Brand-specific estimates of influenza vaccine effectiveness for the 2021-2022 season in Europe: results from the DRIVE multi-stakeholder study platform.

Introduction: Development of Robust and Innovative Vaccine Effectiveness (DRIVE) was a European public-private partnership (PPP) that aimed to provide annual, brand-specific estimates of influenza vaccine effectiveness (IVE) for regulatory and public health purposes. DRIVE was launched in 2017 under the umbrella of the Innovative Medicines Initiative (IMI) and conducted IVE studies from its pilot season in 2017-2018 to its final season in 2021-2022. Methods: In 2021-2022, DRIVE conducted four primary care-based test-negative design (TND) studies (Austria, Italy, Iceland, and England; involving >1,000 general practitioners), nine hospital-based TND studies (France, Iceland, Italy, Romania, and Spain, for a total of 21 hospitals), and one population-based cohort study in Finland. In the TND studies, patients with influenza-like illness (primary care) or severe acute respiratory infection (hospital) were enrolled, and laboratory tested for influenza using RT-PCR. Study contributor-specific IVE was calculated using logistic regression, adjusting for age, sex, and calendar time, and pooled by meta-analysis. Results: In 2021-2022, pooled confounder-adjusted influenza vaccine effectiveness (IVE) estimates against laboratory-confirmed influenza (LCI) overall and per type and subtype/lineage was produced, albeit with wide confidence intervals (CI). The limited circulation of influenza in Europe did not allow the network to reach the optimal sample size to produce precise IVE estimates for all the brands included. The most significant IVE estimates were 76% (95% CI 23%-93%) for any vaccine and 81% (22%-95%) for Vaxigrip Tetra in adults ≥65 years old and 64% (25%-83%) for Fluenz Tetra in children (TND primary care setting), 85% (12%-97%) for any vaccine in adults 18-64 years (TND hospital setting), and 38% (1%-62%) in children 6 months-6 years (population-based cohort, mixed setting). Discussion: Over five seasons, DRIVE collected data on >35,000 patients, more than 60 variables, and 13 influenza vaccines. DRIVE demonstrated that estimating brand-specific IVE across Europe is possible, but achieving sufficient sample size to obtain precise estimates for all relevant stratifications remains a challenge. Finally, DRIVE's network of study contributors and lessons learned have greatly contributed to the development of the COVID-19 vaccine effectiveness platform COVIDRIVE.

Geographic and Population Distributions of Human Immunodeficiency Virus (HIV)-1 and HIV-2 Circulating Subtypes: A Systematic Literature Review and Meta-analysis (2010-2021).

BACKGROUND: HIV poses significant challenges for vaccine development due to its high genetic mutation and recombination rates. Understanding the distribution of HIV subtypes (clades) across regions and populations is crucial. In this study, a systematic review of the past decade was conducted to characterize HIV-1/HIV-2 subtypes. METHODS: A comprehensive search was performed in PubMed, EMBASE, and CABI Global Health, yielding 454 studies from 91 countries. RESULTS: Globally, circulating recombinant forms (CRFs)/unique recombinant forms (URFs) accounted for 29% of HIV-1 strains, followed by subtype C (23%) and subtype A (17%). Among studies reporting subtype breakdowns in key populations, 62% of HIV infections among men who have sex with men (MSM) and 38% among people who inject drugs (PWIDs) were CRF/URFs. Latin America and the Caribbean exhibited a 25% increase in other CRFs (excluding CRF01_AE or CRF02_AG) prevalence between 2010-2015 and 2016-2021. CONCLUSIONS: This review underscores the global distribution of HIV subtypes, with an increasing prevalence of CRFs and a lower prevalence of subtype C. Data on HIV-2 were limited. Understanding subtype diversity is crucial for vaccine development, which need to elicit immune responses capable of targeting various subtypes. Further research is needed to enhance our knowledge and address the challenges posed by HIV subtype diversity.

Investigating confounding in network-based test-negative design influenza vaccine effectiveness studies-Experience from the DRIVE project.

BACKGROUND: Establishing a large study network to conduct influenza vaccine effectiveness (IVE) studies while collecting appropriate variables to account for potential bias is important; the most relevant variables should be prioritized. We explored the impact of potential confounders on IVE in the DRIVE multi-country network of sites conducting test-negative design (TND) studies. METHODS: We constructed a directed acyclic graph (DAG) to map the relationship between influenza vaccination, medically attended influenza infection, confounders, and other variables. Additionally, we used the Development of Robust and Innovative Vaccines Effectiveness (DRIVE) data from the 2018/2019 and 2019/2020 seasons to explore the effect of covariate adjustment on IVE estimates. The reference model was adjusted for age, sex, calendar time, and season. The covariates studied were presence of at least one, two, or three chronic diseases; presence of six specific chronic diseases; and prior healthcare use. Analyses were conducted by site and subsequently pooled. RESULTS: The following variables were included in the DAG: age, sex, time within influenza season and year, health status and comorbidities, study site, health-care-seeking behavior, contact patterns and social precautionary behavior, socioeconomic status, and pre-existing immunity. Across all age groups and settings, only adjustment for lung disease in older adults in the primary care setting resulted in a relative change of the IVE point estimate >10%. CONCLUSION: Our study supports a parsimonious approach to confounder adjustment in TND studies, limited to adjusting for age, sex, and calendar time. Practical implications are that necessitating fewer variables lowers the threshold for enrollment of sites in IVE studies and simplifies the pooling of data from different IVE studies or study networks.

Estimating baseline rates of adverse perinatal and neonatal outcomes using a facility-based surveillance approach: A prospective observational study from the WHO Global Vaccine Safety Multi-Country Collaboration on safety in pregnancy.

Background: Most perinatal and neonatal deaths occur in low- and middle-income countries (LMICs), yet, quality data on burden of adverse outcomes of pregnancy is limited in such countries. Methods: A network of 21 maternity units, across seven countries, undertook surveillance for low birthweight, preterm birth, small for gestational age (SGA), stillbirths, congenital microcephaly, in-hospital neonatal deaths, and neonatal infections in a cohort of over 85,000 births from May 2019 - August 2020. For each outcome, site-specific rates per 1,000 livebirths (or per 1,000 total births for stillbirth) and 95% confidence intervals (CI) were calculated. Descriptive sensitivity analysis was conducted to gain insight regarding underreporting of four outcomes at 16 sites. Findings: Estimated rates varied across countries and sites, ranging between 43·3-329·5 and 21·4-276·6/1000 livebirths for low birthweight and preterm birth respectively and 11·8-81/1,000 livebirths for SGA. No cases of congenital microcephaly were reported by three sites while the highest estimated rate was 13/1,000 livebirths. Neonatal infection and neonatal death rates varied between 1·8-73 and 0-59·9/1000 livebirths respectively while stillbirth rates ranged between 0-57·1/1000 total births across study sites. Results from the sensitivity analysis confirmed the underreporting of congenital microcephaly and SGA in our study. Interpretation: Our study establishes site-specific baseline rates for important adverse perinatal and neonatal outcomes and addresses a critical evidence gap towards improved monitoring of benefits and risks of emerging pregnancy and neonatal interventions. Funding: The study was sponsored by the World Health Organization with funding from the Bill and Melinda Gates Foundation.

WHO global vaccine safety multi-country collaboration project on safety in pregnancy: Assessing the level of diagnostic certainty using standardized case definitions for perinatal and neonatal outcomes and maternal immunization.

Standardized case definitions strengthen post-marketing safety surveillance of new vaccines by improving generated data, interpretation and comparability across surveillance systems. The Global Alignment of Immunization Safety Assessment in Pregnancy (GAIA) project developed standardized case definitions for 21 key obstetric and neonatal terms following the Brighton Collaboration (BC) methodology. In this prospective cohort study, we assessed the applicability of GAIA definitions for maternal immunization exposure and for low birth weight (LBW), preterm birth, small for gestational age (SGA), stillbirth, neonatal death, neonatal infection, and congenital microcephaly. We identified the missing data elements that prevented identified cases and exposures from meeting the case definition (level 1-3 of BC diagnostic certainty). Over a one-year period (2019-2020), all births occurring in 21 sites (mostly secondary and tertiary hospitals) in 6 Low Middle Income Countries and 1 High Income Country were recorded and the 7 perinatal and neonatal outcome cases were identified from routine medical records. Up to 100 cases per outcome were recruited sequentially from each site. Most cases recruited for LBW, preterm birth and neonatal death met the GAIA case definitions. Birth weight, a key parameter for all three outcomes, was routinely recorded at all sites. The definitions for SGA, stillbirth, neonatal infection (particularly meningitis and respiratory infection) and congenital microcephaly were found to be less applicable. The main barrier to obtaining higher levels of diagnostic certainty was the lack of sonographic documentation of gestational age in first or second trimester. The definition for maternal immunization exposure was applicable, however, the highest level of diagnostic certainty was only reached at two sites. Improved documentation of maternal immunization will be important for vaccine safety studies. Following the field-testing of these 8 GAIA definitions, several improvements are suggested that may lead to their easier implementation, increased standardization and hence comparison across studies.

Public health impact of low-dose aspirin on colorectal cancer, cardiovascular disease and safety in the UK - Results from micro-simulation model.

BACKGROUND: Low-dose aspirin therapy reduces the risk of cardiovascular disease and may have a positive effect on the prevention of colorectal cancer. We evaluated the population-level expected effect of regular low-dose aspirin use on cardiovascular disease (CVD), colorectal cancer (CRC), gastrointestinal bleeding, symptomatic peptic ulcers, and intracranial hemorrhage, using a microsimulation study design. METHODS: We used individual-level state transition modeling to assess the impact of aspirin in populations aged 50-59 or 60-69 years old indicated for low-dose aspirin usage for primary or secondary CVD prevention. Model parameters were based on data from governmental agencies from the UK or recent publications. RESULTS: In the 50-59 years cohort, a decrease in incidence rates (IRs per 100 000 person years) of non-fatal CVD (-203 and -794) and fatal CVD (-97 and-381) was reported in the primary and secondary CVD prevention setting, respectively. The IR reduction of CRC (-96 and -93) was similar for primary and secondary CVD prevention. The IR increase of non-fatal (116 and 119) and fatal safety events (6 and 6) was similar for primary and secondary CVD prevention. Similar results were obtained for the 60-69 years cohort. CONCLUSIONS: The decrease in fatal CVD and CRC events was larger than the increase in fatal safety events and this difference was more pronounced when low-dose aspirin was used for secondary compared to primary CVD prevention. These results provide a comprehensive image of the expected effect of regular low-dose aspirin therapy in a UK population indicated to use aspirin for CVD prevention.

Brand-specific influenza vaccine effectiveness estimates during 2019/20 season in Europe - Results from the DRIVE EU study platform.

DRIVE (Development of Robust and Innovative Vaccine Effectiveness) is an IMI funded public-private platform that aims to annually estimate brand-specific influenza vaccine effectiveness (IVE), for public health and regulatory purposes. IVE analyses and reporting are conducted by public partners in the consortium. In 2019/20, four primary care-based test-negative design (TND) studies (Austria, England, Italy (n = 2)), eight hospital-based TND studies (Finland, France, Italy, Romania, Spain (n = 4)), and one population-based cohort study (Finland) were conducted. The COVID-19 pandemic affected influenza surveillance in all participating study sites, therefore the study period was truncated on February 29, 2020. Age-stratified (6 m-17y, 18-64y, ≥65y), confounder-adjusted, site-specific adjusted IVE estimates were calculated and pooled through meta-analysis. Parsimonious confounder-adjustment was performed, adjusting the estimates for age, sex and calendar time. TND studies included 3531 cases (351 vaccinated) and 5546 controls (1415 vaccinated) of all ages. IVE estimates were available for 8/11 brands marketed in Europe in 2019. Most children and adults < 64y were captured in primary care setting and the most frequently observed vaccine brand was Vaxigrip Tetra. The estimate against any influenza for Vaxigrip Tetra in primary care setting was 61% (95%CI 38-77) in children and 32% (95%CI -13-59) in adults up to 64y. Most adults ≥ 65y were captured in hospital setting and the most frequently observed brand was Fluad, with an estimate of 52% (95%CI 27-68). The population-based cohort covered 511,854 person-years and two vaccine brands. In children aged 2-6y, the IVE against any influenza was 68% (95%CI 58-75) for Fluenz Tetra and 71% (56-80) for Vaxigrip Tetra. In adults ≥ 65y, IVE against any influenza was 29% (20-36) for Vaxigrip Tetra. DRIVE is a growing platform. Public health institutes with surveillance data and hospitals in countries with high influenza vaccine coverage are encouraged to join DRIVE.

Vaccine effectiveness against laboratory-confirmed influenza in Europe - Results from the DRIVE network during season 2018/19.

The DRIVE project aims to establish a sustainable network to estimate brand-specific influenza vaccine effectiveness (IVE) annually. DRIVE is a public-private partnership launched in response to EMA guidance that requires effectiveness evaluation from manufacturers for all individual influenza vaccine brands every season. IVE studies are conducted by public partners in DRIVE. Private partners (vaccine manufacturers from the European Federation of Pharmaceutical Industries and Association (EFPIA)) provide written feedback moderated by an independent scientific committee. Test-negative design (TND) case-control studies (4 in primary care and five in hospital) were conducted in six countries in Europe during the 2018/19 season. Site-specific confounder-adjusted vaccine effectiveness (VE) estimates for any vaccine exposure were calculated by age group (<18 years (y), 18-64y and 65 + y) and pooled by setting (primary care, hospital) through random effects meta-analysis. In addition, one population-based cohort study was conducted in Finland. TND studies included 3339 cases and 6012 controls; seven vaccine brands were reported. For ages 65 + y, pooled VE against any influenza strain was estimated at 27% (95%CI 6-44) in hospital setting. Sample size was insufficient for meaningful IVE estimates in other age groups, in the primary care setting, or by vaccine brand. The population-based cohort study included 274,077 vaccinated and 494,337 unvaccinated person-years, two vaccine brands were reported. Brand-specific IVE was estimated for Fluenz Tetra (36% [95%CI 24-45]) for ages 2-6y, Vaxigrip Tetra (54% [43-62]) for ages 6 months to 6y, and Vaxigrip Tetra (30% [25-35]) for ages 65 + y. The results presented are from the second influenza season covered by the DRIVE network. While sample size from the pooled TND studies was still too low for precise (brand-specific) IVE estimates, the network has approximately doubled in size compared to the pilot season. Taking measures to increase sample size is an important focus of DRIVE for the coming years.

On estimating the time to statistical cure.

BACKGROUND: The mortality risk among cancer patients measured from the time of diagnosis is often elevated in comparison to the general population. However, for some cancer types, the patient mortality risk will over time reach the same level as the general population mortality risk. The time point at which the mortality risk reaches the same level as the general population is called the cure point and is of great interest to patients, clinicians, and health care planners. In previous studies, estimation of the cure point has been handled in an ad hoc fashion, often without considerations about margins of clinical relevance. METHODS: We review existing methods for estimating the cure point and discuss new clinically relevant measures for quantifying the mortality difference between cancer patients and the general population, which can be used for cure point estimation. The performance of the methods is assessed in a simulation study and the methods are illustrated on survival data from Danish colon cancer patients. RESULTS: The simulations revealed that the bias of the estimated cure point depends on the measure chosen for quantifying the excess mortality, the chosen margin of clinical relevance, and the applied estimation procedure. These choices are interdependent as the choice of mortality measure depends both on the ability to define a margin of clinical relevance and the ability to accurately compute the mortality measure. The analysis of cancer survival data demonstrates the importance of considering the confidence interval of the estimated cure point, as these may be wide in some scenarios limiting the applicability of the estimated cure point. CONCLUSIONS: Although cure points are appealing in a clinical context and has widespread applicability, estimation remains a difficult task. The estimation relies on a number of choices, each associated with pitfalls that the practitioner should be aware of.

Outlier robust modeling of survival curves in the presence of potentially time-varying coefficients.

In time to event studies, censoring often occurs and models that take this into account are wide-spread. In the presence of outliers, standard estimators of model parameters may be affected such that results and conclusions are not reliable anymore. This in turn also hampers the detection of these outliers due to masking effects. To cope with outliers when using proportional hazard models, we propose to use the Brier score as a loss function. Since the coefficients often vary over time, we focus on the piecewise constant hazard model, which can flexibly model time-varying coefficients if a large number of cut-points is used. To prevent overfitting, we add a penalty term that potentially shrinks time-varying effects to constant effects. By fitting the coefficients of the piecewise constant hazard model using a penalized Brier score loss, we obtain a robust model that can handle time-varying coefficients. Its good performance is illustrated in a simulation study and using two datasets from practice.

COO and MYC/BCL2 status do not predict outcome among patients with stage I/II DLBCL: a retrospective multicenter study.

In advanced-stage diffuse large B-cell lymphoma (DLBCL), the presence of an activated B-cell phenotype or a non-germinal center (GCB) phenotype, coexpression of MYC and BCL2 by immunohistochemistry, and the cooccurrence of MYC and BCL2 or BCL6 rearrangements are associated with inferior outcomes. It is unclear whether these variables remain prognostic in stage I/II patients. In this retrospective study, we evaluated the prognostic impact of cell of origin (COO), as well as dual-expressor (DE) status and molecular double-hit (DH) status, in stage I/II DLBCL by positron emission tomography with computed tomography (PET-CT). A total of 211 patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone)-like regimens, with or without radiotherapy, was included. The median follow-up in the entire cohort was 4 years (range, 0.4-9.4), with estimated 4-year progression-free survival (PFS) and overall survival (OS) rates of 85% (95% confidence interval [CI], 79-89) and 88% (95% CI, 83-92), respectively. By univariable analysis, DE (PFS: hazard ratio [HR], 1.27; 95% CI, 0.58-2.81, P = .55 and OS: HR, 1.40; 95% CI, 0.60-3.30; P = .44), DH (PFS: HR, 1.21; 95% CI, 0.27-5.31; P = .80 and OS: HR, 0.61; 95% CI, 0.08-4.73; P = .64), and non-GCB status (PFS: HR, 1.59; 95% CI, 0.83-3.03; P = .16 and OS: HR, 1.80; 95% CI, 0.89-3.67; P = .10) were associated with poorer outcomes. In patients with PET-CT-defined stage I/II DLBCL treated with R-CHOP-like therapy, with or without radiation, COO and DE and DH status were not significantly associated with inferior PFS or OS.

Risk of death, relapse or progression, and loss of life expectancy at different progression-free survival milestones in primary central nervous system lymphoma.

In this study, we analyzed the evolution of the prognosis of primary central nervous system lymphoma (PCNSL) patients as they reach selected progression-free survival (PFS) milestones after high-dose methotrexate (HD-MTX)-based therapy. In total, 258 and 146 patients were included from Denmark and British Columbia, respectively. All patients were diagnosed during 2000-2017. The 5-year PFS was 27% (95% CI 23; 32); however, for patients reaching 5 years of PFS, this increased to 71% (95% CI 57; 86). Within the first 5 years after diagnosis, patients lost 2.0 years (95% CI 1.8; 2.2) when compared to a similar background population. This reduced to 0.5 years (95% CI 0.2; 0.9) for patients reaching 5 years of PFS. Treatment with rituximab was associated with improved outcomes. The prognosis of patients with PCNSL treated with HD-MTX-based regimens in this cohort is poor, although it improves as patients survive without progression/relapse. However, survival does not conclusively normalize to that of a similar background population.

Relapse Risk and Loss of Lifetime After Modern Combined Modality Treatment of Young Patients With Hodgkin Lymphoma: A Nordic Lymphoma Epidemiology Group Study.

PURPOSE: Estimates of short- and long-term survival for young patients with classic Hodgkin lymphoma (cHL) are of considerable interest. We investigated cHL prognosis in the era of contemporary treatment at different milestones during the follow-up. PATIENTS AND METHODS: On the basis of a Nordic cohort of 2,582 patients diagnosed at ages 18 to 49 years between 2000 and 2013, 5-year relapse risks and 5-year restricted losses in expectation of lifetime were estimated for all patients and for patients who achieved event-free survival (EFS) for 12 (EFS12), 24 (EFS24), 36 (EFS36) or 60 (EFS60) months. The median follow-up time was 9 years (range, 2.9 to 16.8 years). RESULTS: The 5-year overall survival was 95% (95% CI, 94% to 96%). The 5-year risk of relapse was 13.4% (95% CI, 12.1% to 14.8%) overall but decreased to 4.2% (95% CI, 3.8% to 4.6%) given that patients reached EFS24. Relapse risk for patients treated with six to eight courses of bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone (BEACOPP) was comparable to that of patients treated with six to eight courses of doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) despite more adverse risk criteria among patients treated with BEACOPP. Both from diagnosis and if EFS24 was reached, the losses in expectation of lifetime during the following 5 years were small (from diagnosis, 45 days [95% CI, 35 to 54 days] and for patients who reached EFS24, 13 days [95% CI, 7 to 20 days]). In stage-stratified analyses of 5-year restricted loss in expectation of lifetime, patients with stages I to IIA disease had no noteworthy excess risk of death after they reached EFS24, whereas risk remained measurable for patients with stages IIB to IV cHL. CONCLUSION: Real-world data on young patients with cHL from the Nordic countries show excellent outcomes. The outlook is particularly favorable for patients who reach EFS24, which supports limited relapse-oriented clinical follow-up.

Estimating the loss of lifetime function using flexible parametric relative survival models.

BACKGROUND: Within cancer care, dynamic evaluations of the loss in expectation of life provides useful information to patients as well as physicians. The loss of lifetime function yields the conditional loss in expectation of life given survival up to a specific time point. Due to the inevitable censoring in time-to-event data, loss of lifetime estimation requires extrapolation of both the patient and general population survival function. In this context, the accuracy of different extrapolation approaches has not previously been evaluated. METHODS: The loss of lifetime function was computed by decomposing the all-cause survival function using the relative and general population survival function. To allow extrapolation, the relative survival function was fitted using existing parametric relative survival models. In addition, we introduced a novel mixture cure model suitable for extrapolation. The accuracy of the estimated loss of lifetime function using various extrapolation approaches was assessed in a simulation study and by data from the Danish Cancer Registry where complete follow-up was available. In addition, we illustrated the proposed methodology by analyzing recent data from the Danish Lymphoma Registry. RESULTS: No uniformly superior extrapolation method was found, but flexible parametric mixture cure models and flexible parametric relative survival models seemed to be suitable in various scenarios. CONCLUSION: Using extrapolation to estimate the loss of lifetime function requires careful consideration of the relative survival function outside the available follow-up period. We propose extensive sensitivity analyses when estimating the loss of lifetime function.

Limited value of routine follow-up visits in chronic lymphocytic leukemia managed initially by watch and wait: A North Denmark population-based study.

INTRODUCTION: The majority of newly diagnosed chronic lymphocytic leukemia (CLL) patients are followed initially by watch and wait (WAW). Clinical practice varies and the value of frequent follow-up visits remains unclear. Thus, in this study we investigated the clinical value of follow-up visits for patients with CLL. METHODS: We collected data from diagnosis and follow-up visits for patients diagnosed with CLL and managed by WAW in the North Denmark Region between 2007-2014. High- and low-risk group patients were determined by Binet stage, IgVH status, and cytogenetics at diagnosis. The effect of risk group allocation on the probability of receiving CLL-directed treatment within two years was included in a multivariable logistic regression model adjusted for age and blood test results. RESULTS: 273 patients were included in the study with a median follow-up of 3 years (IQR: 1.6-5.4). Overall, the median interval between follow-up visits was 98 days (95% CI: 96-100) (high-risk patients: 91 days [95% CI: 86-95] vs. low-risk patients: 105 days [95% CI: 100-110]). Among 2,312 follow-up visits, only 387 (17%) were associated with interventions. At the following time points: 6 months, 1 year, and 1.5 years, patients with low-risk CLL had significantly lower odds of initiating treatment compared to patients with high-risk CLL. CONCLUSION: WAW plays an important role in managing CLL. Interventions at follow-up visits were infrequent and low-risk patients had significantly lower risk of treatment initiation. We question the value of routine follow-up in CLL in the absence of changes in symptoms and/or blood test results.

Simplicity at the cost of predictive accuracy in diffuse large B-cell lymphoma: a critical assessment of the R-IPI, IPI, and NCCN-IPI.

The international prognostic index (IPI) and similar models form the cornerstone of clinical assessment in newly diagnosed diffuse large B-cell lymphoma (DLBCL). While being simple and convenient to use, their inadequate use of the available clinical data is a major weakness. In this study, we compared performance of the International Prognostic Index (IPI) and its variations (R-IPI and NCCN-IPI) to a Cox proportional hazards (CPH) model using the same covariates in nondichotomized form. All models were tested in 4863 newly diagnosed DLBCL patients from population-based Nordic registers. The CPH model led to a substantial increase in predictive accuracy as compared to conventional prognostic scores when evaluated by the area under the curve and other relevant tests. Furthermore, the generation of patient-specific survival curves rather than assigning patients to one of few predefined risk groups is a relevant step toward personalized management and treatment. A test-version is available on lymphomapredictor.org.