Citations

Abstract The effectiveness of T cell–based immunotherapy depends on durable T-cell responses that can efficiently eliminate tumor cells. NKG7 was discovered three decades ago as a protein associated with lytic granules. However, only studies published over the past 5 years have contributed substantially to our understanding of NKG7 in T-cell biology. NKG7 has been recognized as an important T-cell functional marker in responses to immune checkpoint inhibitor therapy and in the prognosis of certain cancers. Besides its role in the generation, trafficking, and release of lytic granules, which is critical for efficient T-cell cytotoxicity against tumor cells, NKG7 has been identified as a key negative regulator of mTORC1 activity. By restraining mTORC1 activity, NKG7 promotes T-cell longevity and memory generation after infection. Importantly, NKG7 upregulation has demonstrated therapeutic potential in preclinical T-cell therapy for cancer. Collectively, NKG7 is emerging as a promising biomarker and therapeutic addition to T cell–based immunotherapies.

Background: Treatment decisions in patients with anterior cruciate ligament (ACL) injuries are influenced by multiple factors, such as the desire to return to sports or symptomatic instability. Identifying the differential treatment effect of ACL reconstruction (ACLR) compared with nonoperative management on a patient-specific level can inform surgical decision-making. Hypothesis: Unsupervised machine learning can identify distinct patient subgroups based on outcome achievement after ACL injury, and ACLR will exert a protective effect on the development of posttraumatic osteoarthritis (PTOA) over nonoperative management. Study Design: Cohort study; Level of evidence, 3. Methods: A longitudinal populational registry identified patients with ACL injuries from 1990 to 2016 with a minimum 7.5-year follow-up. An unsupervised random forest algorithm was utilized to develop and validate patient subgroups. Treatment effects of ACLR on outcomes were analyzed using a machine learning causal inference estimator. Patient subgroup membership was incorporated into a multivariable logistic regression to identify factors predictive of optimal outcomes. Results: A total of 923 patients (785 primary ACLR, 138 nonoperative) were included. The random forest algorithm arrived at an optimal partition of 2 subgroups, with 653 patients in the optimal outcome subgroup (368 male [56.4%]; mean age, 26.0 ± 10.2 years; mean body mass index [BMI], 26.5 ± 4.30) and 270 patients in the suboptimal outcome subgroup (152 male [56.3%]; mean age, 35.0 ± 10.1 years; mean BMI, 30.5 ± 5.54). The latter group demonstrated significantly increased rates of secondary meniscal injury, development of symptomatic PTOA, and progression to total knee arthroplasty (TKA) at the final follow-up (all P < .01). In the optimal outcome subgroup, ACLR had significantly protective treatment effects on the risk of secondary meniscal injury (average treatment effect [ATE], 61%), contralateral ACL injury (ATE, 8%), symptomatic PTOA (ATE, 16%), and progression to TKA (ATE, 6%) (all P < .01). Conversely, in the suboptimal outcome subgroup, ACLR only protected against symptomatic PTOA (ATE, 11%) and progression to TKA (ATE, 8%) (both P < .01). Conclusion: Two clinically meaningful subgroups were identified from retrospectively collected data and found to experience differential treatment responses after ACL injuries. ACLR decreased the rate of development of PTOA and TKA in both subgroups but was not as effective in preventing secondary meniscal injuries or contralateral ACL injuries in patients who were older, heavier, or had concomitant medial meniscus injuries.

Abstract Clinical question What are the benefits and harms of medications for adults with type 2 diabetes at varied risks of cardiovascular and kidney related complications? Context Emerging clinical trials of novel medications have demonstrated benefits on cardiovascular, kidney, and weight related outcomes in people with type 2 diabetes. Dynamically updated practice guidelines adhering to standards of trustworthiness are necessary in response to a rapidly evolving evidence base and the availability of multiple medication alternatives. This living practice guideline incorporates the latest available medications and evidence and provides recommendations stratified by risks of cardiovascular and kidney complications to inform diabetes management. Recommendations The panel issued risk-stratified recommendations regarding four prioritised medications for adults with type 2 diabetes (SGLT-2 inhibitors, GLP-1 receptor agonists, finerenone and tirzepatide): • Lower risk (three or fewer cardiovascular risk factors without established cardiovascular disease (CVD) or chronic kidney disease (CKD)): weak recommendation against SGLT-2 inhibitors or GLP-1 receptor agonists. • Moderate risk (more than three cardiovascular risk factors without established CVD or CKD; or established CVD and/or CKD at lower risk of complications): weak recommendation in favour of SGLT-2 inhibitors or GLP-1 receptor agonists; and a weak recommendation against finerenone in adults with CKD. • Higher risk (established CVD and/or CKD at higher risk of complications, or established heart failure): strong recommendation in favour of SGLT-2 inhibitors or GLP-1 receptor agonists; and a weak recommendation in favour of finerenone in adults with CKD. • Across risk strata: weak recommendation in favour of tirzepatide in adults with obesity. About this guideline and how it was created An international panel including two patient partners, clinicians, and methodologists produced these recommendations. The panel followed standards for trustworthy guidelines and used the GRADE approach, explicitly considering the balance of benefits, harms and burdens of treatment from an individual patient perspective. Recommendations were informed by a linked living systematic review and network meta-analysis evaluating relative benefits and harms updated to 31 July 2024; and by linked systematic reviews addressing risk prediction models and values and preferences of adults with type 2 diabetes. Candidate therapeutics are prioritised based on availability of sufficient randomised trial data, relevance to a global audience and likelihood of changing practice. This is the first version of the living guideline. The guideline is part of the BMJ Rapid Recommendations series. MAGICapp displays the most recent version of the guideline and full content including evidence summaries and decision aids; major updates will be published in The BMJ . We encourage re-use, adaptation and translation of these living guidelines, and recognise that the lack of availability or high costs of some medications may be prohibitive and will impact on how these recommendations are implemented across different health care systems.

Abstract Background Cabozantinib is indicated for hepatocellular carcinoma (HCC) following first-line (1L) sorafenib, but the 1L standard has shifted to immuno-oncology (IO)-based regimens. This study evaluated real-world outcomes among patients with advanced HCC receiving second-line (2L) cabozantinib following 1L therapies, including newer regimens. Patients and Methods US claims data were used to identify adults with advanced HCC initiating 2L cabozantinib monotherapy (index date). Patients were stratified into three 1L treatment cohorts: IO monotherapy/IO + IO combination therapy; IO + non–IO combination therapy; or tyrosine kinase inhibitor (TKI) monotherapy. Real-world time to treatment discontinuation (rwTTD), real-world time to next treatment or death (rwTNTD), real-world overall survival (rwOS), and cabozantinib dosing were assessed collectively and by cohort. Adverse events were assessed before/after cabozantinib initiation. Results Among 148 patients who received 2L cabozantinib, 28 were in the IO monotherapy/IO + IO combination therapy cohort, 54 in the IO + non–IO combination therapy cohort, and 66 in the TKI monotherapy cohort. Median rwTTD among all patients was 3.2 months; median rwTNTD was 7.6 months; 12-month rwOS rate was 61.6%. There were no significant differences in these outcomes among the three cohorts. Overall, 44.6% of patients initiated 2L cabozantinib at 60 mg/day, of whom 39.4% required a dose reduction; 37.8% initiated at 40 mg/day, of whom 16.1% had a dose reduction. Adverse event rates were similar before/after cabozantinib initiation. Conclusions Cabozantinib shows consistent effectiveness and safety in the 2L HCC setting following prior TKIs or IO-based regimens in real-world clinical practice. These findings may inform 2L treatment decisions.

Background: Surgeons often recommend weight loss for patients with obesity before total knee arthroplasty (TKA). However, it is unknown whether preoperative weight loss affects outcomes. The goals of this study were to determine how many patients with obesity lost weight before TKA, to identify weight loss predictors, and to evaluate if preoperative weight loss affected postoperative outcomes. Methods: Among 23,726 primary TKAs performed between 2002 and 2019, we identified 3,665 patients who had a body mass index (BMI) of ≥30 kg/m 2 measured 1 to 24 months before surgery and had a weight measured at surgery. The mean patient age was 68 years, and 59% of patients were female. The mean patient BMI was 36 kg/m 2 . Univariable linear regressions evaluated weight loss predictors. Univariable and multivariable logistic regressions and Cox proportional hazards models evaluated the impact of preoperative weight change on discharge, operative time, periprosthetic joint infections (PJIs), complications, revisions, and reoperations. The mean follow-up was 6 years. Results: Overall, 20% of patients gained ≥5 pounds (1 pound = 0.45 kg), 39% maintained weight, 17% lost 5 to <10 pounds, 15% lost 10 to <20 pounds, and 9% lost ≥20 pounds before TKA. Male patients lost slightly more weight (−4.6 pounds) than female patients (−4.3 pounds) (p = 0.05). In univariable analyses, gaining >5 pounds was associated with increased odds of extended hospital length of stay (odds ratio [OR], 1.4; p = 0.01) and risk of complications (hazard ratio [HR], 1.7; p < 0.01). Losing 10 to <20 pounds was associated with increased risks of revision (HR, 2.0; p = 0.01), PJI (HR, 3.1; p < 0.01), and complications (HR, 1.6; p = 0.03). In multivariable analyses, compared with maintaining weight, losing 10 to <20 pounds was associated with an increased risk of PJI (HR, 2.6; p = 0.01), whereas gaining >5 pounds was associated with an increased risk of complications (HR, 1.5; p = 0.03). Conclusions: Few patients with obesity lost substantial weight before primary TKA, and reaching common preoperative weight loss goals was not associated with improved outcomes. Although a healthy weight is important for general health, weight loss before TKA may not be sufficient to improve postoperative outcomes for most patients with obesity. Level of Evidence: Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

ABSTRACTBackgroundMetabolic and bariatric surgery (MBS) is the most effective and durable approach to treating obesity, yet recurrent weight gain occurs in a subset of patients. Primary care often serves as a routine point of contact for patients following MBS and is a potential gateway for addressing and/or preventing recurrent weight gain.MethodsAlthough guidelines for the management of recurrent weight gain after MBS exist, this narrative review was undertaken to clarify the role of the primary care providers in enhancing long‐term outcomes and preventing weight gain after MBS.FindingsRegular follow‐up in primary care provides an opportunity to identify challenges related to weight management and overall health outcomes, which may include concerns related to nutrition, mental or physical well‐being, body image, motivation, and behavior change, for example. The availability of multidisciplinary providers within primary care settings, including behavioral health specialists, dietitians, and physical therapists, is an important strength of this setting in addressing potential post‐MBS concerns such as recurrent weight gain.ConclusionThis review outlines clinical considerations for managing recurrent weight gain post‐MBS in primary care and includes recommendations for both primary care providers and specialty clinicians working in primary care to deliver care effectively and mitigate weight stigma post‐MBS.

Abstract Sex differences in cancer outcome, including that of glioblastoma (GBM), are shaped by biological, hormonal, and immunological factors and impact disease progression, treatment responses, survival, and tumor microenvironment (TME) interactions. Platelets regulate the immune responses and tumor progression in many cancers, but it is not clear how they contribute to these sex-based differences by affecting the dynamics of the TME. Here, we show that GBM patients exhibit heightened platelet reactivity driven by PAR4 signaling. In murine GBM models, both pharmacological inhibition of PAR4 using BMS986120 and genetic deletion of PAR4 significantly prolong survival in females but not males. This survival advantage is estrogen dependent: it is preserved in chromosomal male-hormonal female mice within the four-core genotype model and is rescued in ovariectomized mice treated with estrogen. The survival benefit is TME specific and is mediated by platelet-driven enhancement of CD8⁺ T cell infiltration into the tumor. Inhibition of platelet PAR4 signaling increases calcium signaling through an estrogen-dependent interaction between PAR4 and estrogen receptor β (ERβ)—a receptor interaction not previously described. PAR4-activated platelets within the TME suppress CD8⁺ T cell function, and depletion of CD8⁺ T cells abolishes both the tumor-induced platelet reactivity and the survival benefit conferred by PAR4 inhibition. These findings establish platelet-mediated PAR4 signaling as a critical driver of tumor progression and identify sex-specific immune responses as key to therapeutic efficacy.

Importance Diagnostic errors represent a major patient safety concern, with the potential to significantly impact patient outcomes. To address this, various trigger-based strategies have been developed to identify diagnostic errors, aiming to enhance clinical decision-making and improve patient safety. Objective To evaluate the performance of three pre-established triggers (T) in the emergency department (ED) setting and assess their effectiveness in detecting diagnostic errors. Design Consecutive cohort, retrospective observational design. Setting Academic ED with 80 000 annual visits. Participants Adults and children presenting to a single ED in the USA between 1 May 2018 and 1 January 2020. Intervention/outcomes Electronic health records (EHRs) were retrieved and categorised into trigger-positive and trigger-negative cases using the following criteria: T1—unscheduled returnvisits to the ED with admission within 7–10 days of theinitial visit; T2—care escalation from the inpatient unitto the intensive care unit (ICU) within 6, 12 or 24 hoursof ED admission; and T3—all deaths in the ED or within24 hours of ED admission, excluding palliative care. A random sample of trigger-positive cases was reviewed using the SaferDx tool to determine the presence or absence of a diagnostic error. Results A total of 5791 trigger-positive and 118262 trigger-negative cases were identified. Among trigger-positive cases, 4159 (72%) were associated with T1, 1415 (24%) with T2, and 217 (4%) with T3. A preliminary chart review of 462 trigger-positive and 251 trigger-negative cases showed most were error-negative (279 and 217, respectively). Detailed reviews found 32 diagnostic errors among 183 trigger-positive cases, yielding PPVs of 5.4% (T1), 8.9% (T2), and 6.9% (T3). No errors were found in 34 reviewed trigger-negative cases, resulting in a 100% NPV. Sepsis was the most common diagnosis among error-positive cases (n=11, 34.4%). Those with non-specific chief complaints like altered mental status or shortness of breath had higher diagnostic error risk. Conclusion and relevance While previously proposed EHR-based triggers can identify some diagnostic errors, they are insufficient for detecting all cases. To improve error detection performance, we recommend exploring data-driven strategies, such as machine learning techniques, to more effectively identify underlying contributing factors to diagnostic errors and enhance detection accuracy in the ED.