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.

Automatic Active Lesion Tracking in Multiple Sclerosis Using Unsupervised Machine Learning.

BACKGROUND: Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods. OBJECTIVE: To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents. MATERIALS AND METHODS: Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images. RESULTS: LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods. CONCLUSIONS: NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool.

Presentation Acuity and Surgical Outcomes for Patients With Health Insurance Living in Highly Deprived Neighborhoods.

Importance: Insurance coverage expansion has been proposed as a solution to improving health disparities, but insurance expansion alone may be insufficient to alleviate care access barriers. Objective: To assess the association of Area Deprivation Index (ADI) with postsurgical textbook outcomes (TO) and presentation acuity for individuals with private insurance or Medicare. Design, Setting, and Participants: This cohort study used data from the National Surgical Quality Improvement Program (2013-2019) merged with electronic health record data from 3 academic health care systems. Data were analyzed from June 2022 to August 2023. Exposure: Living in a neighborhood with an ADI greater than 85. Main Outcomes and Measures: TO, defined as absence of unplanned reoperations, Clavien-Dindo grade 4 complications, mortality, emergency department visits/observation stays, and readmissions, and presentation acuity, defined as having preoperative acute serious conditions (PASC) and urgent or emergent cases. Results: Among a cohort of 29 924 patients, the mean (SD) age was 60.6 (15.6) years; 16 424 (54.9%) were female, and 13 500 (45.1) were male. A total of 14 306 patients had private insurance and 15 618 had Medicare. Patients in highly deprived neighborhoods (5536 patients [18.5%]), with an ADI greater than 85, had lower/worse odds of TO in both the private insurance group (adjusted odds ratio [aOR], 0.87; 95% CI, 0.76-0.99; P = .04) and Medicare group (aOR, 0.90; 95% CI, 0.82-1.00; P = .04) and higher odds of PASC and urgent or emergent cases. The association of ADIs greater than 85 with TO lost significance after adjusting for PASC and urgent/emergent cases. Differences in the probability of TO between the lowest-risk (ADI ≤85, no PASC, and elective surgery) and highest-risk (ADI >85, PASC, and urgent/emergent surgery) scenarios stratified by frailty were highest for very frail patients (Risk Analysis Index ≥40) with differences of 40.2% and 43.1% for those with private insurance and Medicare, respectively. Conclusions and Relevance: This study found that patients living in highly deprived neighborhoods had lower/worse odds of TO and higher presentation acuity despite having private insurance or Medicare. These findings suggest that insurance coverage expansion alone is insufficient to overcome health care disparities, possibly due to persistent barriers to preventive care and other complex causes of health inequities.

Outcomes of Women Undergoing Noncardiac Surgery in Veterans Affairs Compared With Non-Veterans Affairs Care Settings.

Importance: Recent legislation facilitates veterans' ability to receive non-Veterans Affairs (VA) surgical care. Although veterans are predominantly male, the number of women receiving care within the VA has nearly doubled to 10% over the past decade and recent data comparing the surgical care of women in VA and non-VA care settings are lacking. Objective: To compare postoperative outcomes among women treated in VA hospitals vs private-sector hospitals. Design, Setting, and Participants: This coarsened exact-matched cohort study across 9 noncardiac specialties in the Veterans Affairs Surgical Quality Improvement Program (VASQIP) and American College of Surgeons National Surgical Quality Improvement Program (NSQIP) took place from January 1, 2016, to December 31, 2019. Multivariable Poisson models with robust standard errors were used to evaluate the association between VA vs private-sector care settings and 30-day mortality. Hospitals participating in American College of Surgeons NSQIP and VASQIP were included. Data analysis was performed in January 2023. Participants included female patients 18 years old or older. Exposures: Surgical care in VA or private-sector hospitals. Main Outcomes and Measures: Postoperative 30-day mortality and failure to rescue (FTR). Results: Among 1 913 033 procedures analyzed, patients in VASQIP were younger (VASQIP: mean age, 49.8 [SD, 13.0] years; NSQIP: mean age, 55.9 [SD, 16.9] years; P < .001) and although most patients in both groups identified as White, there were significantly more Black women in VASQIP compared with NSQIP (29.6% vs 12.7%; P < .001). The mean risk analysis index score was lower in VASQIP (13.9 [SD, 6.4]) compared with NSQIP (16.3 [SD, 7.8]) (P < .001 for both). Patients in the VA were more likely to have a preoperative acute serious condition (2.4% vs 1.8%: P < .001), but cases in NSQIP were more frequently emergent (6.9% vs 2.6%; P < .001). The 30-day mortality, complications, and FTR were 0.2%, 3.2%, and 0.1% in VASQIP (n = 36 762 procedures) as compared with 0.8%, 5.0%, and 0.5% in NSQIP (n = 1 876 271 procedures), respectively (all P < .001). Among 1 763 540 matched women (n = 36 478 procedures in VASQIP; n = 1 727 062 procedures in NSQIP), these rates were 0.3%, 3.7%, and 0.2% in NSQIP and 0.1%, 3.4%, and 0.1% in VASQIP (all P < .01). Relative to private-sector care, VA surgical care was associated with a lower risk of death (adjusted risk ratio [aRR], 0.41; 95% CI, 0.23-0.76). This finding was robust among women undergoing gynecologic surgery, inpatient surgery, and low-physiologic stress procedures. VA surgical care was also associated with lower risk of FTR (aRR, 0.41; 95% CI, 0.18-0.92) for frail or Black women and inpatient and low-physiologic stress procedures. Conclusions and Relevance: Although women comprise the minority of veterans receiving care within the VA, in this study, VA surgical care for women was associated with half the risk of postoperative death and FTR. The VA appears better equipped to meet the unique surgical needs and risk profiles of veterans, regardless of sex and health policy decisions, including funding, should reflect these important outcome differences.

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

A Surgical Desirability of Outcome Ranking (DOOR) Reveals Complex Relationships Between Race/Ethnicity, Insurance Type, and Neighborhood Deprivation.

OBJECTIVE: Develop an ordinal Desirability of Outcome Ranking (DOOR) for surgical outcomes to examine complex associations of Social Determinants of Health. BACKGROUND: Studies focused on single or binary composite outcomes may not detect health disparities. METHODS: Three health care system cohort study using NSQIP (2013-2019) linked with EHR and risk-adjusted for frailty, preoperative acute serious conditions (PASC), case status and operative stress assessing associations of multilevel Social Determinants of Health of race/ethnicity, insurance type (Private 13,957; Medicare 15,198; Medicaid 2835; Uninsured 2963) and Area Deprivation Index (ADI) on DOOR and the binary Textbook Outcomes (TO). RESULTS: Patients living in highly deprived neighborhoods (ADI>85) had higher odds of PASC [adjusted odds ratio (aOR)=1.13, CI=1.02-1.25, P <0.001] and urgent/emergent cases (aOR=1.23, CI=1.16-1.31, P <0.001). Increased odds of higher/less desirable DOOR scores were associated with patients identifying as Black versus White and on Medicare, Medicaid or Uninsured versus Private insurance. Patients with ADI>85 had lower odds of TO (aOR=0.91, CI=0.85-0.97, P =0.006) until adjusting for insurance. In contrast, patients with ADI>85 had increased odds of higher DOOR (aOR=1.07, CI=1.01-1.14, P <0.021) after adjusting for insurance but similar odds after adjusting for PASC and urgent/emergent cases. CONCLUSIONS: DOOR revealed complex interactions between race/ethnicity, insurance type and neighborhood deprivation. ADI>85 was associated with higher odds of worse DOOR outcomes while TO failed to capture the effect of ADI. Our results suggest that presentation acuity is a critical determinant of worse outcomes in patients in highly deprived neighborhoods and without insurance. Including risk adjustment for living in deprived neighborhoods and urgent/emergent surgeries could improve the accuracy of quality metrics.

A Multidimensional Connectomics- and Radiomics-Based Advanced Machine-Learning Framework to Distinguish Radiation Necrosis from True Progression in Brain Metastases.

We introduce tumor connectomics, a novel MRI-based complex graph theory framework that describes the intricate network of relationships within the tumor and surrounding tissue, and combine this with multiparametric radiomics (mpRad) in a machine-learning approach to distinguish radiation necrosis (RN) from true progression (TP). Pathologically confirmed cases of RN vs. TP in brain metastases treated with SRS were included from a single institution. The region of interest was manually segmented as the single largest diameter of the T1 post-contrast (T1C) lesion plus the corresponding area of T2 FLAIR hyperintensity. There were 40 mpRad features and 6 connectomics features extracted, as well as 5 clinical and treatment factors. We developed an Integrated Radiomics Informatics System (IRIS) based on an Isomap support vector machine (IsoSVM) model to distinguish TP from RN using leave-one-out cross-validation. Class imbalance was resolved with differential misclassification weighting during model training using the IRIS. In total, 135 lesions in 110 patients were analyzed, including 43 cases (31.9%) of pathologically proven RN and 92 cases (68.1%) of TP. The top-performing connectomics features were three centrality measures of degree, betweenness, and eigenvector centralities. Combining these with the 10 top-performing mpRad features, an optimized IsoSVM model was able to produce a sensitivity of 0.87, specificity of 0.84, AUC-ROC of 0.89 (95% CI: 0.82-0.94), and AUC-PR of 0.94 (95% CI: 0.87-0.97).

Automated Lung Ultrasound Pulmonary Disease Quantification Using an Unsupervised Machine Learning Technique for COVID-19.

COVID-19 is an ongoing global health pandemic. Although COVID-19 can be diagnosed with various tests such as PCR, these tests do not establish pulmonary disease burden. Whereas point-of-care lung ultrasound (POCUS) can directly assess the severity of characteristic pulmonary findings of COVID-19, the advantage of using US is that it is inexpensive, portable, and widely available for use in many clinical settings. For automated assessment of pulmonary findings, we have developed an unsupervised learning technique termed the calculated lung ultrasound (CLU) index. The CLU can quantify various types of lung findings, such as A or B lines, consolidations, and pleural effusions, and it uses these findings to calculate a CLU index score, which is a quantitative measure of pulmonary disease burden. This is accomplished using an unsupervised, patient-specific approach that does not require training on a large dataset. The CLU was tested on 52 lung ultrasound examinations from several institutions. CLU demonstrated excellent concordance with radiologist findings in different pulmonary disease states. Given the global nature of COVID-19, the CLU would be useful for sonographers and physicians in resource-strapped areas with limited ultrasound training and diagnostic capacities for more accurate assessment of pulmonary status.

Association of Insurance Type with Inpatient Surgical 30-day Readmissions, Emergency Department Visits/Observation Stays, and Costs.

OBJECTIVE: To assess the association of Private, Medicare, and Medicaid/Uninsured insurance type with 30-day Emergency Department visits/Observation Stays (EDOS), readmissions, and costs in a safety-net hospital (SNH) serving diverse socioeconomic status patients. SUMMARY BACKGROUND DATA: Medicare's Hospital Readmission Reduction Program (HRRP) disproportionately penalizes SNHs. METHODS: This retrospective cohort study used inpatient National Surgical Quality Improvement Program (2013-2019) data merged with cost data. Frailty, expanded Operative Stress Score, case status, and insurance type were used to predict odds of EDOS and readmissions, as well as index hospitalization costs. RESULTS: The cohort had 1,477 Private; 1,164 Medicare; and 3,488 Medicaid/Uninsured cases with a patient mean age 52.1 years [SD=14.7] and 46.8% of the cases were performed on male patients. Medicaid/Uninsured (aOR=2.69, CI=2.38-3.05, P<.001) and Medicare (aOR=1.32, CI=1.11-1.56, P=.001) had increased odds of urgent/emergent surgeries and complications versus Private patients. Despite having similar frailty distributions, Medicaid/Uninsured compared to Private patients had higher odds of EDOS (aOR=1.71, CI=1.39-2.11, P<.001), and readmissions (aOR=1.35, CI=1.11-1.65, P=.004), after adjusting for frailty, OSS, and case status, while Medicare patients had similar odds of EDOS and readmissions versus Private. Hospitalization variable cost %change was increased for Medicare (12.5%) and Medicaid/Uninsured (5.9%), but Medicaid/Uninsured was similar to Private after adjusting for urgent/emergent cases. CONCLUSIONS: Increased rates and odds of urgent/emergent cases in Medicaid/Uninsured patients drive increased odds of complications and index hospitalization costs versus Private. SNHs care for higher cost populations while receiving lower reimbursements and are further penalized by the unintended consequences of HRRP. Increasing access to care, especially for Medicaid/Uninsured patients, could reduce urgent/emergent surgeries resulting in fewer complications, EDOS/readmissions, and costs.

Independent Associations of Neighborhood Deprivation and Patient-level Social Determinants of Health with Textbook Outcomes after Inpatient Surgery.

Objective: Assess associations of Social Determinants of Health (SDoH) using Area Deprivation Index (ADI), race/ethnicity and insurance type with Textbook Outcomes (TO). Summary Background Data: Individual- and contextual-level SDoH affect health outcomes, but only one SDoH level is usually included. Methods: Three healthcare system cohort study using National Surgical Quality Improvement Program (2013-2019) linked with ADI risk-adjusted for frailty, case status and operative stress examining TO/TO components (unplanned reoperations, complications, mortality, Emergency Department/Observation Stays and readmissions). Results: Cohort (34,251 cases) mean age 58.3 [SD=16.0], 54.8% females, 14.1% Hispanics, 11.6% Non-Hispanic Blacks, 21.6% with ADI>85, and 81.8% TO. Racial and ethnic minorities, non-Private insurance, and ADI>85 patients had increased odds of urgent/emergent surgeries (aORs range: 1.17-2.83, all P<.001). Non-Hispanic Black patients, ADI>85 and non-Private insurances had lower TO odds (aORs range: 0.55-0.93, all P<.04), but ADI>85 lost significance after including case status. Urgent/emergent versus elective had lower TO odds (aOR=0.51, P<.001). ADI>85 patients had higher complication and mortality odds. Estimated reduction in TO probability was 9.9% (CI=7.2%-12.6%) for urgent/emergent cases, 7.0% (CI=4.6%-9.3%) for Medicaid, and 1.6% (CI=0.2%-3.0%) for non-Hispanic Black patients. TO probability difference for lowest-risk (White-Private-ADI≤85-elective) to highest-risk (Black-Medicaid-ADI>85-urgent/emergent) was 29.8% for very frail patients. Conclusion: Multi-level SDoH had independent effects on TO, predominately affecting outcomes through increased rates/odds of urgent/emergent surgeries driving complications and worse outcomes. Lowest-risk versus highest-risk scenarios demonstrated the magnitude of intersecting SDoH variables. Combination of insurance type and ADI should be used to identify high-risk patients to redesign care pathways to improve outcomes. Risk adjustment including contextual neighborhood deprivation and patient-level SDoH could reduce unintended consequences of value-based programs.

Who is afraid of Hermy and Jimmy? Relating to and normalizing psychosis through theater.

OBJECTIVE: Psychotic experiences, such as hearing voices that others do not hear, being afraid of threats that others do not perceive, or believing in ideas that others find implausible can be confusing for those who face them and challenging to relate to for those who do not, leading to alienation and social exclusion. The objective of this article is to discuss how immersion in theater can enhance our understanding of human nature and facilitate a social environment that supports the recovery of individuals with psychosis. METHODS: Drawing on theories of the psychology of art and narrative psychology, this conceptual article discusses a theatrical production, a play, titled "Voices," created by a person with lived experience of voice hearing. We apply Semenov's model of art as a social psychological system as a guiding framework to focus on the roles of the art product, artist-author, artist-performer, and recipient. RESULTS: Theater is a uniquely reciprocal art form where actors and spectators share emotional, intellectual, and cathartic experiences, which could foster interpersonal connection, personal growth, and empathy. This article brings new perspective on how theater can elucidate psychotic experiences, encourage dialogue about these experiences, and facilitate social integration and recovery of individuals living with psychosis. CONCLUSION AND IMPLICATIONS FOR PRACTICE: Theater can promote social change, making space for a wider range of perspectives in society. Engaging individuals with lived experiences of psychosis in theatrical productions could lead to new insights about and acceptance of psychotic experiences, both for these individuals and for society at large. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Differentiating Urgent from Elective Cases Matters in Minority Populations: Developing an Ordinal "Desirability of Outcome Ranking" to Increase Granularity and Sensitivity of Surgical Outcomes Assessment.

BACKGROUND: Surgical analyses often focus on single or binary outcomes; we developed an ordinal Desirability of Outcome Ranking (DOOR) for surgery to increase granularity and sensitivity of surgical outcome assessments. Many studies also combine elective and urgent procedures for risk adjustment. We used DOOR to examine complex associations of race/ethnicity and presentation acuity. STUDY DESIGN: NSQIP (2013 to 2019) cohort study assessing DOOR outcomes across race/ethnicity groups risk-adjusted for frailty, operative stress, preoperative acute serious conditions, and elective, urgent, and emergent cases. RESULTS: The cohort included 1,597,199 elective, 340,350 urgent, and 185,073 emergent cases with patient mean age of 60.0 ± 15.8, and 56.4% of the surgeries were performed on female patients. Minority race/ethnicity groups had increased odds of presenting with preoperative acute serious conditions (adjusted odds ratio [aORs] range 1.22 to 1.74), urgent (aOR range 1.04 to 2.21), and emergent (aOR range 1.15 to 2.18) surgeries vs the White group. Black (aOR range 1.23 to 1.34) and Native (aOR range 1.07 to 1.17) groups had increased odds of higher/worse DOOR outcomes; however, the Hispanic group had increased odds of higher/worse DOOR (aOR 1.11, CI 1.10 to 1.13), but decreased odds (aORs range 0.94 to 0.96) after adjusting for case status; the Asian group had better outcomes vs the White group. DOOR outcomes improved in minority groups when using elective vs elective/urgent cases as the reference group. CONCLUSIONS: NSQIP surgical DOOR is a new method to assess outcomes and reveals a complex interplay between race/ethnicity and presentation acuity. Combining elective and urgent cases in risk adjustment may penalize hospitals serving a higher proportion of minority populations. DOOR can be used to improve detection of health disparities and serves as a roadmap for the development of other ordinal surgical outcomes measures. Improving surgical outcomes should focus on decreasing preoperative acute serious conditions and urgent and emergent surgeries, possibly by improving access to care, especially for minority populations.

Cost of Failure to Achieve Textbook Outcomes: Association of Insurance Type with Outcomes and Cumulative Cost for Inpatient Surgery.

BACKGROUND: Surgical outcome/cost analyses typically focus on single outcomes and do not include encounters beyond the index hospitalization. STUDY DESIGN: This cohort study used NSQIP (2013-2019) data with electronic health record and cost data risk-adjusted for frailty, preoperative acute serious conditions (PASC), case status, and operative stress assessing cumulative costs of failure to achieve textbook outcomes defined as absence of 30-day Clavien-Dindo level III and IV complications, emergency department visits/observation stays (EDOS), and readmissions across insurance types (private, Medicare, Medicaid, uninsured). Return costs were defined as costs of all 30-day emergency department visits/observation stays and readmissions. RESULTS: Cases were performed on patients (private 1,506; Medicare 1,218; Medicaid 1,420; uninsured 2,178) with a mean age 52.3 years (SD 14.7) and 47.5% male. Medicaid and uninsured patients had higher odds of presenting with preoperative acute serious conditions (adjusted odds ratios 1.89 and 1.81, respectively) and undergoing urgent/emergent surgeries (adjusted odds ratios 2.23 and 3.02, respectively) vs private. Medicaid and uninsured patients had lower odds of textbook outcomes (adjusted odds ratios 0.53 and 0.78, respectively) and higher odds of emergency department visits/observation stays and readmissions vs private. Not achieving textbook outcomes was associated with a greater than 95.1% increase in cumulative costs. Medicaid patients had a relative increase of 23.1% in cumulative costs vs private, which was 18.2% after adjusting for urgent/emergent cases. Return costs were 37.5% and 65.8% higher for Medicaid and uninsured patients, respectively, vs private. CONCUSIONS: Higher costs for Medicaid patients were partially driven by increased presentation acuity (increased rates/odds of preoperative acute serious conditions and urgent/emergent surgeries) and higher rates of multiple emergency department visits/observation stays and readmission occurrences. Decreasing surgical costs/improving outcomes should focus on reducing urgent/emergent surgeries and improving postoperative care coordination, especially for Medicaid and uninsured populations.

Association of Cumulative Colorectal Surgery Hospital Costs, Readmissions, and Emergency Department/Observation Stays with Insurance Type.

BACKGROUND/PURPOSE: Medicare's Hospital Readmission Reduction Program disproportionately penalizes safety-net hospitals (SNH) caring for vulnerable populations. This study assessed the association of insurance type with 30-day emergency department visits/observation stays (EDOS), readmissions, and cumulative costs in colorectal surgery patients. METHODS: Retrospective inpatient cohort study using the National Surgical Quality Improvement Program (2013-2019) with cost data in a SNH. The odds of EDOS and readmissions and cumulative variable (index hospitalization and all 30-day EDOS and readmissions) costs were modeled adjusting for frailty, case status, presence of a stoma, and open versus laparoscopic surgery. RESULTS: The cohort had 245 private, 195 Medicare, and 590 Medicaid/uninsured cases, with a mean age 55.0 years (SD = 13.3) and 52.9% of the cases were performed on male patients. Most cases were open surgeries (58.7%). Complication rates were 41.8%, EDOS 12.0%, and readmissions 20.1%. Medicaid/uninsured had increased odds of urgent/emergent surgeries (aOR = 2.15, CI = 1.56-2.98, p < 0.001) and complications (aOR = 1.43, CI = 1.02-2.03, p = 0.042) versus private patients. Medicaid/uninsured versus private patients had higher EDOS (16.6% versus 4.1%) and readmissions (22.9% versus 14.3%) rates and higher odds of EDOS (aOR = 4.81, CI = 2.57-10.06, p < 0.001), and readmissions (aOR = 1.62, CI = 1.07-2.50, p = 0.025), while Medicare patients had similar odds versus private. Cumulative variable cost %change was increased for Medicare and Medicaid/uninsured, but Medicaid/uninsured was similar to private after adjusting for urgent/emergent cases. CONCLUSIONS: Increased urgent/emergent cases in Medicaid/uninsured populations drive increased complications odds and higher costs compared to private patients, suggesting lack of access to outpatient care. SNH care for higher cost populations, receive lower reimbursements, and are penalized by value-based programs. Increasing healthcare access for Medicaid/uninsured patients could reduce urgent/emergent surgeries, resulting in fewer complications, EDOS/readmissions, and costs.

Sex-Related Differences in Acuity and Postoperative Complications, Mortality and Failure to Rescue.

INTRODUCTION: Yentl syndrome describing sex-related disparities has been extensively studied in medical conditions but not after surgery. This retrospective cohort study assessed the association of sex, frailty, presenting with preoperative acute serious conditions (PASC), and the expanded Operative Stress Score (OSS) with postoperative complications, mortality, and failure-to-rescue. METHODS: The National Surgical Quality Improvement Program from 2015 to 2019 evaluating 30-d complications, mortality, and failure-to-rescue. RESULTS: Of 4,860,308 cases (43% were male; mean [standard deviation] age of 56 [17] y), 6.0 and 0.8% were frail and very frail, respectively. Frailty score distribution was higher in men versus women (P < 0.001). Most cases were low-stress OSS2 (44.9%) or moderate-stress OSS3 (44.5%) surgeries. While unadjusted 30-d mortality rates were higher (P < 0.001) in males (1.1%) versus females (0.8%), males had lower odds of mortality (adjusted odds ratio (aOR) = 0.92, 95% confidence interval [CI] = 0.90-0.94, P < 0.001) after adjusting for frailty, OSS, case status, PASC, and Clavien-Dindo IV (CDIV) complications. Males have higher odds of PASC (aOR = 1.33, CI = 1.31-1.35, P < 0.001) and CDIV complications (aOR = 1.13, CI = 1.12-1.15, P < 0.001). Male-PASC (aOR = 0.76, CI = 0.72-0.80, P < 0.001) and male-CDIV (aOR = 0.87, CI = 0.83-0.91, P < 0.001) interaction terms demonstrated that the increased odds of mortality associated with PASC or CDIV complications/failure-to-rescue were lower in males versus females. CONCLUSIONS: Our study provides a comprehensive analysis of sex-related surgical outcomes across a wide range of procedures and health care systems. Females presenting with PASC or experiencing CDIV complications had higher odds of mortality/failure to rescue suggesting sex-related care differences. Yentl syndrome may be present in surgical patients; possibly related to differences in presenting symptoms, patient care preferences, or less aggressive care in female patients and deserves further study.

Retrospective cohort study comparing surgical inpatient charges, total costs, and variable costs as hospital cost savings measures.

We analyzed differences (charges, total, and variable costs) in estimating cost savings of quality improvement projects using reduction of serious/life-threatening complications (Clavien-Dindo Level IV) and insurance type (Private, Medicare, and Medicaid/Uninsured) to evaluate the cost measures. Multiple measures are used to analyze hospital costs and compare cost outcomes across health systems with differing patient compositions. We used National Surgical Quality Improvement Program inpatient (2013-2019) with charge and cost data in a hospital serving diverse socioeconomic status patients. Simulation was used to estimate variable costs and total costs at 3 proportions of fixed costs (FC). Cases (Private 1517; Medicare 1224; Medicaid/Uninsured 3648) with patient mean age 52.3 years (Standard Deviation = 14.7) and 47.3% male. Medicare (adjusted odds ratio = 1.55, 95% confidence interval = 1.16-2.09, P = .003) and Medicaid/Uninsured (adjusted odds ratio = 1.41, 95% confidence interval = 1.10-1.82, P = .008) had higher odds of complications versus Private. Medicaid/Uninsured had higher relative charges versus Private, while Medicaid/Uninsured and Medicare had higher relative variable and total costs versus Private. Targeting a 15% reduction in serious complications for robust patients undergoing moderate-stress procedures estimated variable cost savings of $286,392. Total cost saving estimates progressively increased with increasing proportions of FC; $443,943 (35% FC), $577,495 (50% FC), and $1184,403 (75% FC). In conclusion, charges did not identify increased costs for Medicare versus Private patients. Complications were associated with > 200% change in costs. Surgical hospitalizations for Medicare and Medicaid/Uninsured patients cost more than Private patients. Variable costs should be used to avoid overestimating potential cost savings of quality improvement interventions, as total costs include fixed costs that are difficult to change in the short term.

Lung Cancer Recurrence Risk Prediction through Integrated Deep Learning Evaluation.

Background: Prognostic risk factors for completely resected stage IA non-small-cell lung cancers (NSCLCs) have advanced minimally over recent decades. Although several biomarkers have been found to be associated with cancer recurrence, their added value to TNM staging and tumor grade are unclear. Methods: Features of preoperative low-dose CT image and histologic findings of hematoxylin- and eosin-stained tissue sections of resected lung tumor specimens were extracted from 182 stage IA NSCLC patients in the National Lung Screening Trial. These features were combined to predict the risk of tumor recurrence or progression through integrated deep learning evaluation (IDLE). Added values of IDLE to TNM staging and tumor grade in progression risk prediction and risk stratification were evaluated. Results: The 5-year AUC of IDLE was 0.817 ± 0.037 as compared to the AUC = 0.561 ± 0.042 and 0.573 ± 0.044 from the TNM stage and tumor grade, respectively. The IDLE score was significantly associated with cancer recurrence (p < 0.0001) even after adjusting for TNM staging and tumor grade. Synergy between chest CT image markers and histological markers was the driving force of the deep learning algorithm to produce a stronger prognostic predictor. Conclusions: Integrating markers from preoperative CT images and pathologist's readings of resected lung specimens through deep learning can improve risk stratification of stage 1A NSCLC patients over TNM staging and tumor grade alone. Our study suggests that combining markers from nonoverlapping platforms can increase the cancer risk prediction accuracy.

Radiomic Analysis: Study Design, Statistical Analysis, and Other Bias Mitigation Strategies.

Rapid advances in automated methods for extracting large numbers of quantitative features from medical images have led to tremendous growth of publications reporting on radiomic analyses. Translation of these research studies into clinical practice can be hindered by biases introduced during the design, analysis, or reporting of the studies. Herein, the authors review biases, sources of variability, and pitfalls that frequently arise in radiomic research, with an emphasis on study design and statistical analysis considerations. Drawing on existing work in the statistical, radiologic, and machine learning literature, approaches for avoiding these pitfalls are described.

Tumor Connectomics: Mapping the Intra-Tumoral Complex Interaction Network Using Machine Learning.

The high-level relationships that form complex networks within tumors and between surrounding tissue is challenging and not fully understood. To better understand these tumoral networks, we developed a tumor connectomics framework (TCF) based on graph theory with machine learning to model the complex interactions within and around the tumor microenvironment that are detectable on imaging. The TCF characterization model was tested with independent datasets of breast, brain, and prostate lesions with corresponding validation datasets in breast and brain cancer. The TCF network connections were modeled using graph metrics of centrality, average path length (APL), and clustering from multiparametric MRI with IsoSVM. The Matthews Correlation Coefficient (MCC), Area Under the Curve-ROC, and Precision-Recall (AUC-ROC and AUC-PR) were used for statistical analysis. The TCF classified the breast and brain tumor cohorts with an IsoSVM AUC-PR and MCC of 0.86, 0.63 and 0.85, 0.65, respectively. The TCF benign breast lesions had a significantly higher clustering coefficient and degree centrality than malignant TCFs. Grade 2 brain tumors demonstrated higher connectivity compared to Grade 4 tumors with increased degree centrality and clustering coefficients. Gleason 7 prostate lesions had increased betweenness centrality and APL compared to Gleason 6 lesions with AUC-PR and MCC ranging from 0.90 to 0.99 and 0.73 to 0.87, respectively. These TCF findings were similar in the validation breast and brain datasets. In conclusion, we present a new method for tumor characterization and visualization that results in a better understanding of the global and regional connections within the lesion and surrounding tissue.