Radiation Therapy in the Last Month of Life: Association With Aggressive Care at the End of Life.

CONTEXT: Half of the patients with cancer who undergo radiation therapy do so with palliative intent. OBJECTIVES: To determine the proportion of undergoing radiation in the last month of life, patient characteristics, cancer course, the type and duration of radiation, whether palliative care was involved, and the of radiation with aggressive cancer care metrics. METHODS: One thousand seven hundred twenty-seven patients who died of cancer between January 1, 2018, and December 31, 2019, were included. Demographics, cancer stage, palliative care referral, advance directives, use of home health care, radiation timing, and survival were collected. Type of radiation, course, and intent were reviewed. Chi-square analysis was utilized for categorical variables, and Kruskal-Wallis tests for continuous variables. A stepwise selection was used to build a Cox proportional hazard model. RESULTS: Two hundred thirty-three patients underwent radiation in the last month of life. Younger patients underwent radiation 67.3 years (SD 11.52) versus 69.2 years (SD 11.96). 42.6% had radiation within two weeks of death. The average fraction number was 5.5. Individuals undergoing radiation were more likely to start chemotherapy within the last 30 days of life, continue chemotherapy within two weeks of death, be admitted to the ICU, and have two or more hospitalizations or emergency room visits. Survival measured from the date of diagnosis was shorter for those undergoing radiation, 122 days (IQR 58-462) versus 474 days (IQR 225-1150). Palliative care consultations occurred later in those undergoing radiation therapy. CONCLUSION: Radiation therapy in the last month of life occurs in younger patients with rapidly progressive cancer, who are subject to more aggressive cancer care, and have late palliative care consults.

Validation of the Surprise Question and the Development of a Multivariable Model.

CONTEXT: The Surprise Question (SQ) (would you be surprised if this patient died within a year?) is a prognostic variable explored in chronic illnesses. Validation is limited to sensitivity, specificity, and predictive values. OBJECTIVES: Our objective is to validate the SQ in cancer patients and develop a predictive model with additional variables. METHODS: A prospective cohort study of adult (age>18) cancer patients seen between October 1, 2019, through March 31, 2021, undergoing systemic therapies had the SQ completed by oncologists prior to each change in systemic therapy. The primary outcome was survival for one year. Secondary outcomes were predictions of survival at three, six, and nine months. Patients were grouped into negative SQ (not surprised) and positive SQ (surprised). Sensitivity, specificity, predictive values, and likelihood ratios (LR) were calculated for the SQ. Additional prognostic variables were age, gender, cancer stage, line of therapy, Charleson Comorbid Index (CCI), palliative care consultation (prior to, after the SQ, or not at all), and healthcare utilization (outpatient, inpatient, and emergency department (ED). Logistic regression and receiver operating characteristics (ROC) were used for discrimination and modeling. Akaike information criterion (AIC) was used to compare the model fit as each predictor. RESULTS: 1366 patients had 1 SQ; 784 died within a year. The SQ predicted survival at one year (P = 0.008), with a positive LR of 1.459 (95%CI 1.316-1.602) and a c-statistic of 0.565 (95%CI 0.530-0.600). Additional variables increased the c-statistic to 0.648 (95% CI 0.608-0.686). The total model best predicted survival at three months, c-statistic of 0.663 (95% CI 0.616-0.706). However, the total model c-statistic remained <0.70. CONCLUSIONS: The SQ, as a single factor, poorly predicts survival and should not be used to alter therapies. Adding additional objective variables improved prognostication, but further refinement and external validation are needed.

Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank.

Cardiometabolic diseases are the leading cause of death worldwide. Despite a known genetic component, our understanding of these diseases remains incomplete. Here, we analyzed the contribution of rare variants to 57 diseases and 26 cardiometabolic traits, using data from 200,337 UK Biobank participants with whole-exome sequencing. We identified 57 gene-based associations, with broad replication of novel signals in Geisinger MyCode. There was a striking risk associated with mutations in known Mendelian disease genes, including MYBPC3, LDLR, GCK, PKD1 and TTN. Many genes showed independent convergence of rare and common variant evidence, including an association between GIGYF1 and type 2 diabetes. We identified several large effect associations for height and 18 unique genes associated with blood lipid or glucose levels. Finally, we found that between 1.0% and 2.4% of participants carried rare potentially pathogenic variants for cardiometabolic disorders. These findings may facilitate studies aimed at therapeutics and screening of these common disorders.

Rare Coding Variants Associated With Electrocardiographic Intervals Identify Monogenic Arrhythmia Susceptibility Genes: A Multi-Ancestry Analysis.

BACKGROUND: Alterations in electrocardiographic (ECG) intervals are well-known markers for arrhythmia and sudden cardiac death (SCD) risk. While the genetics of arrhythmia syndromes have been studied, relations between electrocardiographic intervals and rare genetic variation at a population level are poorly understood. METHODS: Using a discovery sample of 29 000 individuals with whole-genome sequencing from Trans-Omics in Precision Medicine and replication in nearly 100 000 with whole-exome sequencing from the UK Biobank and MyCode, we examined associations between low-frequency and rare coding variants with 5 routinely measured electrocardiographic traits (RR, P-wave, PR, and QRS intervals and corrected QT interval). RESULTS: We found that rare variants associated with population-based electrocardiographic intervals identify established monogenic SCD genes (KCNQ1, KCNH2, and SCN5A), a controversial monogenic SCD gene (KCNE1), and novel genes (PAM and MFGE8) involved in cardiac conduction. Loss-of-function and pathogenic SCN5A variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of the first-degree atrioventricular block (P=8.4×10-5). Similar variants in KCNQ1 and KCNH2 (0.2% of individuals) were associated with a 23-fold increased odds of marked corrected QT interval prolongation (P=4×10-25), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal electrocardiographic intervals. CONCLUSIONS: Our findings indicate that large-scale high-depth sequence data and electrocardiographic analysis identifies monogenic arrhythmia susceptibility genes and rare variants with large effects. Known pathogenic variation in conventional arrhythmia and SCD genes exhibited incomplete penetrance and accounted for only a small fraction of marked electrocardiographic interval prolongation.

Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.

BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We hypothesized that a deep neural network could predict new-onset AF from the resting 12-lead ECG and that this prediction may help identify those at risk of AF-related stroke. METHODS: We used 1.6 M resting 12-lead digital ECG traces from 430 000 patients collected from 1984 to 2019. Deep neural networks were trained to predict new-onset AF (within 1 year) in patients without a history of AF. Performance was evaluated using areas under the receiver operating characteristic curve and precision-recall curve. We performed an incidence-free survival analysis for a period of 30 years following the ECG stratified by model predictions. To simulate real-world deployment, we trained a separate model using all ECGs before 2010 and evaluated model performance on a test set of ECGs from 2010 through 2014 that were linked to our stroke registry. We identified the patients at risk for AF-related stroke among those predicted to be high risk for AF by the model at different prediction thresholds. RESULTS: The area under the receiver operating characteristic curve and area under the precision-recall curve were 0.85 and 0.22, respectively, for predicting new-onset AF within 1 year of an ECG. The hazard ratio for the predicted high- versus low-risk groups over a 30-year span was 7.2 (95% CI, 6.9-7.6). In a simulated deployment scenario, the model predicted new-onset AF at 1 year with a sensitivity of 69% and specificity of 81%. The number needed to screen to find 1 new case of AF was 9. This model predicted patients at high risk for new-onset AF in 62% of all patients who experienced an AF-related stroke within 3 years of the index ECG. CONCLUSIONS: Deep learning can predict new-onset AF from the 12-lead ECG in patients with no previous history of AF. This prediction may help identify patients at risk for AF-related strokes.