Risk-adjusted Models Research Articles

Donor and Recipient Factors Associated with Primary Graft Dysfunction Following Lung Transplantation: A DMG Registry Analysis

Current risk-adjusted models to predict primary graft dysfunction (PGD) following lung transplantation (LTx) do not include bedside donor critical care data. Donor management goals (DMG) represent predefined critical care endpoints aimed at optimizing multi-organ donor management. Here, we sought to identify novel predictors to better understand the relationship between donor management and PGD following LTx. We used the national DMG registry to identify a cohort of LTx recipients linked to their respective donors between January 1st, 2015 and March 1st, 2023. Grade 3 PGD (PGD3) was defined according to modified ISHLT criteria. Multivariable modeling was performed to identify risk factors for the development of PGD3. A total of 2704 eligible patients were identified of which 643 (23.8%) developed PGD3. After multivariable modeling, the likelihood of PGD3 was greater with increased donor age (OR 1.06 [1.02, 1.10] per 5 year change, p=0.003), increased donor serum pH at the time of authorization (OR 1.14 [1.02, 1.25] per 0.1 increase, p=0.016), donor history of cocaine use (OR 1.34 [1.05, 1.71], p=0.020), and increased recipient central venous pressure (1.03 [1.01, 1.06], p=0.005). Recipients who received donor lungs in which the DMG for PF ratio was met had a lower likelihood of developing PGD3 (OR 0.63 [0.46, 0.86], p=0.006). This study leverages a novel detailed donor management database to identify factors associated with the development of PGD3. These factors may be used to recognize donors and recipients that may benefit from early interventions to improve short-term outcomes.

Associations of smoking status with long-term cardiovascular events in different arterial territories: the EPIC Norfolk prospective population cohort

Abstract Background Smoking cessation is associated with a reduced risk of cardiovascular disease (CVD) events, mainly in ischemic heart disease (IHD) and stroke. However, the impact of smoking status (never, former or current smoking) on long-term CVD events across different arterial territories remains understudied. Knowledge of such associations is important, as it may assist in risk communication and smoking cessation motivation. We therefore investigated associations between smoking status and territory-specific CVD events in a large cohort with ≥20 years follow-up. Methods We included participants without baseline CVD from the European Prospective Investigation of Cancer (EPIC) Norfolk prospective population cohort. The strengths of associations between smoking, former smoking and never smoking with the first occurrence of a CVD event were evaluated using a competing risk-adjusted regression model. CVD events were defined as hospitalisation or death due to IHD, ischemic stroke, haemorrhagic stroke, peripheral arterial disease (PAD) or aortic aneurysm (AA). Patients were followed-up until the first event, non-CVD death (competing risk) or end of study (15-03-2018). The model was adjusted for sex, age, systolic blood pressure, low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), diabetes, renal function and body mass index. We compared former and current smoking (at baseline) with never smoking. Results We included 23,581 participants (56% women) with a median baseline age of 58 years [interquartile range (IQR) 51-66]. During median follow up of 21.3 years (IQR 19.0-22.8), 26.4% experienced ≥1 CVD event. The first event of occurrence was IHD in 17.3%, ischemic stroke in 3.6%, haemorrhagic stroke in 1.4%, PAD in 3.1% and AA in 1.4%. Current (vs. never) smoking was associated with IHD [adjusted hazard rate (aHR) 1.53, 95% confidence interval (CI) 1.36-1.72], PAD (aHR 1.29, 95%CI 1.07-1.56) and AA (aHR 4.81, 95%CI 3.54-6.54). Current smoking was not significantly associated with ischemic stroke (aHR 1.20, 95% CI 0.95-1.52) and haemorrhagic stroke (aHR 1.18, 95% CI 0.81-1.70). In former (vs. never) smoking individuals, the association with AA remained statistically significant but less pronounced [aHR 1.69, 95%CI 1.29-2.23]. Conversely, former smoking was not associated with IHD (aHR 1.07, 95%CI 0.99-1.16) or PAD (aHR 1.09, 95%CI 0.95-1.26) (Figure 1). Conclusion After 20-years of follow-up, former smokers, compared to never-smokers, remain at considerably higher risk for CVD events, particularly for aortic aneurysm. This knowledge may assist in treatment decisions and risk communication.

SCORE2-ASIA risk prediction algorithms: revised models to estimate 10-year risk of cardiovascular disease in the Asia Pacific Region

Abstract Background Most current cardiovascular disease (CVD) prediction tools have been developed and validated solely in Western countries, while there are significant differences between Asian and Western populations in risk factor distributions and CVD incidence patterns. This study outlines the recalibration and validation of the SCORE2-ASIA model to estimate 10-year fatal and non-fatal CVD risk among adults aged 40-69 years with no previous CVD or diabetes in the Asia Pacific Region. Methods The sex-specific and competing risk-adjusted SCORE2 models were systematically recalibrated to reflect CVD incidence in the Asian clinical practice. We defined four risk regions in Asia according to country-specific CVD mortality rates (low risk (<100 CVD deaths per 100,000), moderate risk (100 to <150 CVD deaths per 100,000), high risk (150 to <300 CVD deaths per 100,000), and very high risk (≥300 CVD deaths per 100,000)). Models were recalibrated using region representative data on expected incidences and risk factor distributions, utilising the methodological approaches developed in recalibration of the SCORE2 models for Europe. The SCORE-ASIA model was externally validated in all risk regions using population-based cohort data from independents cohort data. To ensure the European coefficients well applied to Asian populations, the SCORE2 model derivation process was repeated in external validation cohorts. Results Region-specific incidence was estimated using country-specific data on CVD mortality and data on the ratio of fatal to total events on 9,496,212 individuals from 5 Asian countries (496,212 CVD events). For external validation, we analysed data from 12 additional cohorts in 13 Asian countries (4,775,976 individuals, 176,011 CVD events). After applying the derived risk prediction models to external validation cohorts, the pooled external validation C-index was 0.709 (95%CI 0.676-0.743). In the nationally representative CDMD cohort from Singapore (population completely separate from recalibration data) there was adequate agreement between predicted and observed risks. Risk factor coefficients derived in Asian populations were largely similar to the original SCORE2 coefficients, indicating that the original SCORE2 coefficients apply well to the Asian population. Predicted CVD risk varied several-fold across Asian risk regions. For example, the estimated 10-year CVD risk for a 50-year-old non-smoker, with a systolic blood pressure of 140mmHg, total cholesterol of 5.5mmol/L, and HDL-cholesterol of 1.3mmol/L, ranged from 12% for men in low-risk countries to 26% for men in very-high-risk countries, and from 7% for women in low-risk countries to 26% for women in very-high-risk countries. Conclusions The SCORE2-ASIA model accurately predicts 10-year risk of CVD for apparently healthy people in Asian countries after systematic recalibration, thereby enhancing the identification of individuals at higher risk of developing CVD across the Asia Pacific Region.

Heterogeneous associations of traditional atherosclerotic risk factors and long-term events in different arterial territories: the EPIC Norfolk prospective population cohort

Abstract Background Atherosclerotic risk factors have been associated with cardiovascular disease (CVD) events in specific arterial territories. However, these observations are limited by relatively short-term follow-up (i.e. ten years) and a focus on a limited number of outcome events. We therefore investigated associations between atherosclerotic risk factors and territory-specific cardiovascular events in a large cohort with ≥20 years follow-up. Methods We included participants without baseline CVD from the European Prospective Investigation of Cancer (EPIC) Norfolk prospective population cohort. The strengths of associations between CVD risk factors (smoking, low-density lipoprotein cholesterol (LDL-c), and systolic blood pressure (SBP)) and the first occurrence of a CVD event were evaluated using a competing risk-adjusted regression model. CVD events were defined as hospitalisations or death due to ischemic heart disease (IHD), ischemic stroke, haemorrhagic stroke, peripheral arterial disease (PAD) or aortic aneurysm (AA). Patients were followed-up until the first event, non-CVD death (competing risk) or end of study (15-03-2018). The model was adjusted for sex, age, diabetes, HDL-c, kidney function and body mass index. LDL-c and SBP were analysed by comparing highest versus lowest quartiles. Smoking (at baseline) was compared to never smoking. Results We included 23,581 participants (56% women) with a median baseline age of 58 years [interquartile range (IQR) 51-66]. During median follow up of 21.3 years (IQR 19.0-22.8), 26.4% of individuals experienced ≥1 CVD event. The first event of occurrence was IHD in 17.3%, ischemic stroke in 3.6%, haemorrhagic stroke in 1.4%, PAD in 3.1% and AA in 1.4%. LDL-c was strongest associated with IHD [adjusted hazard rate (aHR) 1.7, 95% confidence interval (CI) 1.5-1.9], SBP with ischemic stroke (aHR 1.9, 95%CI 1.5-2.5) and smoking with AA (aHR 4.8, 95%CI 3.5-6.5) (Figure 1). Conclusions There is considerable heterogeneity in the associations between atherosclerotic risk factors and long-term, first, territory-specific cardiovascular events. Knowledge of these differences may assist in personalised treatment decisions and risk communication.

Effect of disability, homelessness, and neighborhood marginalization on risk-adjustment for hospital performance measurement.

Background It is not known how disability, homelessness, or neighborhood marginalization influence risk-adjusted hospital performance measurement in a universal health care system. Methods We evaluated the effect of including these equity-related factors in risk-adjustment models for in-hospital mortality, and 7- and 30-day readmission in 28 hospitals in Ontario, Canada. We compared risk-adjustment with commonly-used clinical factors to models that also included homelessness, disability, and neighborhood indices of marginalization. We evaluated models in historical data using internal-external cross-validation. We calculated risk-standardized outcome rates for each hospital in a recent reporting period using mixed-effects logistic regression. Results The cohort included 544,805 admissions. Adjustment for disability, homelessness, and neighborhood marginalization had little impact on discrimination or calibration of risk-adjustment models. However, it influenced comparative hospital performance on risk-standardized 30-day readmission rates, resulting in 5 hospitals being reclassified between below-average, average, and above-average groups. No hospitals were reclassified for mortality and 7-day readmission. Conclusion In a system with universally insured hospital services, adjustment for disability, homelessness, and neighborhood marginalization influenced estimates of hospital performance for 30-day readmission but not 7-day readmission or in-hospital mortality. These findings can inform researchers and policymakers as they thoughtfully consider when to adjust for these factors in hospital performance measurement.

Investigating the Impact of Additional Congenital Malformations on Speech Outcomes at age Five in Children with a Cleft Palate.

To determine whether children born with a cleft palate ± lip (CP ± L) and additional congenital differences (ACDs - including 'Congenital malformations and deformations' as coded in ICD-10), are less likely to meet the three national speech outcome standards at age five compared to children with CP ± L and no ACDs. An observational study, utilizing national data from the UK Cleft Registry and Audit NEtwork (CRANE) Database linked to national administrative data of hospital admissions. National Health Service, England. 2191 children (993 female, 1198 male) with CP ± L, born 2006-2012 in England, with complete speech data and consent to data linkage. Perceptual speech analysis utilised the Cleft Audit Protocol for Speech - Augmented (CAPS-A) rating and United Kingdom National Speech Outcome Standards (Speech Standard 1 (SS1), Speech Standard 2a (SS2a) and Speech Standard 3 (SS3)). Of 2191 children, 759 (35%) had at least one ACD. Presence of one ACD did not significantly impact speech outcomes but two or more ACDs reduced the odds of achieving all three speech standards: SS1 aOR 0.602 (CI 0.45-0.82, P = .002), SS2a aOR 0.563 (CI 0.41-0.77, P = .001), SS3 aOR 0.606 (0.43-0.84, P = .003). When exploring ACDs by ICD-10 groupings, congenital malformations of the 'Eye, ear, face and neck', 'Circulatory system', 'Digestive system', 'Musculoskeletal system' and 'Other congenital malformations' reduced a child's odds of achieving the speech standards. ACDs, in the absence of a known syndrome, have a significant impact on speech outcome at age five. Incorporating these factors into risk-adjustment models for service level outcome reporting is recommended.

Social Determinants of Health and Risk-Adjusted Sepsis Mortality in the Nationwide Veterans Affairs Healthcare System.

Traditional risk prediction and risk adjustment models have focused on clinical characteristics, but accounting for social determinants of health (SDOH) and complex health conditions could improve understanding of sepsis outcomes and our ability to predict outcomes, treat patients, and assess quality of care. To evaluate the impact of SDOH and health scales in sepsis mortality risk prediction and hospital performance assessment. Observational cohort study. One hundred twenty-nine hospitals in the nationwide Veterans Affairs (VA) Healthcare System between 2017 and 2021. Veterans admitted through emergency departments with community-acquired sepsis. Individual- and community-level SDOH (race, housing instability, marital status, Area Deprivation Index [ADI], and rural residence) and two health scales (the Care Assessment Need [CAN] score and Claims-Based Frailty Index [CFI]). The primary outcome was 90-day mortality from emergency department arrival; secondary outcomes included 30-day mortality and in-hospital mortality. Among 144,889 patients admitted to the hospital with community-acquired sepsis, 139,080 were men (96.0%), median (IQR) age was 71 (64-77) years, and median (IQR) ADI was 60 (38-81). Multivariable regression models had good calibration and discrimination across models that adjusted for different sets of variables (e.g., AUROC, 0.782; Brier score, 1.33; and standardized mortality rate, 1.00). Risk-adjusted hospital performance was similar across all models. Among 129 VA hospitals, three hospitals shifted from the lowest or highest quintile of performance when comparing models that excluded SDOH to models that adjusted for all variables. Models that adjusted for ADI reported odds ratios (CI) of 1.00 (1.00-1.00), indicating that ADI does not significantly predict sepsis mortality in this cohort of patients. In patients with community-acquired sepsis, adjusting for community SDOH variables such as ADI did not improve 90-day sepsis mortality predictions in mortality models and did not substantively alter hospital performance within the VA Healthcare System. Understanding the role of SDOH in risk prediction and risk adjustment models is vital because it could prevent hospitals from being negatively evaluated for treating less advantaged patients. However, we found that in VA hospitals, the potential impact of SDOH on 90-day sepsis mortality was minimal.

Importance of risk adjusting central line-associated bloodstream infection rates in children.

Central line-associated bloodstream infection (CLABSI) is one of the most prevalent pediatric healthcare-associated infections and is used to benchmark hospital performance. Pediatric patients have increased in acuity and complexity over time. Existing approaches to risk adjustment do not control for individual patient characteristics, which are strong predictors of CLABSI risk and vary over time. Our objective was to develop a risk adjustment model for CLABSI in hospitalized children and compare observed to expected rates over time. We conducted a prospective cohort study using electronic health record data at a quaternary Children's Hospital. We included hospitalized children with central catheters. Risk factors identified from published literature were considered for inclusion in multivariable modeling based on association with CLABSI risk in bivariable analysis and expert input. We calculated observed and expected (risk model-adjusted) annual CLABSI rates. Among 16,411 patients with 520,209 line days, 633 patients experienced 796 CLABSIs. The final model included age, behavioral health condition, non-English speaking, oncology service, port catheter type, catheter dwell time, lymphatic condition, total parenteral nutrition, and number of organ systems requiring ICU level care. For every organ system receiving ICU level care the odds ratio for CLABSI was 1.24 (95% CI 1.12-1.37). Although not statistically different, observed rates were lower than expected rates for later years. Failure to adjust for patient factors, particularly acuity and complexity of disease, may miss clinically significant differences in CLABSI rates, and may lead to inaccurate interpretation of the impact of quality improvement efforts.

External validation of the parsimonious EuroLung risk models: analysis of the Brazilian Lung Cancer Registry.

The purpose of this study was to assess performance in the Brazilian Lung Cancer Registry Database by using the parsimonious EuroLung risk models for morbidity and mortality. The EuroLung1 and EuroLung2 models were tested and evaluated through calibration (calibration plot, Brier score, and the Hosmer-Lemeshow test) and discrimination (ROC AUCs), in a national multicenter registry of 1,031 patients undergoing anatomic lung resection. The evaluation of performance in Brazilian health care facilities utilizing risk-adjustment models, specifically EuroLung1 and EuroLung2, revealed substantial miscalibration, as evidenced by calibration plots and Hosmer-Lemeshow tests in both models. In terms of calibration, EuroLung1 exhibited a calibration plot with overlapping points, characterized by a slope of 1.11 and a Brier score of 0.15; the Hosmer-Lemeshow test yielded a statistically significant p-value of 0.015; and the corresponding ROC AUC was 0.678 (95% CI: 0.636-0.721). The EuroLung2 model displayed better calibration, featuring fewer overlapping points in the calibration plot, with a slope of 1.22, with acceptable discrimination, as indicated by a ROC AUC of 0.756 (95% CI: 0.670-0.842). Both models failed to accurately predict morbidity and mortality outcomes in this specific health care context. Discrepancies between the EuroLung model predictions and outcomes in Brazil underscore the need for model refinement and for a probe into inefficiencies in the Brazilian health care system.

Variational Bayes machine learning for risk adjustment of general outcome indicators with examples in urology

Risk adjustment is often necessary for outcome quality indicators (QIs) to provide fair and accurate feedback to healthcare professionals. However, traditional risk adjustment models are generally oversimplified and not equipped to disentangle complex factors influencing outcomes that are out of a healthcare professional’s control. We present VIRGO, a novel variational Bayes model trained on routinely collected, large administrative datasets to risk-adjust outcome QIs. VIRGO uses detailed demographics, diagnosis, and procedure codes to provide individualized risk adjustment and explanations on patient factors affecting outcomes. VIRGO achieves state-of-the-art on external datasets and features capabilities of uncertainty expression, explainable features, and counterfactual analysis capabilities. VIRGO facilitates risk adjustment by explaining how patient factors led to adverse outcomes and expresses the uncertainty of each prediction, allowing healthcare professionals to not only explore patient factors with unexplained variance that are associated with worse outcomes but also reflect on the quality of their clinical practice.

Adjusting the risk-adjustment: Accounting for variation between organisations in the responsiveness of their expenditure to need

There is concern that basing healthcare budgets on risk adjustment estimates derived from historical utilisation data may reinforce patterns of unmet need. We propose a method to avoid this, based on a measure of how closely local health organisations align resources to the needs of their populations. We refer to this measure as the ‘responsiveness of expenditure to need’ and estimate it using national person-level data on use of acute hospital and secondary mental health services in England. We find large variation in responsiveness in both services and show that higher expenditure responsiveness in mental health is associated with fewer suicides. We then re-estimate the national risk-adjustment model removing the data from the organisations with the lowest expenditure responsiveness to need. As expected, higher need individuals are estimated to have higher expenditure needs when less responsive organisations are removed from the estimation of the risk-adjustment. Removal of organisations with below-average responsiveness results in the neediest deciles of individuals having an extra £163 (7%) annual need for acute hospital care and an additional £79 (27%) annual need for mental health services. The application of this approach to risk adjustment would result in more resources being directed towards organisations serving higher-need populations.

A Framework for the Design of Risk-Adjustment Models in Health care Provider Payment Systems.

Prospective payments for health care providers require adequate risk adjustment (RA) to address systematic variation in patients' health care needs. However, the design of RA for provider payment involves many choices and difficult trade-offs between incentives for risk selection, incentives for cost control, and feasibility. Despite a growing literature, a comprehensive framework of these choices and trade-offs is lacking. This article aims to develop such a framework. Using literature review and expert consultation, we identify key design choices for RA in the context of provider payment and subsequently categorize these choices along two dimensions: (a) the choice of risk adjusters and (b) the choice of payment weights. For each design choice, we provide an overview of options, trade-offs, and key references. By making design choices and associated trade-offs explicit, our framework facilitates customizing RA design to provider payment systems, given the objectives and other characteristics of the context of interest.

A high hospital frailty risk score indicates an increased risk for complications following surgical treatment of proximal humerus fractures

BackgroundApproximately 70 % of proximal humerus fractures (PHF) occur after the age of 60. High complication rates have been described in correlation with the treatment of PHF. Major risk factors for the outcome might be frailty, mobility and comorbidities of patients at the time of hospital admission. The aim of this study was to create risk adjusted quality indicators for surgical treatment of proximal humerus fractures based on German claims data and to evaluate the impact of the Hospital Frailty Risk Score (HFRS) on risk adjustment. MethodsRetrospective claims data (2015–2021) were used to create risk adjusted quality indicators for eight outcomes by clustered multivariable logistic regression. The comparison of different risk adjustment model performances was done by ROC-AUC and Standardized Mortality/Morbidity Ratios. ResultsIn total, N = 34,912 patients (median age 75 years, 80.3 % female) were included. The most common surgical procedure was open reduction and internal fixation with plate osteosynthesis with 39.7 %, followed by reverse shoulder arthroplasty with 25.3 %. The most influential risk factor for all outcomes was a high HFRS with an Odds Ratio of 2.0 (95 %-Confidence Interval 1.8–2.3) for any secondary surgery (365 days) up to an Odds Ratio of 17.6 (95 %-Confidence Interval 14.9–20.8) for general complications during the index stay. ConclusionComparative quality reporting for the surgical treatment of PHF appears feasible with the developed models for risk adjustment using claims data. Preoperative evaluation of HFRS in PHF can contribute to risk assessment, and individual patient management. It therefore enables personalized treatment decisions.

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.

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

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's 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 can aid in fair hospital benchmarking, assessment of temporal changes, and observational causal inference analysis.

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.