Liu, Bolun

Abstract Immune checkpoint inhibitor–associated immune thrombocytopenia (ICI-ITP) has been described in case reports and small case series, but comprehensive data on its incidence, risk factors, clinical features, treatment, and outcomes are lacking. We reviewed medical records of all adults initiating ICI therapy between 2016 and 2023 at 29 US hospitals across 7 major cancer centers to identify patients with ICI-ITP. Multivariable logistic regression was used to identify risk factors, and Cox modeling was performed to assess the association between ICI-ITP, its severity, and mortality. Among 86 467 patients, ICI-ITP occurred in 214 (0.25%). Independent risk factors included lower baseline platelet count, combination ICI therapy, stage IV cancer, and additional immune-related adverse events. ICI-ITP occurred at a median of 8 weeks (interquartile range [IQR], 4-18) after ICI initiation, with a median nadir platelet count of 41 × 109/L (IQR, 17 × 109/L to 64 × 109/L). Patients were treated with glucocorticoids (n = 106 [49.5%]), immune globulin (n = 39 [18.2%]), and thrombopoietin receptor agonists (n = 29 [13.6%]). Recovery occurred in 161 patients (75.2%) at a median of 2.3 weeks (IQR, 1.0-5.3). Of 76 patients rechallenged with ICIs, 23 (30.3%) developed recurrent ICI-ITP. ICI-ITP and its severity were associated with higher all-cause mortality, with a nearly threefold increase in risk among patients with severe ICI-ITP than those without ICI-ITP (adjusted hazard ratio, 2.96 [95% confidence interval, 2.14-4.08]). These findings establish ICI-ITP as a rare but clinically significant complication of ICI therapy, provide, to our knowledge, the first large-scale description of its risk factors and clinical course, and underscore the importance of timely recognition and management.

Acute myeloid leukemia (AML) is an aggressive hematologic malignancy characterized by the clonal proliferation of myeloid precursors and rapid progression. Historically consisting of intensive chemotherapy, AML management has evolved significantly due to advances in molecular diagnostics and risk stratification. This review discusses current therapeutic paradigms in AML, emphasizing the growing role of personalized medicine across age and risk groups. For younger, fit patients, intensive regimens such as the “7 + 3” protocol remain the standard, often enhanced by targeted agents like FMS-like tyrosine kinase 3 (FLT3) and IDH inhibitors. Older or unfit individuals benefit from low-intensity treatments such as hypomethylating agents combined with venetoclax, now considered a frontline standard of care. The use of liposomal chemotherapy (CPX-351), measurable residual disease (MRD) monitoring, and maintenance therapy further refine post-remission strategies. Emerging therapies, including menin inhibitors, antibody–drug conjugates, and immunotherapies like CAR-T cells and vaccines, offer additional options, especially in relapsed/refractory settings. This comprehensive review outlines the current landscape and future directions in AML therapy, emphasizing the transition toward individualized, mutation-driven treatment strategies.

Blood transfusion is a common therapeutic intervention in hospitalized patients. There are numerous indications for transfusion, including anemia and coagulopathy with deficiency of single or multiple coagulation components such as platelets or coagulation factors. Nevertheless, the practice of transfusion in critically ill patients has been controversial mainly due to a lack of evidence and the need to consider the appropriate clinical context for transfusion. Further, transfusion carries many risk factors that must be balanced with benefits. Therefore, transfusion practice in ICU patients has constantly evolved, and we endeavor to present a contemporary review of transfusion practices in this population guided by clinical trials and expert guidelines.

Abstract Background It was reported that patients with Non-Alcoholic Fatty Liver Disease (NAFLD) were at increased risk of contracting severe infections requiring hospitalization which could end up with sepsis. However, data regarding the impact of comorbid NAFLD on the in-hospital outcomes of sepsis admission is scarce. Methods This nationwide retrospective observational study included 21,057,911 adult patients who were admitted into hospitals in the United States between 2000 and 2019 with a primary discharge diagnosis of sepsis. These patients were categorized according to the presence or absence of comorbid NAFLD. The twenty-year trend of nationwide NAFLD prevalence among sepsis inpatients was elucidated. Multivariate logistic regression analysis was used to analyze NAFLD's impact on in-hospital outcomes of sepsis. Results In the twenty-year study period, the prevalence of NALFD among sepsis inpatients trended up from 1.2% in 2000 to 4.2 % in 2019. While sepsis mortality had trended down, comorbid NAFLD in sepsis patients was consistently associated with a higher adjusted in-hospital all-cause mortality rate (adjusted odds ratio (OR), 1.45; 95% confidence interval (CI), 1.42-1.48), higher odds of developing septic shock, and multi-organ dysfunctions (including cardiovascular-, renal-, and hepatic dysfunction). They were more likely to be discharged to another acute hospital. The length of stay of sepsis patients with NAFLD who died in the hospital was significantly shorter than those without NAFLD. Conclusions Comorbid NAFLD is associated with higher in-hospital all-cause mortality and worse clinical outcomes in sepsis inpatients. Addressing this rising epidemic will be of paramount importance to improve sepsis in-hospital outcomes.

Therapeutic plasma exchange (TPE) is a treatment paradigm used to remove harmful molecules from the body. In short, it is a technique that employs a process that functions partially outside the body and involves the replacement of the patient’s plasma. It has been used in the ICU for a number of different disease states, for some as a first-line treatment modality and for others as a type of salvage therapy. This paper provides a brief review of the principles, current applications, and potential future directions of TPE in critical care settings.

Background Acute pericarditis (AP) is considered a cardiovascular complication in patients with COVID‐19. We aimed to ass‐ess the incidence, associated complications, and clinical impact of AP on hospitalized patients with COVID‐19. Methods and Results In this retrospective cohort study, International Classification of Diseases, Tenthth Revision, Clinical Modification ( ICD‐10 ) codes were used to identify patients with COVID‐19 with or without AP in the National Inpatient Sample 2020 database. We compared outcomes between AP and non‐AP groups before and after propensity‐score matching for patient and hospital demographics and relevant comorbidities. A total of 211 619 patients with a primary diagnosis of COVID‐19 were identified, including 983 (0.46%) patients who had a secondary diagnosis of AP. Before matching, patients with COVID‐19 with AP were younger (59.93±19.24 years old versus 64.29±16.82 years old) and more likely to have anemia (40.5% versus 19.9%), cancer (6.7% versus 3.6%), and chronic kidney disease (29.3% versus 19.6%) (all P <0.05). After matching, patients with COVID‐19 with AP (n=980), when compared with the matched non‐AP group (n=2936), had higher rates of mortality (21.3% versus 11.1%, P <0.001), cardiac arrest (5.0% versus 2.6%, P <0.001), cardiogenic shock (4.2% versus 0.5%, P <0.001), ventricular arrhythmia (4.7% versus 1.9%, P <0.001), acute kidney injury (38.3% versus 28.9%, P <0.001), acute congestive heart failure (14.3% versus 4.8%, P <0.001), and longer length of stay (7.00±10.00 days versus 5.00±7.00 days, P <0.001) and higher total charges ($75066.5±$130831.3 versus $44824.0±$63660.5, P <0.001). Conclusions In hospitalized patients with COVID‐19, AP is a rare but severe in‐hospital complication and is associated with worse in‐hospital outcomes.

ABSTRACT Colorectal signet ring cell carcinoma is a rare type of colon cancer. Early diagnosis remains challenging because of nonspecific colonoscopy findings, such as diffuse circumferential thickening, stricture, and ulcerations, and the potential absence of typical pathological features in the initial biopsy sample. In this article, we report a 41-year-old man with ulcerating rectosigmoid stricture in the rectosigmoid colon with inconclusive histology. Subsequently, the patient developed small bowel obstruction and was diagnosed with stage 4 colorectal signet ring cell carcinoma with peritoneal carcinomatosis.

e13552 Background: Real-world data from Electronic Health Records (EHR) have been widely used for patient identification to build study cohorts for clinical research. Traditionally, diagnosis codes in the EHR, such International Classification of Diseases (ICD), are used to identify the target patients. However, the accuracy of this approach is dependent on the accuracy of ICD coding, with potential errors especially for tumor types that are frequent locations for metastases (which may contribute to mis-coding). In this study, we attempted to develop a Machine Learning (ML) based approach on EHR data to improve the accuracy of identification of patients with lung cancer. Methods: We used survey respondents in the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2, NCT03892967) cluster-randomized trial at Mayo Clinic as our initial pan-cancer cohort. E2C2 includes adults receiving Medical Hematology/Oncology care for a solid or liquid tumor at Mayo Clinic. We collected cancer diagnoses from the individually abstracted Mayo Clinic Cancer Registry to annotate the cancer type for the patients. Lung cancer related ICD-9 (162.X) and ICD-10 (C34.X) codes were used to build a search query on the Mayo’s EHR to find target patients from the E2C2 cohort, and to investigate the ICD-based lung cancer patient identification performance. Diagnosis, radiation oncology treatment (CPT 77261 - 77799), and antineoplastic drug administration data were collected from EHR as variables. Logistic Regression (LR), Support vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) were selected to build models for lung cancer patient identification. 10-fold cross-validation was implemented to assess the models. Precision, Recall, F1 Score, and area under the curve (AUC) were selected to measure the performance. Results: We collected 13,893 patients with a specific cancer diagnosis, and 1,394 were identified as having lung cancer. The identification performance across different methods is shown. The ICD-based method only had 0.65 precision. It means we collected a lot of false positive cases (other cancer but no lung cancer patient), as we mentioned in the background. SVM gets the best precision results, but its recall and F1 score are not good enough. XGB shows the best F1 Score and AUC, which also means this method achieved the best and most balanced performance. Conclusions: In this study, we found that XGB-based methods achieved the best identification performance for lung cancer. In the future, we will investigate if this is also true for the identification of other cancer types. [Table: see text]