Impact of Spousal Mental Illness on Healthcare Utilization Among Patients With Gastrointestinal Cancer.

BACKGROUND: Informal caregiving involves increased responsibilities, with financial and emotional challenges, thereby affecting the well-being of the caregiver. We aimed to investigate the effect of spousal mental illness on hospital visits and medical spending among patients with gastrointestinal (GI) cancer. METHODS: Patients who underwent GI cancer surgery between 2013 and 2020 were identified from the IBM Marketscan database. Multivariable regression analysis was used to examine the association between spousal mental illness and healthcare utilization. RESULTS: A total of 6,035 patients underwent GI surgery for a malignant indication. Median age was 54 years (IQR: 49-59), most patients were male (n = 3592, 59.5%), and had a CCI score of ≤ 2 (n = 5512, 91.3%). Of note, in the 1 year follow-up period, 19.4% (anxiety: n = 509, 8.4%; depression: n = 301, 5.0%; both anxiety and depression: n = 273, 4.5%; severe mental illness: n = 86, 1.4%) of spouses developed a mental illness. On multivariable analysis, after controlling for competing factors, spousal mental illness remained independently associated with increased odds of emergency department visits (OR 1.20, 95% CI 1.05-1.38) and becoming a super healthcare utilizer (OR 1.37, 95% CI 1.04-1.79), as well as 12.1% (95% CI 10.6-15.3) higher medical spending. CONCLUSION: Among patients with GI cancer spousal mental illness is associated with higher rates of outpatient visits, emergency department visits, and expenditures during the 1-year postoperative period. These findings underscore the importance of caregiving resources and counseling in alleviating caregiver burden, thereby reducing the overall burden on the healthcare system.

Pharmacy Accessibility and Social Vulnerability.

This cross-sectional study examines the association of availability of primary care practitioners and level of socioeconomic vulnerability with risk of pharmacy deserts in regions of the US.

The Association of Socio-Environmental Inequality and Outcomes Among Patients Undergoing Major Surgery.

INTRODUCTION: Environmental hazards may influence health outcomes and be a driver of health inequalities. We sought to characterize the extent to which social-environmental inequalities were associated with surgical outcomes following a complex operation. METHODS: In this cross-sectional study, patients who underwent abdominal aortic aneurysm repair, coronary artery bypass grafting, colectomy, pneumonectomy, or pancreatectomy between 2016 and 2021 were identified from Medicare claims data. Patient data were linked with social-environmental data sourced from Centers for Disease Control and Agency for Toxic Substances and Disease Registry data based on county of residence. The Environmental Justice Index social-environmental ranking (SER) was used as a measure of environmental injustice. Multivariable regression analysis was performed to assess the relationship between SER and surgical outcomes. RESULTS: Among 1,052,040 Medicare beneficiaries, 346,410 (32.9%) individuals lived in counties with low SER, while 357,564 (33.9%) lived in counties with high SER. Patients experiencing greater social-environmental injustice were less likely to achieve textbook outcome (odds ratio 0.95, 95% confidence interval 0.94-0.96, P < 0.001) and to be discharged to an intermediate care facility or home with a health agency (odds ratio 0.97, 95% confidence interval 0.96-0.98, P < 0.001). CONCLUSIONS: Cumulative social and environmental inequalities, as captured by the Environmental Justice Index SER, were associated with postoperative outcomes among Medicare beneficiaries undergoing a range of surgical procedures. Policy makers should focus on environmental, as well as socioeconomic injustice to address preventable health disparities.

Preoperative platelet count as an independent predictor of long-term outcomes among patients undergoing resection for intrahepatic cholangiocarcinoma.

BACKGROUND AND OBJECTIVES: An elevated platelet count may reflect neoplastic and inflammatory states, with cytokine-driven overproduction of platelets. The objective of this study was to evaluate the prognostic utility of high platelet count among patients undergoing curative-intent liver surgery for intrahepatic cholangiocarcinoma (ICC). METHODS: An international, multi-institutional cohort was used to identify patients undergoing curative-intent liver resection for ICC (2000-2020). A high platelet count was defined as platelets >300 *109/L. The relationship between preoperative platelet count, cancer-specific survival (CSS), and overall survival (OS) was examined. RESULTS: Among 825 patients undergoing curative-intent resection for ICC, 139 had a high platelet count, which correlated with multifocal disease, lymph nodes metastasis, poor to undifferentiated grade, and microvascular invasion. Patients with high platelet counts had worse 5-year (35.8% vs. 46.7%, p = 0.009) CSS and OS (24.8% vs. 39.8%, p < 0.001), relative to patients with a low platelet count. After controlling for relevant clinicopathologic factors, high platelet count remained an adverse independent predictor of CSS (HR = 1.46, 95% CI 1.02-2.09) and OS (HR = 1.59, 95% CI 1.14-2.22). CONCLUSIONS: High platelet count was associated with worse tumor characteristics and poor long-term CSS and OS. Platelet count represents a readily-available laboratory value that may preoperatively improve risk-stratification of patients undergoing curative-intent liver resection for ICC.

Preoperative and postoperative predictive models of early recurrence for colorectal liver metastases following chemotherapy and curative-intent one-stage hepatectomy.

INTRODUCTION: Accurate prediction of patients at risk for early recurrence (ER) among patients with colorectal liver metastases (CRLM) following preoperative chemotherapy and hepatectomy remains limited. METHODS: Patients with CRLM who received chemotherapy prior to undergoing curative-intent resection between 2000 and 2020 were identified from an international multi-institutional database. Multivariable Cox regression analysis was used to assess clinicopathological factors associated with ER, and an online calculator was developed and validated. RESULTS: Among 768 patients undergoing preoperative chemotherapy and curative-intent resection, 128 (16.7 %) patients had ER. Multivariable Cox analysis demonstrated that Eastern Cooperative Oncology Group Performance status ≥1 (HR 2.09, 95%CI 1.46-2.98), rectal cancer (HR 1.95, 95%CI 1.35-2.83), lymph node metastases (HR 2.39, 95%CI 1.60-3.56), mutated Kirsten rat sarcoma oncogene status (HR 1.95, 95%CI 1.25-3.02), increase in tumor burden score during chemotherapy (HR 1.51, 95%CI 1.03-2.24), and bilateral metastases (HR 1.94, 95%CI 1.35-2.79) were independent predictors of ER in the preoperative setting. In the postoperative model, in addition to the aforementioned factors, tumor regression grade was associated with higher hazards of ER (HR 1.91, 95%CI 1.32-2.75), while receipt of adjuvant chemotherapy was associated with lower likelihood of ER (HR 0.44, 95%CI 0.30-0.63). The discriminative accuracy of the preoperative (training: c-index: 0.77, 95%CI 0.72-0.81; internal validation: c-index: 0.79, 95%CI 0.75-0.82) and postoperative (training: c-index: 0.79, 95%CI 0.75-0.83; internal validation: c-index: 0.81, 95%CI 0.77-0.84) models was favorable (https://junkawashima.shinyapps.io/CRLMfollwingchemotherapy/). CONCLUSIONS: Patient-, tumor- and treatment-related characteristics in the preoperative and postoperative setting were utilized to develop an online, easy-to-use risk calculator for ER following resection of CRLM.

Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature.

BACKGROUND AND AIM: Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. The project of incorporating artificial intelligence (AI) techniques in HCC aims to improve the performance of image recognition. Herein, we thoroughly analyze and evaluate proposed AI models in the field of HCC diagnosis. METHODS: A comprehensive review of the literature was performed utilizing MEDLINE/PubMed and Web of Science databases with the end of search date being the 30th of September 2023. The MESH terms "Artificial Intelligence," "Liver Cancer," "Hepatocellular Carcinoma," "Machine Learning," and "Deep Learning" were searched in the title and/or abstract. All references of the obtained articles were also evaluated for any additional information. RESULTS: Our search resulted in 183 studies meeting our inclusion criteria. Across all diagnostic modalities, reported area under the curve (AUC) of most developed models surpassed 0.900. A B-mode US and a contrast-enhanced US model achieved AUCs of 0.947 and 0.957, respectively. Regarding the more challenging task of HCC diagnosis, a 2021 deep learning model, trained with CT scans, classified hepatic malignant lesions with an AUC of 0.986. Finally, a MRI machine learning model developed in 2021 displayed an AUC of 0.975 when differentiating small HCCs from benign lesions, while another MRI-based model achieved HCC diagnosis with an AUC of 0.970. CONCLUSIONS: AI tools may lead to significant improvement in diagnostic management of HCC. Many models fared better or comparable to experienced radiologists while proving capable of elevating radiologists' accuracy, demonstrating promising results for AI implementation in HCC-related diagnostic tasks.

Effect of telemedicine use on medical spending and health care utilization among patients with gastrointestinal cancer.

BACKGROUND: We sought to assess the impact of telemedicine on healthcare utilization and medical expenditures among patients with a diagnosis of gastrointestinal (GI) cancer. METHODS: Patients with newly diagnosed GI cancer from 2013 to 2020 were identified from the IBM MarketScan database (IBM Watson Health) . Healthcare utilization, total medical outpatient insurance payments within 1 year post-diagnosis, and out-of-pocket (OOP) expenses among telemedicine users and non-users were assessed after propensity score matching (PSM). RESULTS: Among the 32,677 patients with GI cancer (esophageal, n = 1862, 5.7%; gastric, n = 2009, 6.1%; liver, n = 2929, 9.0%; bile duct, n = 597, 1.8%; pancreas, n = 3083, 9.4%; colorectal, n = 22,197, 67.9%), a total of 3063 (9.7%) utilized telemedicine. After PSM (telemedicine users, n = 3064; non-users, n = 3064), telemedicine users demonstrated a higher frequency of clinic visits (median: 5.0 days, IQR 4.0-7.0 vs non-users: 2.0 days, IQR 2.0-3.0, P < .001) and fewer potential days missed from daily activities (median: 7.5 days, IQR 4.5-12.5 vs non-users: 8.5 days, IQR 5.5-13.5, P < .001). Total medical spending per month and utilization of emergency room (ER) visits for telemedicine users were higher vs non-users (median: $10,658, IQR $5112-$18,528 vs non-users: $10,103, IQR $4628-$16,750; 46.8% vs 42.6%, both P < .01), whereas monthly OOP costs were comparable (median: $273, IQR $137-$449 for telemedicine users vs non-users: $268, IQR $142-$434, P = .625). CONCLUSION: Telemedicine utilization was associated with increased outpatient clinic visits yet reduced potential days missed from daily activities among patients with GI cancer. Telemedicine users tended to have more ER visits and total medical spending per month, although monthly OOP costs were comparable with non-users.