Dynamic Risk Profiling Using Serial Tumor Biomarkers for Personalized Outcome Prediction.

Accurate prediction of long-term outcomes remains a challenge in the care of cancer patients. Due to the difficulty of serial tumor sampling, previous prediction tools have focused on pretreatment factors. However, emerging non-invasive diagnostics have increased opportunities for serial tumor assessments. We describe the Continuous Individualized Risk Index (CIRI), a method to dynamically determine outcome probabilities for individual patients utilizing risk predictors acquired over time. Similar to "win probability" models in other fields, CIRI provides a real-time probability by integrating risk assessments throughout a patient's course. Applying CIRI to patients with diffuse large B cell lymphoma, we demonstrate improved outcome prediction compared to conventional risk models. We demonstrate CIRI's broader utility in analogous models of chronic lymphocytic leukemia and breast adenocarcinoma and perform a proof-of-concept analysis demonstrating how CIRI could be used to develop predictive biomarkers for therapy selection. We envision that dynamic risk assessment will facilitate personalized medicine and enable innovative therapeutic paradigms.

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics.

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

The Evolutionary Landscape of Localized Prostate Cancers Drives Clinical Aggression.

The majority of newly diagnosed prostate cancers are slow growing, with a long natural life history. Yet a subset can metastasize with lethal consequences. We reconstructed the phylogenies of 293 localized prostate tumors linked to clinical outcome data. Multiple subclones were detected in 59% of patients, and specific subclonal architectures associate with adverse clinicopathological features. Early tumor development is characterized by point mutations and deletions followed by later subclonal amplifications and changes in trinucleotide mutational signatures. Specific genes are selectively mutated prior to or following subclonal diversification, including MTOR, NKX3-1, and RB1. Patients with low-risk monoclonal tumors rarely relapse after primary therapy (7%), while those with high-risk polyclonal tumors frequently do (61%). The presence of multiple subclones in an index biopsy may be necessary, but not sufficient, for relapse of localized prostate cancer, suggesting that evolution-aware biomarkers should be studied in prospective studies of low-risk tumors suitable for active surveillance.

Evaluating eligibility criteria of oncology trials using real-world data and AI.

There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging1-3. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes with real-world data using the computational framework of Trial Pathfinder. We apply Trial Pathfinder to emulate completed trials of advanced non-small-cell lung cancer using data from a nationwide database of electronic health records comprising 61,094 patients with advanced non-small-cell lung cancer. Our analyses reveal that many common criteria, including exclusions based on several laboratory values, had a minimal effect on the trial hazard ratios. When we used a data-driven approach to broaden restrictive criteria, the pool of eligible patients more than doubled on average and the hazard ratio of the overall survival decreased by an average of 0.05. This suggests that many patients who were not eligible under the original trial criteria could potentially benefit from the treatments. We further support our findings through analyses of other types of cancer and patient-safety data from diverse clinical trials. Our data-driven methodology for evaluating eligibility criteria can facilitate the design of more-inclusive trials while maintaining safeguards for patient safety.

Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7.

SARS-CoV-2 lineage B.1.1.7, a variant that was first detected in the UK in September 20201, has spread to multiple countries worldwide. Several studies have established that B.1.1.7 is more transmissible than pre-existing variants, but have not identified whether it leads to any change in disease severity2. Here we analyse a dataset that links 2,245,263 positive SARS-CoV-2 community tests and 17,452 deaths associated with COVID-19 in England from 1 November 2020 to 14 February 2021. For 1,146,534 (51%) of these tests, the presence or absence of B.1.1.7 can be identified because mutations in this lineage prevent PCR amplification of the spike (S) gene target (known as S gene target failure (SGTF)1). On the basis of 4,945 deaths with known SGTF status, we estimate that the hazard of death associated with SGTF is 55% (95% confidence interval, 39-72%) higher than in cases without SGTF after adjustment for age, sex, ethnicity, deprivation, residence in a care home, the local authority of residence and test date. This corresponds to the absolute risk of death for a 55-69-year-old man increasing from 0.6% to 0.9% (95% confidence interval, 0.8-1.0%) within 28 days of a positive test in the community. Correcting for misclassification of SGTF and missingness in SGTF status, we estimate that the hazard of death associated with B.1.1.7 is 61% (42-82%) higher than with pre-existing variants. Our analysis suggests that B.1.1.7 is not only more transmissible than pre-existing SARS-CoV-2 variants, but may also cause more severe illness.

Effect of Donor Sex on Recipient Mortality in Transfusion.

BACKGROUND: Conflicting observational evidence exists regarding the association between the sex of red-cell donors and mortality among transfusion recipients. Evidence to inform transfusion practice and policy is limited. METHODS: In this multicenter, double-blind trial, we randomly assigned patients undergoing red-cell transfusion to receive units of red cells from either male donors or female donors. Patients maintained their trial-group assignment throughout the trial period, including during subsequent inpatient and outpatient encounters. Randomization was conducted in a 60:40 ratio (male donor group to female donor group) to match the historical allocation of red-cell units from the blood supplier. The primary outcome was survival, with the male donor group as the reference group. RESULTS: A total of 8719 patients underwent randomization before undergoing transfusion; 5190 patients were assigned to the male donor group, and 3529 to the female donor group. At baseline, the mean (±SD) age of the enrolled patients was 66.8±16.4 years. The setting of the first transfusion was as an inpatient in 6969 patients (79.9%), of whom 2942 (42.2%) had been admitted under a surgical service. The baseline hemoglobin level before transfusion was 79.5±19.7 g per liter, and patients received a mean of 5.4±10.5 units of red cells in the female donor group and 5.1±8.9 units in the male donor group (difference, 0.3 units; 95% confidence interval [CI], -0.1 to 0.7). Over the duration of the trial, 1141 patients in the female donor group and 1712 patients in the male donor group died. In the primary analysis of overall survival, the adjusted hazard ratio for death was 0.98 (95% CI, 0.91 to 1.06). CONCLUSIONS: This trial showed no significant difference in survival between a transfusion strategy involving red-cell units from female donors and a strategy involving red-cell units from male donors. (Funded by the Canadian Institutes of Health Research; iTADS ClinicalTrials.gov number, NCT03344887.).

Recombinant or Standard-Dose Influenza Vaccine in Adults under 65 Years of Age.

BACKGROUND: Quadrivalent recombinant influenza vaccines contain three times the amount of hemagglutinin protein as standard-dose egg-based vaccines, and the recombinant formulation is not susceptible to antigenic drift during manufacturing. Data are needed on the relative effectiveness of recombinant vaccines as compared with standard-dose vaccines against influenza-related outcomes in adults under the age of 65 years. METHODS: In this cluster-randomized observational study, Kaiser Permanente Northern California facilities routinely administered either a high-dose recombinant influenza vaccine (Flublok Quadrivalent) or one of two standard-dose influenza vaccines during the 2018-2019 and 2019-2020 influenza seasons to adults 50 to 64 years of age (primary age group) and 18 to 49 years of age. Each facility alternated weekly between the two vaccine formulations. The primary outcome was influenza (A or B) confirmed by polymerase-chain-reaction (PCR) testing. Secondary outcomes included influenza A, influenza B, and influenza-related hospitalization outcomes. We used Cox regression analysis to estimate the hazard ratio of the recombinant vaccine as compared with the standard-dose vaccines against each outcome. We calculated the relative vaccine effectiveness as 1 minus the hazard ratio. RESULTS: The study population included 1,630,328 vaccinees between the ages of 18 and 64 years (632,962 in the recombinant-vaccine group and 997,366 in the standard-dose group). During this study period, 1386 cases of PCR-confirmed influenza were diagnosed in the recombinant-vaccine group and 2435 cases in the standard-dose group. Among the participants who were 50 to 64 years of age, 559 participants (2.00 cases per 1000) tested positive for influenza in the recombinant-vaccine group as compared with 925 participants (2.34 cases per 1000) in the standard-dose group (relative vaccine effectiveness, 15.3%; 95% confidence interval [CI], 5.9 to 23.8; P = 0.002). In the same age group, the relative vaccine effectiveness against influenza A was 15.7% (95% CI, 6.0 to 24.5; P = 0.002). The recombinant vaccine was not significantly more protective against influenza-related hospitalization than were the standard-dose vaccines. CONCLUSIONS: The high-dose recombinant vaccine conferred more protection against PCR-confirmed influenza than an egg-based standard-dose vaccine among adults between the ages of 50 and 64 years. (Funded by Sanofi; ClinicalTrials.gov number, NCT03694392.).

Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data.

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

The single-cell pathology landscape of breast cancer.

Single-cell analyses have revealed extensive heterogeneity between and within human tumours1-4, but complex single-cell phenotypes and their spatial context are not at present reflected in the histological stratification that is the foundation of many clinical decisions. Here we use imaging mass cytometry5 to simultaneously quantify 35 biomarkers, resulting in 720 high-dimensional pathology images of tumour tissue from 352 patients with breast cancer, with long-term survival data available for 281 patients. Spatially resolved, single-cell analysis identified the phenotypes of tumour and stromal single cells, their organization and their heterogeneity, and enabled the cellular architecture of breast cancer tissue to be characterized on the basis of cellular composition and tissue organization. Our analysis reveals multicellular features of the tumour microenvironment and novel subgroups of breast cancer that are associated with distinct clinical outcomes. Thus, spatially resolved, single-cell analysis can characterize intratumour phenotypic heterogeneity in a disease-relevant manner, with the potential to inform patient-specific diagnosis.

Factors associated with COVID-19-related death using OpenSAFELY.

Coronavirus disease 2019 (COVID-19) has rapidly affected mortality worldwide1. There is unprecedented urgency to understand who is most at risk of severe outcomes, and this requires new approaches for the timely analysis of large datasets. Working on behalf of NHS England, we created OpenSAFELY-a secure health analytics platform that covers 40% of all patients in England and holds patient data within the existing data centre of a major vendor of primary care electronic health records. Here we used OpenSAFELY to examine factors associated with COVID-19-related death. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19-related deaths. COVID-19-related death was associated with: being male (hazard ratio (HR) 1.59 (95% confidence interval 1.53-1.65)); greater age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared with people of white ethnicity, Black and South Asian people were at higher risk, even after adjustment for other factors (HR 1.48 (1.29-1.69) and 1.45 (1.32-1.58), respectively). We have quantified a range of clinical factors associated with COVID-19-related death in one of the largest cohort studies on this topic so far. More patient records are rapidly being added to OpenSAFELY, we will update and extend our results regularly.

Longer CAG repeat length is associated with shorter survival after disease onset in Huntington disease.

It is well known that the length of the CAG trinucleotide expansion of the huntingtin gene is associated with many aspects of Huntington disease progression. These include age of clinical onset and rate of initial progression of disease severity. The relationship between CAG length and survival in Huntington disease is less studied. To address this, we obtained the complete Registry HD database from the European Huntington Disease Network and reanalyzed the time from reported age of disease onset until death. We conducted semiparametric proportional hazards modeling of 8,422 participants who had experienced onset of clinical Huntington disease, either retrospectively or prospectively. Of these, 826 had a recorded age of death. To avoid biased model estimates, retrospective onset ages were represented by left truncation at study entry. After controlling for onset age, which tends to be younger in those with longer CAG repeat lengths, we found that CAG length had a substantial and highly significant influence upon survival time after disease onset. For a fixed age of onset, longer CAG expansions were predictive of shorter survival. This is consistent with other known relationships between CAG length and disease severity. We also show that older onset age predicts shorter lifespan after controlling for CAG length and that the influence of CAG on survival length is substantially greater in women. We demonstrate that apparent contradictions between these and previous analyses of the same data are primarily due to the question of whether to control for clinical onset age in the analysis of time until death.

Darolutamide and Survival in Metastatic, Hormone-Sensitive Prostate Cancer.

BACKGROUND: Darolutamide is a potent androgen-receptor inhibitor that has been associated with increased overall survival among patients with nonmetastatic, castration-resistant prostate cancer. Whether a combination of darolutamide, androgen-deprivation therapy, and docetaxel would increase survival among patients with metastatic, hormone-sensitive prostate cancer is unknown. METHODS: In this international, phase 3 trial, we randomly assigned patients with metastatic, hormone-sensitive prostate cancer in a 1:1 ratio to receive darolutamide (at a dose of 600 mg [two 300-mg tablets] twice daily) or matching placebo, both in combination with androgen-deprivation therapy and docetaxel. The primary end point was overall survival. RESULTS: The primary analysis involved 1306 patients (651 in the darolutamide group and 655 in the placebo group); 86.1% of the patients had disease that was metastatic at the time of the initial diagnosis. At the data cutoff date for the primary analysis (October 25, 2021), the risk of death was significantly lower, by 32.5%, in the darolutamide group than in the placebo group (hazard ratio 0.68; 95% confidence interval, 0.57 to 0.80; P<0.001). Darolutamide was also associated with consistent benefits with respect to the secondary end points and prespecified subgroups. Adverse events were similar in the two groups, and the incidences of the most common adverse events (occurring in ≥10% of the patients) were highest during the overlapping docetaxel treatment period in both groups. The frequency of grade 3 or 4 adverse events was 66.1% in the darolutamide group and 63.5% in the placebo group; neutropenia was the most common grade 3 or 4 adverse event (in 33.7% and 34.2%, respectively). CONCLUSIONS: In this trial involving patients with metastatic, hormone-sensitive prostate cancer, overall survival was significantly longer with the combination of darolutamide, androgen-deprivation therapy, and docetaxel than with placebo plus androgen-deprivation therapy and docetaxel, and the addition of darolutamide led to improvement in key secondary end points. The frequency of adverse events was similar in the two groups. (Funded by Bayer and Orion Pharma; ARASENS ClinicalTrials.gov number, NCT02799602.).

Characterizing quantile-varying covariate effects under the accelerated failure time model.

An important task in survival analysis is choosing a structure for the relationship between covariates of interest and the time-to-event outcome. For example, the accelerated failure time (AFT) model structures each covariate effect as a constant multiplicative shift in the outcome distribution across all survival quantiles. Though parsimonious, this structure cannot detect or capture effects that differ across quantiles of the distribution, a limitation that is analogous to only permitting proportional hazards in the Cox model. To address this, we propose a general framework for quantile-varying multiplicative effects under the AFT model. Specifically, we embed flexible regression structures within the AFT model and derive a novel formula for interpretable effects on the quantile scale. A regression standardization scheme based on the g-formula is proposed to enable the estimation of both covariate-conditional and marginal effects for an exposure of interest. We implement a user-friendly Bayesian approach for the estimation and quantification of uncertainty while accounting for left truncation and complex censoring. We emphasize the intuitive interpretation of this model through numerical and graphical tools and illustrate its performance through simulation and application to a study of Alzheimer's disease and dementia.

Modeling biomarker variability in joint analysis of longitudinal and time-to-event data.

The role of visit-to-visit variability of a biomarker in predicting related disease has been recognized in medical science. Existing measures of biological variability are criticized for being entangled with random variability resulted from measurement error or being unreliable due to limited measurements per individual. In this article, we propose a new measure to quantify the biological variability of a biomarker by evaluating the fluctuation of each individual-specific trajectory behind longitudinal measurements. Given a mixed-effects model for longitudinal data with the mean function over time specified by cubic splines, our proposed variability measure can be mathematically expressed as a quadratic form of random effects. A Cox model is assumed for time-to-event data by incorporating the defined variability as well as the current level of the underlying longitudinal trajectory as covariates, which, together with the longitudinal model, constitutes the joint modeling framework in this article. Asymptotic properties of maximum likelihood estimators are established for the present joint model. Estimation is implemented via an Expectation-Maximization (EM) algorithm with fully exponential Laplace approximation used in E-step to reduce the computation burden due to the increase of the random effects dimension. Simulation studies are conducted to reveal the advantage of the proposed method over the two-stage method, as well as a simpler joint modeling approach which does not take into account biomarker variability. Finally, we apply our model to investigate the effect of systolic blood pressure variability on cardiovascular events in the Medical Research Council elderly trial, which is also the motivating example for this article.

Lisocabtagene maraleucel as second-line therapy for large B-cell lymphoma: primary analysis of the phase 3 TRANSFORM study.

This global phase 3 study compared lisocabtagene maraleucel (liso-cel) with a standard of care (SOC) as second-line therapy for primary refractory or early relapsed (≤12 months) large B-cell lymphoma (LBCL). Adults eligible for autologous stem cell transplantation (ASCT; N = 184) were randomly assigned in a 1:1 ratio to liso-cel (100 × 106 chimeric antigen receptor-positive T cells) or SOC (3 cycles of platinum-based immunochemotherapy followed by high-dose chemotherapy and ASCT in responders). The primary end point was event-free survival (EFS). In this primary analysis with a 17.5-month median follow-up, median EFS was not reached (NR) for liso-cel vs 2.4 months for SOC. Complete response (CR) rate was 74% for liso-cel vs 43% for SOC (P < .0001) and median progression-free survival (PFS) was NR for liso-cel vs 6.2 months for SOC (hazard ratio [HR] = 0.400; P < .0001). Median overall survival (OS) was NR for liso-cel vs 29.9 months for SOC (HR = 0.724; P = .0987). When adjusted for crossover from SOC to liso-cel, 18-month OS rates were 73% for liso-cel and 54% for SOC (HR = 0.415). Grade 3 cytokine release syndrome and neurological events occurred in 1% and 4% of patients in the liso-cel arm, respectively (no grade 4 or 5 events). These data show significant improvements in EFS, CR rate, and PFS for liso-cel compared with SOC and support liso-cel as a preferred second-line treatment compared with SOC in patients with primary refractory or early relapsed LBCL. This trial was registered at www.clinicaltrials.gov as #NCT03575351.

Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort.

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.

Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss-Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.

Normal Lung Function and Mortality in World Trade Center Responders and National Health and Nutrition Examination Survey III Participants.

Rationale: Low FEV1 is a biomarker of increased mortality. The association of normal lung function and mortality is not well described. Objectives: To evaluate the FEV1-mortality association among participants with normal lung function. Methods: A total of 10,999 Fire Department of the City of New York (FDNY) responders and 10,901 Third National Health and Nutrition Examination Survey (NHANES III) participants, aged 18-65 years with FEV1 ⩾80% predicted, were analyzed, with FEV1 percent predicted calculated using Global Lung Function Initiative Global race-neutral reference equations. Mortality data were obtained from linkages to the National Death Index. Cox proportional hazards models estimated the association between FEV1 and all-cause mortality, controlling for age, sex, race/ethnicity, smoking history, and, for FDNY, work assignment. Cohorts were followed for a maximum of 20.3 years. Measurements and Main Results: We observed 504 deaths (4.6%) of 10,999 for FDNY and 1,237 deaths (9.4% [weighted]) of 10,901 for NHANES III. Relative to FEV1 ⩾120% predicted, mortality was significantly higher for FEV1 100-109%, 90-99%, and 80-89% predicted in the FDNY cohort. In the NHANES III cohort, mortality was significantly higher for FEV1 90-99% and 80-89% predicted. Each 10% higher predicted FEV1 was associated with 15% (hazard ratio, 0.85; 95% confidence interval, 0.80-0.91) and 23% (hazard ratio, 0.77; 95% confidence interval, 0.71-0.84) lower mortality for FDNY and NHANES III, respectively. Conclusions: In both cohorts, higher FEV1 is associated with lower mortality, suggesting higher FEV1 is a biomarker of better health. These findings demonstrate that a single cross-sectional measurement of FEV1 is predictive of mortality over two decades, even when FEV1 is in the normal range.

Higher Molecular Injury at Diagnosis of Acute Cellular Rejection Increases the Risk of Lung Allograft Failure: A Clinical Trial.

Rationale: The association of acute cellular rejection (ACR) with chronic lung allograft dysfunction (CLAD) in lung transplant recipients has primarily been described before consensus recommendations incorporating restrictive phenotypes. Furthermore, the association of the degree of molecular allograft injury during ACR with CLAD or death remains undefined. Objectives: To investigate the association of ACR with the risk of CLAD or death and to further investigate if this risk depends on the degree of molecular allograft injury. Methods: This multicenter, prospective cohort study included 188 lung transplant recipients. Subjects underwent serial plasma collections for donor-derived cell-free DNA (dd-cfDNA) at prespecified time points and bronchoscopy. Multivariable Cox proportional-hazards analysis was conducted to analyze the association of ACR with subsequent CLAD or death as well as the association of dd-cfDNA during ACR with risk of CLAD or death. Additional outcomes analyses were performed with episodes of ACR categorized as "high risk" (dd-cfDNA ⩾ 1%) and "low risk" (dd-cfDNA < 1%). Measurements and Main Results: In multivariable analysis, ACR was associated with the composite outcome of CLAD or death (hazard ratio [HR], 2.07 [95% confidence interval (CI), 1.05-4.10]; P = 0.036). Elevated dd-cfDNA ⩾ 1% at ACR diagnosis was independently associated with increased risk of CLAD or death (HR, 3.32; 95% CI, 1.31-8.40; P = 0.012). Patients with high-risk ACR were at increased risk of CLAD or death (HR, 3.13; 95% CI, 1.41-6.93; P = 0.005), whereas patients with low-risk status ACR were not. Conclusions: Patients with ACR are at higher risk of CLAD or death, but this may depend on the degree of underlying allograft injury at the molecular level. Clinical trial registered with www.clinicaltrials.gov (NCT02423070).

Five-Year Outcomes among U.S. Bronchiectasis and NTM Research Registry Patients.

Rationale: Nontuberculous mycobacteria (NTM) are prevalent among patients with bronchiectasis. However, the long-term natural history of patients with NTM and bronchiectasis is not well described. Objectives: To assess the impact of NTM on 5-year clinical outcomes and mortality in patients with bronchiectasis. Methods: Patients in the Bronchiectasis and NTM Research Registry with ⩾5 years of follow-up were eligible. Data were collected for all-cause mortality, lung function, exacerbations, hospitalizations, and disease severity. Outcomes were compared between patients with and without NTM at baseline. Mortality was assessed using Cox proportional hazards models and the log-rank test. Measurements and Main Results: In total, 2,634 patients were included: 1,549 (58.8%) with and 1,085 (41.2%) without NTM at baseline. All-cause mortality (95% confidence interval) at Year 5 was 12.1% (10.5%, 13.7%) overall, 12.6% (10.5%, 14.8%) in patients with NTM, and 11.5% (9.0%, 13.9%) in patients without NTM. Independent predictors of 5-year mortality were baseline FEV1 percent predicted, age, hospitalization within 2 years before baseline, body mass index, and sex (all P < 0.01). The probabilities of acquiring NTM or Pseudomonas aeruginosa were approximately 4% and 3% per year, respectively. Spirometry, exacerbations, and hospitalizations were similar, regardless of NTM status, except that annual exacerbations were lower in patients with NTM (P < 0.05). Conclusions: Outcomes, including exacerbations, hospitalizations, rate of loss of lung function, and mortality rate, were similar across 5 years in patients with bronchiectasis with or without NTM.