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Background: Black patients were severely under-represented in the clinical trials that led to the approval of immune checkpoint inhibitors (ICIs) for all cancers. The aim of this study was to characterise the effectiveness and safety of ICIs in Black patients., Methods: We did a retrospective cohort study of patients in the US Veterans Health Administration (VHA) system's Corporate Data Warehouse containing electronic medical records for all patients who self-identified as non-Hispanic Black or African American (referred to as Black) or non-Hispanic White (referred to as White) and received PD-1, PD-L1, CTLA-4, or LAG-3 inhibitors between Jan 1, 2010, and Dec 31, 2023. Effectiveness outcomes were overall survival, time to treatment discontinuation, and time to next treatment. The safety outcome was the frequency of immune-related adverse events; assessed among a random sample of 1000 Black patients and 1000 White patients, 892 pairs were matched on the basis of baseline characteristics using 1:1 exact matching without replacement. After manual chart review, patients who did not receive ICI therapy or who had inadequate follow-up were excluded. The adjusted effect of race on each effectiveness outcome was assessed in the whole ICI-treated cohort with propensity-weighted Cox regression with robust standard errors. Immune-related adverse events outcomes were analysed in the random matched sample with multivariable Cox regression, adjusting for baseline characteristics., Findings: We identified 26 398 patients, of whom 4943 (18·7%) patients were Black, 21 455 (81·3%) were White, 895 (3·4%) were female, 25 503 (96·6%) were male, 11 859 (45%) had non-small-cell lung cancer, and 26 045 (98·7%) received PD-1 or PD-L1 inhibitors. As of data cutoff (Aug 28, 2024), median follow-up was 40·3 months (95% CI 38·3-42·3) for Black patients and 43·9 months (43·0-45·1) for White patients. Compared with White patients, Black patients had longer time to treatment discontinuation (2-year unadjusted rates 10·7% [95% CI 9·8-11·7] for Black patients vs 8·6% [8·2-9·0] for White patients; adjusted hazard ratio [HR] 0·91, 95% CI 0·87-0·95, p<0·0001), similar time to next treatment (23·5% [22·3-24·8] for Black patients vs 25·6% [25·0-26·2] for White patients; 1·00, 0·95-1·05, p=0·96), and slightly improved overall survival (36·5% [35·2-38·1] for Black patients vs 36·5% [35·8-37·1]; 0·95, 0·90-0·99, p=0·036). 1710 patients (n=862 Black and n=848 White) were analysed for safety outcomes. Compared with White patients, Black patients had a reduced risk of all-grade immune-related adverse events (unadjusted 2-year rate 33·1% [95% CI 28·9-37·1] vs 44·1% [95% CI 39·1-48·7]; adjusted HR 0·75, 95% CI 0·62-0·90, p=0·0026), immune-related adverse events requiring treatment with systemic steroids (0·61, 0·46-0·81, p=0·00051), and immune-related adverse events resulting in permanent ICI discontinuation (0·58, 0·44-0·78, p=0·00024). In exploratory analyses of irAE subtypes, a significant risk reduction in Black patients was found for colitis (0·46, 0·27-0·76, p=0·0026) and hyperthyroidism or hypothyroidism (0·63, 0·44-0·90, p=0·011), and no significant differences were found for any other immune-related adverse event subtypes analysed. Similar results were found in analyses using a steroid-based definition of immune-related adverse events among the entire ICI-treated cohort., Interpretation: Compared with White patients, Black patients had similar ICI effectiveness and lower toxicities among those treated in the national VHA system, potentially reflecting an important difference in the therapeutic ratio (ratio of benefit to harm) of ICIs. Our findings of decreased toxicity among Black patients require further investigation to assess their generalisability., Funding: Million Veteran Program, Office of Research and Development, Veterans Health Administration and the LUNGevity foundation., Competing Interests: Declaration of interests We declare no competing interests., (Copyright Published by Elsevier Ltd.)

Background: The cumulative, health system-wide survival benefit of immune checkpoint inhibitors (ICIs) is unclear, particularly among real-world patients with limited life expectancies and among subgroups poorly represented on clinical trials. We sought to determine the health system-wide survival impact of ICIs., Methods: We identified all patients receiving PD-1/PD-L1 or CTLA-4 inhibitors from 2010 to 2023 in the national Veterans Health Administration (VHA) system (ICI cohort) and all patients who received non-ICI systemic therapy in the years before ICI approval (historical control). ICI and historical control cohorts were matched on multiple cancer-related prognostic factors, comorbidities, and demographics. The effect of ICI on overall survival was quantified with Cox regression incorporating matching weights. Cumulative life-years gained system-wide were calculated from the difference in adjusted 5-year restricted mean survival times., Results: There were 27,322 patients in the ICI cohort and 69,801 patients in the historical control cohort. Among ICI patients, the most common cancer types were NSCLC (46%) and melanoma (10%). ICI demonstrated a large OS benefit in most cancer types with heterogeneity across cancer types (NSCLC: adjusted HR [aHR] 0.56, 95% confidence interval [CI] 0.54-0.58, p < 0.001; urothelial: aHR 0.91, 95% CI 0.83-1.01, p = 0.066). The relative benefit of ICI was stable across patient age, comorbidity, and self-reported race subgroups. Across VHA, 15,859 life-years gained were attributable to ICI within 5-years of treatment, with NSCLC contributing the most life-years gained., Conclusion: We demonstrated substantial increase in survival due to ICIs across a national health system, including in patient subgroups poorly represented on clinical trials., (© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.)

Purpose: Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology., Methods: We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology., Results: Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping., Conclusions: Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies., (© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.)

We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of [Formula: see text] 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity. We measure concentration of risk across stratifications of predicted probability and observe an enriched likelihood of finding confirmed false negative responses from veterans with diagnosed housing instability. Positive responses were 34 times more likely to be detected within the top 1 % of patients predicted at risk than from those randomly selected. There is a 1 in 4 chance of detecting false negatives within the top 1 % of predicted risk. Machine learning methods can classify between episodes of housing instability using a data-driven approach that does not rely on variables curated from domain experts. This method has the potential to improve clinicians' ability to identify veterans who are experiencing housing instability but are not captured by HSCR., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Suicide is a major public health problem affecting US Veterans and the US in general. Many variables (e.g., demographic, clinical, biological, geographic) have been associated with risk for suicide and suicidal behavior, including altitude; however, the exact nature of the relationship between altitude and suicide remains unclear in part due to the fact that previous studies have used either geospatial data or individual-level data, but not both. Prior research has also failed to consider the full range of suicidal thoughts and behaviors, ranging from suicidal ideation to suicide deaths. Accordingly, the objective of the present research was to use both geospatial data (county and zip codes) and individual-level data to comprehensively assess the association between altitude and suicide mortality, suicide attempts, and suicidal ideation among US Veterans between 2000 and 2018. Taken together, our results demonstrate that there is a strong correlation between altitude and suicide rates at all the levels investigated and using different statistical analyses and even after controlling for significant covariates such as percent of age >50yr, percent male, percent white, percent non-Hispanic, median household income, and population density. We show that there is a positive correlation between altitude and suicide attempts especially when controlling by the covariates and a weak correlation between altitude and suicide ideation and the combination of suicide, suicide attempts and suicide ideation., (Published by Elsevier Ltd.)

The onset and persistence of life events (LE) such as housing instability, job instability, and reduced social connection have been shown to increase risk of suicide. Predictive models for suicide risk have low sensitivity to many of these factors due to under-reporting in structured electronic health records (EHR) data. In this study, we show how natural language processing (NLP) can help identify LE in clinical notes at higher rates than reported medical codes. We compare domain-specific lexicons formulated from Unified Medical Language System (UMLS) selection, content analysis by subject matter experts (SME) and the Gravity Project, to data-driven expansion through contextual word embedding using Word2Vec. Our analysis covers EHR from the Veterans Affairs (VA) Corporate Data Warehouse (CDW) and measures the prevalence of LE across time for patients with known underlying cause of death in the National Death Index (NDI). We found that NLP methods had higher sensitivity of detecting LE relative to structured EHR (S-EHR) variables. We observed that, on average, suicide cases had higher rates of LE over time when compared to patients who died of non-suicide related causes with no previous history of diagnosed mental illness. When used to discriminate these outcomes, the inclusion of NLP derived variables increased the concentration of LE along the top 0.1%, 0.5% and 1% of predicted risk. LE were less informative when discriminating suicide death from non-suicide related death for patients with diagnosed mental illness., (Published by Elsevier Ltd.)