstruction (ABR) after mastectomy is increasing due to benefits over implant-based reconstruction. However, free flap reconstruction is not universally offered to patients of advanced age due to perceived increased perioperative risk. Patients undergoing free flap breast reconstruction at our institution from 2005 to 2018 were included. Risk-adjusted logistic regression models were fit while controlling for demographic and comorbid characteristics to determine the association of age with the probability of venous thromboembolism (VTE), delayed healing, skin necrosis, surgical site infection (SSI), seroma, hematoma, hernia, and flap loss. Linear predictions from risk-adjusted logistic regression models were used to create spline curves and determine the risk of outcomes associated with age. A cohort of 2,598 patients underwent free flap breast reconstruction in the period examined. The median age was 51 with approximately 9% of patients being 65 or older. Increased age was associated with a greater risk of delayed healing, skin necrosis, and hematoma after surgery. There was no increased risk of medical complications such as VTE or complications such as flap loss, seroma, or SSI. A set age cutoff for patients undergoing free flap breast reconstruction does not appear warranted. There is no difference in major surgical complications such as flap loss with increasing age. However, older age does predispose patients to specific wound complications such as hematoma, skin necrosis, and delayed wound healing, which should guide preoperative counseling. Further, medical complications do not increase with advanced age. Overall, however, the safety of ABR in older patients appears uncompromised.

5-year multicenter analysis of clinical trial participation and total cost of care for older adults with cancer.

11013 Background: Clinical trial access particularly for older adults aged 65 years and above with cancer is impacted by multilevel barriers, including cost concerns to payers in the usual models of cancer care delivery. Alternative models recently studied include the Centers for Medicare and Medicaid Innovation’s Oncology Care Model (OCM), a value-based care pilot to enhance quality &amp; reduce costs. Here, we investigated the association between clinical trial participation (CTP) and the total cost of care (TCOC) within this non-traditional model. Methods: We identified prospective longitudinal observational cohort of patients cared for in 11 states within 323 clinics in 14 multi-site community practices participating in OCM, in The US Oncology Network and extracted data (episode claims, medical records, episodes) from OCM performance periods (PP) 3-11 between 7/1/17 and 6/30/22. Propensity score matching by PP, age, gender, date of death, cancer type identified the analysis cohort of matched episodes, then stratified by trial participation (CTP vs usual care) to compare TCOC and drug expenditures. Results: Over 5 years, 121,717 unique patients (94% of whom were aged 65y+) received 282,604 episodes of cancer care in the community, most common cancers being breast, lung, multiple myeloma. Propensity score matching identified 13,260 matched episodes (6630 usual care, 6630 CTP). The actual expenditure differed by $2,341 per episode between CTP &amp; usual care (CTP $43,890 vs usual care $41,548; p &lt; 0.0001), while trial participation had significant savings against the OCM CTP benchmark ($4816 saved per CTP episode), compared to usual care vs its benchmark ($826 saved per usual care episode, p &lt; 0.0001). Drug costs per episode did not differ between the two groups (CTP $29,516 vs usual care $30,553; p 0.54). Further breakdown shows that drug spending on anticancer agents (CTP $28,008 vs usual care $29,139) and other drugs including supportive care meds (CTP $1,511 vs usual care $1,414) did not differ significantly. Conclusions: Clinical trial participation saw relative cost savings against the benchmark compared to usual care, owing largely to the higher benchmark prices for CTP episodes in OCM’s risk-adjusted price prediction model, rather than an absolute cost savings to payers. Drug expenditures remain the largest contributor to total cost of care in this alternative model. The contributors to both drug expenditures as well as the absence of absolute cost savings between the two cohorts represent key areas for further investigation. CMMI Disclaimer: The statements contained in this document are solely those of the authors and do not necessarily reflect the views or policies of CMS. The authors assume responsibility for the accuracy and completeness of the information contained in this document.

Contrasting the Performance of Active and Passive Unit Trusts under Normal Market Conditions: Is the Experience of Emerging Markets Different?

The predominant tradition in the literature is to scrutinise the competitive performance of passive and active investment strategies with less regard to the prevailing market climate. The thesis of this paper is that volatile market conditions may necessitate investment strategy adjustments. Such readjustments may confound empirical comparison of passive and active investing if econometric models do not adapt accordingly. Currently, the literature cannot give a stylised fact of whether the historical claims of passive–active relationships relate to normal market conditions or not, which is a flaw. The present study reduces the flagged knowledge gap by answering the question whether passive investment outperforms active investing under normal market conditions. The study applies the parametric and non-parametric risk-adjusted models of the Jensen alpha and Sharpe index. The empirical analysis is based on the weekly returns of 16 unit trusts listed on South Africa’s Johannesburg Stock Exchange for ten years (between 2009 and 2019). Consistent with the hypothesis of inefficient capital markets in developing economies, the study finds that active investing significantly outperforms passive investment strategy under normal market conditions. The results should benefit investors and policymakers in the era of increasing competitiveness, digitalisation, and globalisation.

Multi-omics analysis of macrophage-associated receptor and ligand reveals a strong prognostic signature and subtypes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a significant contributor to morbidity and mortality worldwide. The interaction between receptors and ligands is the primary mode of intercellular signaling and plays a vital role in the progression of HCC. This study aimed to identify the macrophage-related receptor ligand marker genes associated with HCC and further explored the molecular immune mechanisms attributed to altered biomarkers. Single-cell RNA sequencing data containing primary and recurrent samples were downloaded from the China National GeneBank. Cell types were first identified to explore differences between immune cells from different sample sources. CellChat analysis was used to infer and analyze intercellular communication networks quantitatively. Three molecular subtypes were constructed based on the screened twenty macrophage-associated receptor ligand genes. Bulk RNA-Seq data were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. After the screening, the minor absolute shrinkage and selection operator (LASSO) regression model was employed to identify key markers. After collecting peripheral blood and clinical information from patients, an enzyme-linked immunosorbent assay (ELISA) was used to detect the correlation between key markers and IL-10, one of the macrophage markers. After developing a new HCC risk adjustment model and conducting analysis, it was found that there were significant differences in immune status and gene mutations between the high-risk and low-risk groups of patients based on macrophage-associated receptor and ligand genes. This study identified SPP1, ANGPT2, and NCL as key biological targets for HCC. The drug-gene interaction network analysis identified wortmannin, ribavirin, and tarnafloxin as potential therapeutic drugs for the three key markers. In a clinical cohort study, patients with immune checkpoint inhibitor (ICI) resistance had significantly higher expression levels of OPN, ANGPT2, NCL, and IL-10 than patients with ICI-responsiveness. These three key markers were positively correlated with the expression level of IL-10. The signature based on macrophage-associated receptor and ligand genes can accurately predict the prognosis of patients with HCC and the sensitivity to immunotherapy. These results may help guide the development of targeted prevention and personalized treatment of HCC.

Unplanned Surgery in Dually Eligible Beneficiaries for Conditions that Should Be Treated Electively.

To evaluate the rate of unplanned surgery among dually eligible beneficiaries for surgical conditions that should be treated electively. Access-sensitive surgical conditions (e.g. abdominal aortic aneurysm repair, colectomy for colon cancer, ventral hernia repair) are ideally treated with elective surgery, but when left untreated have a natural history leading to unplanned surgery. Dually eligible beneficiaries may face systematic barriers to access surgical care. Cross-sectional retrospective study of all beneficiaries who were eligible for both Medicare and Medicaid, and underwent surgery for an access-sensitive surgical condition between 2016-2020. We compared the rate of unplanned surgery as well as 30-day mortality, complications and readmissions for dually eligible versus non-dually eligible beneficiaries. Sex, age, race/ethnicity, comorbidities, teaching status, nursing ratio, hospital region and bed size and surgery year were included in the risk-adjustment model. Out of 853,500 beneficiaries, 118,812 were dually eligible with an average age (SD) of 75.2(7.7) years. Compared to non-dually eligible beneficiaries, dually eligible beneficiaries had a higher rates of unplanned surgery for access-sensitive surgical conditions (45.1% vs. 31.8%, P<0.001), 30-day mortality (2.9% vs. 2.6%, aOR=1.10 (1.07-1.14), P<0.001), complications (23.6% vs. 20.1%, aOR=1.23 (1.20-1.25), P<0.001), and 30-day readmissions (15.5% vs. 12.9%, aOR=1.24 (1.22-1.27), P<0.001). These differences narrowed significantly when evaluating elective procedures only. Dually eligible beneficiaries were more likely to undergo unplanned surgery for access-sensitive surgical conditions, leading to worse rates of mortality, complications and readmissions. Our findings suggest that improving rates of elective surgery for these conditions represents an actionable target to narrow the difference in post-operative outcomes between dually eligible and non-dually eligible beneficiaries.

Better off by risk adjustment? Socioeconomic disparities in care utilization in Sweden following a payment reform

AbstractReducing socioeconomic health inequalities is a key goal of most health systems. A challenge in this regard is that healthcare providers may have incentives to avoid or undertreat patients who are relatively costly to treat. Due to the socioeconomic gradient in health, individuals with low socioeconomic status (SES) are especially likely to be negatively affected by such attempts. To counter these incentives, payments are often risk adjusted based on patient characteristics. However, empirical evidence is lacking on how, or if, risk adjustment affects care utilization. We examine if a novel risk adjustment model in primary care affected socioeconomic differences in care utilization among individuals with a chronic condition. The new risk adjustment model implied that the capitation—the monthly reimbursement paid by the health authority to care providers for each enrolled patient—increased substantially for chronically ill low‐SES patients. Yet, we do not find any robust evidence that their access to primary care improved relative to patients with high SES, and we find no effects on adverse health events (hospitalizations). These results suggest that the new risk adjustment model did not reduce existing health inequalities, indicating the need for more targeted incentives and interventions to reach low‐SES groups.

Demystifying the association of center-level operative trauma volume and outcomes of emergency general surgery

BackgroundRecent studies have demonstrated a positive volume-outcome relationship in emergency general surgery. Some have advocated for the sub-specialization of emergency general surgery independent from trauma. We hypothesized inferior clinical outcomes of emergency general surgery with increasing center-level operative trauma volume, potentially attributable to overall hospital quality. MethodsAdults (≥18 years) undergoing complex emergency general surgery operations (large and small bowel resection, repair of perforated peptic ulcer, lysis of adhesions, laparotomy) were identified in the 2016 to 2020 Nationwide Readmissions Database. Multivariable risk-adjusted models were developed to evaluate the association of treatment at a high-volume trauma center (reference: low-volume trauma center) with clinical and financial outcomes after emergency general surgery. To evaluate hospital quality, mortality among adult hospitalizations for acute myocardial infarction was assessed by hospital trauma volume. ResultsOf an estimated 785,793 patients undergoing a complex emergency general surgery operation, 223,116 (28.4%) were treated at a high-volume trauma center. Treatment at a high-volume trauma center was linked to 1.19 odds of in-hospital mortality (95% confidence interval 1.12–1.27). Although emergency general surgery volume was associated with decreasing predicted risk of mortality, increasing trauma volume was linked to an incremental rise in the odds of mortality after emergency general surgery. Secondary analysis revealed increased mortality for admissions for acute myocardial infarction with greater trauma volume. ConclusionWe note increased mortality for emergency general surgery and acute myocardial infarction in patients receiving treatment at high-volume trauma centers, signifying underlying structural factors to broadly affect quality. Thus, decoupling trauma and emergency general surgery services may not meaningfully improve outcomes for emergency general surgery patients. Our findings have implications for the evolving specialty of emergency general surgery, especially for the safety and continued growth of the acute care surgery model.

Elective Versus Nonelective Spinal Fusions: Surgical and Financial Outcomes in a Bundled Payment Reimbursement Model.

Bundled payment for care improvement advanced (BPCIA) is a voluntary alternative payment model administered by the Centers for Medicare and Medicaid Services using value-based care to reduce costs by incentivizing care coordination and improved quality. We aimed to identify drivers of negative financial performance in BPCIA among patients undergoing spinal fusion surgery. This is a single-institution retrospective review of patients enrolled in BPCIA undergoing spinal fusion with DRGs 453, 454, 455, 459, and 460 from 2018 to 2022. Univariate and multivariable logistic regression analyses were used to identify factors associated with negative financial performance and compare nonelective vs elective surgeries. We identified 172 cases, of which 24% (n = 41) had negative financial performance and 9% (n = 16) were nonelective cases. Nonelective surgery (P < .001, odds ratios 19.81), greater levels instrumented (P < .001), and no anterior procedure (P = .001) were associated with negative financial performance. Surgical outcomes associated with negative financial performance and factors more common in nonelective cases respectively included higher hospital length of stay (P < .001, P = .005), nonhome discharge (P < .001, P < .001), 90-day hospital readmission (P < .001, P < .001), 90-day additional nonspine surgery (P = .01, P < .001), and less days at home of the 90 days (P < .001, P = .01). Nonelective surgeries had higher total spend (P = .01), readmission spend (P = .03), skilled nursing facility spend (P = .02), durable medical equipment spend (P = .003), and professional billing spend (P = .04) despite similar target pricing (P = .60), all of which resulted in greater financial loss compared with elective surgeries (P = .001). Nonelective spinal surgery is an independent preoperative predictor of negative financial performance in BPCIA. Nonelective spinal surgeries are more likely than elective surgeries to have higher length of stay, nonhome discharge, 90-day hospital readmission, 90-day additional nonspine surgeries, and less time spent at home during the bundled period, all of which contribute to higher health care utilization. The Centers for Medicare and Medicaid Services should consider incorporating nonelective spine surgery into risk-adjustment models.

Social Determinants of Long-Term Opioid Use Following Total Knee Arthroplasty.

Total knee arthroplasty (TKA) risks persistent pain and long-term opioid use (LTO). The role of social determinants of health (SDoH) in LTO is not well established. We hypothesized that SDoH would be associated with postsurgical LTO after controlling for relevant demographic and clinical variables. This study utilized data from the Veterans Affairs Surgical Quality Improvement Program, VA Corporate Data Warehouse, and Centers for Medicare and Medicaid Services, including Veterans aged ≥ 65 who underwent elective TKA between 2013 and 2019 with no postsurgical complications or history of significant opioid use. LTO was defined as > 90 days of opioid use beginning within 90 days postsurgery. SDoH variables included the Area Deprivation Index, rurality, and housing instability in the last 12 months identified via medical record screener or International Classification of Diseases, Tenth Revision codes. Multivariable risk adjustment models controlled for demographic and clinical characteristics. Of the 9,064 Veterans, 97% were male, 84.2% white, mean age was 70.6 years, 46.3% rural, 11.2% living in highly deprived areas, and 0.9% with a history of homelessness/housing instability. Only 3.7% (n = 336) developed LTO following TKA. In a logistic regression model of only SDoH variables, housing instability (odds ratio [OR] = 2.38, 95% confidence interval [CI]: 1.09-5.22) and rurality conferred significant risk for LTO. After adjusting for demographic and clinical variables, LTO was only associated with increasing days of opioid supply in the year prior to surgery (OR = 1.52, 95% CI: 1.43-1.63 per 30 days) and the initial opioid fill (OR = 1.07; 95% CI: 1.06-1.08 per day). Our primary hypothesis was not supported; however, our findings do suggest that patients with housing instability may present unique challenges for postoperative pain management and be at higher risk for LTO.

Development and validation of a lifetime prediction model for incident type 2 diabetes in patients with established cardiovascular disease: the CVD2DM model.

Identifying patients with established cardiovascular disease (CVD) who are at high risk of type 2 diabetes (T2D) may allow for early interventions, reducing the development of T2D and associated morbidity. The aim of this study was to develop and externally validate the CVD2DM model to estimate the 10-year and lifetime risks of T2D in patients with established CVD. Sex-specific, competing risk-adjusted Cox proportional hazard models were derived in 19 281 participants with established CVD and without diabetes at baseline from the UK Biobank. The core model's pre-specified predictors were age, current smoking, family history of diabetes mellitus, body mass index, systolic blood pressure, fasting plasma glucose, and HDL cholesterol. The extended model also included HbA1c. The model was externally validated in 3481 patients from the UCC-SMART study. During a median follow-up of 12.2 years (interquartile interval 11.3-13.1), 1628 participants with established CVD were diagnosed with T2D in the UK Biobank. External validation c-statistics were 0.79 [95% confidence interval (CI) 0.76-0.82] for the core model and 0.81 (95% CI 0.78-0.84) for the extended model. Calibration plots showed agreement between predicted and observed 10-year risk of T2D. The 10-year and lifetime risks of T2D can be estimated with the CVD2DM model in patients with established CVD, using readily available clinical predictors. The model would benefit from further validation across diverse ethnic groups to enhance its applicability. Informing patients about their T2D risk could motivate them further to adhere to a healthy lifestyle.

366 Frailty Is a Risk Factor for Mortality in Operative and Non-operative Type II Odontoid Fracture Patients: An ACS-TQIP Survival Analysis of 22,440 Patients

INTRODUCTION: Type II Odontoid Fractures (T2OF) are the most prevalent cervical spine fractures in adults =65 years old. Nonetheless, there is insufficient data regarding the influence of frailty on outcomes for both nonoperative and operative, treatment approaches. METHODS: We conducted a retrospective review of T2OF patients from 2015 to 2020 using the ACS-TQIP® database. Using unmatched and propensity score-matched (PSM) risk-adjusted models we assessed the association between frailty and all adverse outcomes. Survival probabilities were examined using Kaplan-Meier (K-M) plots and frailty-based risk stratification. Our outcomes include major complications, hospital length of stay (LOS), nonhome discharge (NHD), mortality rates, and survival probabilities. RESULTS: There were 22,440 T2OF patients, comprised of 31.8% (n = 7,138) robust, 35.2% (n = 7,913) typical, 22.9% (n = 5,158) frail, and 9.9% (n = 2,231) very frail patients. Results from unmatched multivariable analyses revealed a dose-dependent relationship between frailty and all outcomes, in both cohorts (p &lt; 0.05). PSM resulted in 3,942 nonoperative and operative matched pairs. Preoperative characteristics were similar between cohorts, except for age: 75 years (IQR: 60-83) nonoperative and 70 years (IQR: 56-78) operative, and male sex: nonoperative 1,799 (55.8%) and operative 439 (61.1%). Significant mortality reductions (43-77%; p &lt; 0.05) were observed in the operative cohort compared to the nonoperative cohort across all frailty strata. As expected, the operative cohort had longer LOS and increased NHD risk ranging from 64-97%, p &lt; 0.001. In K-M analyses, survival declined significantly across increasing frailty strata p &lt; 0.001. CONCLUSIONS: Increasing frailty in T2OF patients was independently associated with major complications, LOS, NHD, and decreased survival. Interestingly, operative patients exhibited improved survival compared to nonoperative patients across all frailty strata. These findings highlight the importance of frailty in T2OF management and care coordination.

National Institutes of Health Stroke Scale Reporting in Medicare Claims Data: Reporting in the First 3 Years.

Since 2016, hospitals have been able to document International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for the National Institutes of Health Stroke Scale (NIHSS). As of 2023, the Centers for Medicare & Medicaid Services uses NIHSS as a risk adjustment variable. We assessed associations between patient- and hospital-level variables and contemporary NIHSS reporting. We performed a retrospective cross-sectional analysis of 2019 acute ischemic stroke admissions using deidentified, national 100% inpatient Medicare Fee-For-Service data sets. We identified index acute ischemic stroke admissions using the ICD-10-CM code I63.x and abstracted demographic information, medical comorbidities, hospital characteristics, and NIHSS. We linked Medicare and Mount Sinai Health System (New York, NY) registry data from 2016 to 2019. We calculated NIHSS documentation at the patient and hospital levels, predictors of documentation, change over time, and concordance with local data. There were 231 383 index acute ischemic stroke admissions in 2019. NIHSS was documented in 44.4% of admissions and by 66.5% of hospitals. Hospitals that documented ≥1 NIHSS were more commonly teaching hospitals (39.0% versus 5.5%; standardized mean difference score, 0.88), stroke certified (37.2% versus 8.0%; standardized mean difference score, 0.75), higher volume (mean, 80.8 [SD, 92.6] versus 6.33 [SD, 14.1]; standardized mean difference score, 1.12), and had intensive care unit availability (84.9% versus 23.2%; standardized mean difference score, 1.57). Adjusted odds of documentation were lower for patients with inpatient mortality (odds ratio, 0.64 [95% CI, 0.61-0.68]; P<0.0001), in nonmetropolitan areas (odds ratio, 0.49 [95% CI, 0.40-0.61]; P<0.0001), and male sex (odds ratio, 0.95 [95% CI, 0.93-0.97]; P<0.0001). NIHSS was documented for 52.9% of Medicare cases versus 93.1% of registry cases, and 74.7% of Medicare NIHSS scores equaled registry admission NIHSS. Missing ICD-10-CM NIHSS data remain widespread 3 years after the introduction of the ICD-10-CM NIHSS code, and there are systematic differences in reporting at the patient and hospital levels. These findings support continued assessment of NIHSS reporting and caution in its application to risk adjustment models.

Comparing risk-adjusted inpatient fall rates internationally: validation of a risk-adjustment model using multicentre cross-sectional data from hospitals in Switzerland and Austria

BackgroundInpatient falls in hospitals are an acknowledged indicator of quality of care. International comparisons could highlight quality improvement potential and enable cross-national learning. Key to fair cross-national comparison is the availability of a risk adjustment model validated in an international context. This study aimed to 1) ascertain that the variables of the inpatient fall risk adjustment model do not interact with country and thus can be used for risk adjustment, 2) compare the risk of falling in hospitals between Switzerland and Austria after risk adjustment.MethodsThe data on inpatient falls from Swiss and Austrian acute care hospitals were collected on a single measurement day in 2017, 2018 and 2019 as part of an international multicentre cross-sectional study. Multilevel logistic regression models were used to screen for interaction effects between the patient-related fall risk factors and the countries. The risks of falling in hospital in Switzerland and in Austria were compared after applying the risk-adjustment model.ResultsData from 176 hospitals and 43,984 patients revealed an inpatient fall rate of 3.4% in Switzerland and 3.9% in Austria. Two of 15 patient-related fall risk variables showed an interaction effect with country: Patients who had fallen in the last 12 months (OR 1.49, 95% CI 1.10–2.01, p = 0.009) or had taken sedatives/psychotropic medication (OR 1.40, 95% CI 1.05–1.87, p = 0.022) had higher odds of falling in Austrian hospitals. Significantly higher odds of falling were observed in Austrian (OR 1.38, 95% CI 1.13–1.68, p = 0.002) compared to Swiss hospitals after applying the risk-adjustment model.ConclusionsAlmost all patient-related fall risk factors in the model are suitable for a risk-adjusted cross-country comparison, as they do not interact with the countries. Further model validation with additional countries is warranted, particularly to assess the interaction of risk factors “fall in the last 12 months” and “sedatives/psychotropic medication intake” with country variable. The study underscores the crucial role of an appropriate risk-adjustment model in ensuring fair international comparisons of inpatient falls, as the risk-adjusted, as opposed to the non-risk-adjusted country comparison, indicated significantly higher odds of falling in Austrian compared to Swiss hospitals.

Epidemiology of Postoperative Junctional Ectopic Tachycardia in Infants Undergoing Cardiac Surgery

BackgroundJunctional ectopic tachycardia (JET) complicates congenital heart surgery in 2% to 8.3% of cases. JET is associated with postoperative morbidity in single-center studies. We used the Pediatric Cardiac Critical Care Consortium data registry to provide a multicenter epidemiologic description of treated JET. MethodsThis is a retrospective study (February 2019-August 2022) of patients with treated JET. Inclusion criteria were (1) <12 months old at the index operation, and (2) treated for JET <72 hours after surgery. Diagnosis was defined by receiving treatment (pacing, cooling, and medications). A multilevel logistic regression analysis with hospital random effect identified JET risk factors. Impact of JET on outcomes was estimated by margins/attributable risk analysis using previous risk-adjustment models. ResultsAmong 24,073 patients from 63 centers, 1436 (6.0%) were treated for JET with significant center variability (0% to 17.9%). Median time to onset was 3.4 hours, with 34% present on admission. Median duration was 2 days (interquartile range, 1-4 days). Tetralogy of Fallot, atrioventricular canal, and ventricular septal defect repair represented >50% of JET. Patient characteristics independently associated with JET included neonatal age, Asian race, cardiopulmonary bypass time, open sternum, and early postoperative inotropic agents. JET was associated with increased risk-adjusted durations of mechanical ventilation (incidence rate ratio, 1.6; 95% CI, 1.5-1.7) and intensive care unit length of stay (incidence rate ratio, 1.3; 95% CI, 1.2-1.3), but not mortality. ConclusionsJET is treated in 6% of patients with substantial center variability. JET contributes to increased use of postoperative resources. High center variability warrants further study to identify potential modifiable factors that could serve as targets for improvement efforts to ameliorate deleterious outcomes.

Lifetime and 10-year cardiovascular risk prediction in individuals with type 1 diabetes: The LIFE-T1D model.

To develop and externally validate the LIFE-T1D model for the estimation of lifetime and 10-year risk of cardiovascular disease (CVD) in individuals with type 1 diabetes. A sex-specific competing risk-adjusted Cox proportional hazards model was derived in individuals with type 1 diabetes without prior CVD from the Swedish National Diabetes Register (NDR), using age as the time axis. Predictors included age at diabetes onset, smoking status, body mass index, systolic blood pressure, glycated haemoglobin level, estimated glomerular filtration rate, non-high-density lipoprotein cholesterol, albuminuria and retinopathy. The model was externally validated in the Danish Funen Diabetes Database (FDDB) and the UK Biobank. During a median follow-up of 11.8 years (interquartile interval 6.1-17.1 years), 4608 CVD events and 1316 non-CVD deaths were observed in the NDR (n = 39 756). The internal validation c-statistic was 0.85 (95% confidence interval [CI] 0.84-0.85) and the external validation c-statistics were 0.77 (95% CI 0.74-0.81) for the FDDB (n = 2709) and 0.73 (95% CI 0.70-0.77) for the UK Biobank (n = 1022). Predicted risks were consistent with the observed incidence in the derivation and both validation cohorts. The LIFE-T1D model can estimate lifetime risk of CVD and CVD-free life expectancy in individuals with type 1 diabetes without previous CVD. This model can facilitate individualized CVD prevention among individuals with type 1 diabetes. Validation in additional cohorts will improve future clinical implementation.