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This 14-state study compared mortality rates at hospitals with a level 1 trauma center and hospitals without a trauma center among patients with moderate-to-severe injuries. After adjustment for baseline differences in patients' characteristics, the one-year mortality rate was significantly lower at trauma centers than at non–trauma centers (10 percent vs. 14 percent). This 14-state study compared mortality rates at hospitals with a level 1 trauma center and hospitals without a trauma center among patients with moderate-to-severe injuries. The one-year mortality rate was significantly lower at trauma centers than at non–trauma centers (10 percent vs. 14 percent). In 1976, the American College of Surgeons Committee on Trauma published criteria for categorizing hospitals according to the resources required to provide various levels of care for traumatic injuries. 1 Increasingly, states are using these criteria as a basis for designating trauma centers as part of a regionalized approach to trauma care. 2 However, this process has not been uniform. There is substantial variation across states in the number and geographic distribution of trauma centers, 2 – 4 owing in part to the lack of strong evidence of the effectiveness of trauma centers coupled with high costs of verifying their capabilities. 5 The existing evidence . . .

Background There has been an increasing interest in learning low-dimensional vector representations of medical concepts from Electronic Health Records (EHRs). Vector representations of medical concepts facilitate exploratory analysis and predictive modeling of EHR data to gain insights about the patterns of care and health outcomes. EHRs contain structured data such as diagnostic codes and laboratory tests, as well as unstructured free text data in form of clinical notes, which provide more detail about condition and treatment of patients. Methods In this work, we propose a method that jointly learns vector representations of medical concepts and words. This is achieved by a novel learning scheme based on the word2vec model. Our model learns those relationships by integrating clinical notes and sets of accompanying medical codes and by defining joint contexts for each observed word and medical code. Results In our experiments, we learned joint representations using MIMIC-III data. Using the learned representations of words and medical codes, we evaluated phenotypes for 6 diseases discovered by our and baseline method. The experimental results show that for each of the 6 diseases our method finds highly relevant words. We also show that our representations can be very useful when predicting the reason for the next visit. Conclusions The jointly learned representations of medical concepts and words capture not only similarity between codes or words themselves, but also similarity between codes and words. They can be used to extract phenotypes of different diseases. The representations learned by the joint model are also useful for construction of patient features.

Desmoplasia, a fibrotic mass including cancer-associated fibroblasts (CAFs) and self-sustaining extracellular matrix (D-ECM), is a puzzling feature of pancreatic ductal adenocarcinoma (PDACs). Conflicting studies have identified tumor-restricting and tumor-promoting roles of PDAC-associated desmoplasia, suggesting that individual CAF/D-ECM protein constituents have distinguishable tumorigenic and tumor-repressive functions. Using 3D culture of normal pancreatic versus PDAC-associated human fibroblasts, we identified a CAF/D-ECM phenotype that correlates with improved patient outcomes, and that includes CAFs enriched in plasma membrane-localized, active α 5 β 1 -integrin. Mechanistically, we established that TGFβ is required for D-ECM production but dispensable for D-ECM-induced naïve fibroblast-to-CAF activation, which depends on α v β 5 -integrin redistribution of pFAK-independent active α 5 β 1 -integrin to assorted endosomes. Importantly, the development of a simultaneous multi-channel immunofluorescence approach and new algorithms for computational batch-analysis and their application to a human PDAC panel, indicated that stromal localization and levels of active SMAD2/3 and α 5 β 1 -integrin distinguish patient-protective from patient-detrimental desmoplasia and foretell tumor recurrences, suggesting a useful new prognostic tool. Tumors are not entirely made out of cancerous cells. They contain many other components – referred to as tumor stroma – that may either encourage or hinder the tumor’s growth. Tumor stroma includes non-cancerous cells and a framework of fibrous sugary proteins, called the extracellular matrix, which surround and signal to cells while providing physical support. In the most common and aggressive form of pancreatic cancer, the stroma often makes up the majority of the tumor’s mass. Sometimes the stroma of these pancreatic tumors can protect the cancer cells from anti-cancer drugs. Researchers have therefore been interested in finding out exactly which aspects of the tumor stroma shield and support cancer cells, and which impede their growth and progression. Answering these questions could make it possible to develop new drugs that will change a tumor-supporting stroma into one that hinders the tumor’s growth and spread. The most abundant cells in the stroma of pancreatic tumors are called cancer-associated fibroblasts. Healthy specialized fibroblasts – known as pancreatic stellate cells – help to build and maintain the ‘normal’ extracellular matrix and so these cells normally restrict a tumor’s development. However, cancer cells can adapt healthy fibroblasts into cancer-associated fibroblasts, which produce an altered extracellular matrix that could allow the tumor to grow. Franco-Barraza et al. have now compared healthy and cancer-associated fibroblasts from patients’ pancreatic tumors. One of the main differences between these two cell types was the location of the activated form of a molecule called α 5 β 1 -integrin. Healthy fibroblasts, in a normal extracellular matrix, have active α 5 β 1 -integrin on the surface of the cell. However, a number of tumor-promoting signals, including some from the altered extracellular matrix, could force the active α 5 β 1 -integrins to relocate inside the fibroblasts instead. In further experiments, where the activated integrin was retained at the cell surface, the fibroblasts were able to resist the influence of the cancer-associated extracellular matrix. Then again, if the active α 5 β 1 -integrins were directed inside the cells, healthy cells turned into cancer-associated fibroblasts. With this information in hand, Franco-Barraza et al. examined tumor samples from over a hundred pancreatic cancer patients using a new microscopy-based technique that distinguishes cancer cells from stroma cells. The analysis confirmed the pattern observed in the laboratory: those patients who appeared to produce more normal extracellular matrix and have active α 5 β 1 -integrin localized mostly to the surface of the cells survived longer without the cancer returning than those patients who lacked these stroma traits. Samples from patients with kidney cancer also showed similar results and, as before, an altered extracellular matrix was linked to a worse outcome of the disease. Together these findings suggest that if future studies uncover ways to relocate or maintain active α 5 β 1 -integrin to the cell surface of fibroblasts they could lead to new treatments to restrict the growth of tumors in cancer patients.

Background Aggressive treatment with multimodal therapies such as surgery, chemotherapy, and radiation therapy has improved survival in head and neck cancer (HNC) but at a human cost of a substantial symptom burden and impact on quality of life. We developed the NYU Electronic Patient Visit Assessment (ePVA) © for HNC as a digital patient-reported symptom monitoring system that enables early symptom detection and real-time interventions at the point of care. With this study protocol, we aim to test the effectiveness of the ePVA in improving HNC outcomes in real-world settings and to identify implementation strategies optimizing its effectiveness. Methods We will conduct a longitudinal mixed-methods hybrid type I study at four National Cancer Institute-designated Comprehensive Cancer Centers serving diverse populations in rural and urban settings (New York University, the University of Kansas Cancer Center, Fox Chase Cancer Center, and Baylor College of Medicine) guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Patient eligibility criteria include having histologically diagnosed HNC and undergoing radiation therapy with or without chemotherapy for curative intent. We will also interview clinicians caring for patients with HNC at the participating institutions regarding facilitators and barriers to implementing the ePVA. The accrual goal is 270 patients. Aim 1 is to determine the effect of the ePVA on HNC symptoms in a two-arm (usual care vs. ePVA + usual care) trial. The study’s primary outcomes are patients’ self-reported social function, senses of taste and smell, and swallowing, measured by the European Organization for Research and Treatment of Cancer QLQ-C30 and QLQ-H&N35. For Aim 2, we will interview patients ( n  = 40) as well as clinicians ( n  = 30) caring for patients with HNC at the participating institutions regarding facilitators and barriers to implementing the ePVA. In Aim 3, we will integrate Aims 1 and 2 data to identify strategies that optimize the use of the ePVA. Discussion The overarching goal of this research is to advance cancer care by identifying implementation standards for effective, widespread use of the ePVA that apply to all patient-reported outcomes in cancer care. Trial registration ClinicalTrials.gov NCT06030011. Registered on 8 September 2023.

Background: When making treatment decisions, cancer patients must make trade-offs among efficacy, toxicity, and cost. However, little is known about what patient characteristics may influence these trade-offs. Methods: A total of 400 cancer patients reviewed 2 of 3 stylized curative and noncurative scenarios that asked them to choose between 2 treatments of varying levels of efficacy, toxicity, and cost. Each scenario included 9 choice sets. Demographics, cost concerns, numeracy, and optimism were assessed. Within each scenario, we used latent class methods to distinguish groups with discrete preferences. We then used regressions with group membership probabilities as covariates to identify associations. Results: The median age of the patients was 61 years (range, 27-90 y). Of the total number of patients included, 25% were enrolled at a community hospital, and 99% were insured. Three latent classes were identified that demonstrated (1) preference for survival, (2) aversion to high cost, and (3) aversion to toxicity. Across all scenarios, patients with higher income were more likely to be in the class that favored survival. Lower income patients were more likely to be in the class that was averse to high cost (P< 0.05). Similar associations were found between education, employment status, numeracy, cost concerns, and latent class. Conclusions: Even in these stylized scenarios, socioeconomic status predicted the treatment choice. Higher income patients may be more likely to focus on survival, whereas those of lower socioeconomic status may be more likely to avoid expensive treatment, regardless of survival or toxicity. This raises the possibility that insurance plans with greater cost-sharing may have the unintended consequence of increasing disparities in cancer care.

BACKGROUND:Comorbidity adjustment is an important component of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. We examined the conditions under which individual versus summary measures are most appropriate. METHODS:We provide an analytic proof of the utility of comorbidity summary measures when used in place of individual comorbidities. We compared the use of the Charlson and Elixhauser scores versus individual comorbidities in prognostic models using a SEER-Medicare data example. We examined the ability of summary comorbidity measures to adjust for confounding using simulations. RESULTS:We devised a mathematical proof that found that the comorbidity summary measures are appropriate prognostic or adjustment mechanisms in survival analyses. Once one knows the comorbidity score, no other information about the comorbidity variables used to create the score is generally needed. Our data example and simulations largely confirmed this finding. CONCLUSIONS:Summary comorbidity measures, such as the Charlson Comorbidity Index and Elixhauser scores, are commonly used for clinical prognosis and comorbidity adjustment. We have provided a theoretical justification that validates the use of such scores under many conditions. Our simulations generally confirm the utility of the summary comorbidity measures as substitutes for use of the individual comorbidity variables in health services research. One caveat is that a summary measure may only be as good as the variables used to create it.

Background Rates of cervical cancer incidence and mortality are persistently higher among Latina women in the continental United States (US) and women in Puerto Rico (a US territory) compared with non-Hispanic White (NHW) women. Multiple factors contribute to low participation in cancer screening, including structural barriers (e.g., low access to healthcare services, racism/discrimination, lack of culturally and linguistically adequate information), cultural concerns, and low perceived risk and awareness of cervical cancer. Although community-based education and navigation support can be effective in overcoming some barriers to screening, structural barriers and limited access remain formidable challenges to overcome. Emerging technologies supporting self-sampling for high-risk human papillomavirus (HPV) testing may offer a valuable evidence-based strategy for empowering Latina women to engage in cervical cancer screening. Thus, the objective of this study is to assess the feasibility and acceptability of a novel HPV self-sampling intervention for underscreened Latina women. Methods The study will be a randomized controlled feasibility trial involving 100 Latina women who have not received cervical cancer screening within the recommended guidelines. Participants will be randomly assigned to the intervention condition, which includes a synchronous three-session group cervical cancer educational program delivered virtually along with a mailed HPV self-sampling kit (to obtain self-collected cervical samples for HPV testing), or to a comparison condition that involves receipt of the mailed HPV self-sampling kit with written information about cervical cancer screening and nearby clinics. Study assessments will be obtained at baseline (i.e., study entry) and 1-month post-program. The primary outcome of feasibility will be measured through study enrollment and intervention completion. In addition, acceptability of study materials and the self-sampling procedures will be assessed using self-report surveys at 1-month post-program. Discussion Provision of a mailed HPV self-sampling kit may present new options for encouraging participation in cervical cancer screening among underscreened Latina women. This study will evaluate the feasibility and acceptability of such an approach, which will inform the subsequent design of a full-scale randomized trial to assess intervention effectiveness on screening behavior. Trial registration ClinicalTrials.gov no. NCT06439706. Registered 28 May 2024 — retrospectively registered.

Background Additional evaluations, including second opinions, before breast cancer surgery may improve care, but may cause detrimental treatment delays that could allow disease progression. Aims We investigate the timing of surgical delays that are associated with survival benefits conferred by preoperative encounters versus the timing that are associated with potential harm. Methods and results We investigated survival outcomes of SEER Medicare patients with stage 1–3 breast cancer using propensity score‐based weighting. We examined interactions between the number of preoperative evaluation components and time from biopsy to definitive surgery. Components include new patient visits, unique surgeons, medical oncologists, or radiation oncologists consulted, established patient encounters, biopsies, and imaging studies. We identified 116 050 cases of whom 99% were female and had an average age of 75.0 (SD = 6.2). We found that new patient visits have a protective association with respect to breast cancer mortality if they occur quickly after diagnosis with breast cancer mortality subdistribution Hazard Ratios [sHRs] = 0.87 (95% Confidence Interval [CI] 0.76–1.00) for 2, 0.71 (CI 0.55–0.92) for 3, and 0.63 (CI 0.37–1.07) for 4+ visits at minimal delay. New patient visits predict worsened mortality compared with no visits if the surgical delay is greater than 33 days (CI 14–53) for 2, 33 days (CI 17–49) for 3, and 44 days (CI 12–75) for 4+. Medical oncologist visits predict worse outcomes if the surgical delay is greater than 29 days (CI 20–39) for 1 and 38 days (CI 12–65) for 2+ visits. Similarly, surgeon encounters switch from a positive to a negative association if the surgical delay exceeds 29 days (CI 17–41) for 1 visit, but the positive estimate persists over time for 3+ surgeon visits. Conclusion Preoperative visits that cause substantial delays may be associated with increased mortality in older patients with breast cancer.