Dynamic prediction: A challenge for biostatisticians, but greatly needed by patients, physicians and the public.

Prognosis is usually expressed in terms of the probability that a patient will or will not have experienced an event of interest t years after diagnosis of a disease. This quantity, however, is of little informative value for a patient who is still event-free after a number of years. Such a patient would be much more interested in the conditional probability of being event-free in the upcoming t years, given that he/she did not experience the event in the s years after diagnosis, called "conditional survival." It is the simplest form of a dynamic prediction and can be dealt with using straightforward extensions of standard time-to-event analyses in clinical cohort studies. For a healthy individual, a related problem with further complications is the so-called "age-conditional probability of developing cancer" in the next t years. Here, the competing risk of dying from other diseases has to be taken into account. For both situations, the hazard function provides the central dynamic concept, which can be further extended in a natural way to build dynamic prediction models that incorporate both baseline and time-dependent characteristics. Such models are able to exploit the most current information accumulating over time in order to accurately predict the further course or development of a disease. In this article, the biostatistical challenges as well as the relevance and importance of dynamic prediction are illustrated using studies of multiple myeloma, a hematologic malignancy with a formerly rather poor prognosis which has improved over the last few years.

Prevention of hospital infections by intervention and training (PROHIBIT): results of a pan-European cluster-randomized multicentre study to reduce central venous catheter-related bloodstream infections.

PURPOSE: To test the effectiveness of a central venous catheter (CVC) insertion strategy and a hand hygiene (HH) improvement strategy to prevent central venous catheter-related bloodstream infections (CRBSI) in European intensive care units (ICUs), measuring both process and outcome indicators. METHODS: Adult ICUs from 14 hospitals in 11 European countries participated in this stepped-wedge cluster randomised controlled multicentre intervention study. After a 6 month baseline, three hospitals were randomised to one of three interventions every quarter: (1) CVC insertion strategy (CVCi); (2) HH promotion strategy (HHi); and (3) both interventions combined (COMBi). Primary outcome was prospective CRBSI incidence density. Secondary outcomes were a CVC insertion score and HH compliance. RESULTS: Overall 25,348 patients with 35,831 CVCs were included. CRBSI incidence density decreased from 2.4/1000 CVC-days at baseline to 0.9/1000 (p < 0.0001). When adjusted for patient and CVC characteristics all three interventions significantly reduced CRBSI incidence density. When additionally adjusted for the baseline decreasing trend, the HHi and COMBi arms were still effective. CVC insertion scores and HH compliance increased significantly with all three interventions. CONCLUSIONS: This study demonstrates that multimodal prevention strategies aiming at improving CVC insertion practice and HH reduce CRBSI in diverse European ICUs. Compliance explained CRBSI reduction and future quality improvement studies should encourage measuring process indicators.

A concise revised Myeloma Comorbidity Index as a valid prognostic instrument in a large cohort of 801 multiple myeloma patients.

With growing numbers of elderly multiple myeloma patients, reliable tools to assess their vulnerability are required. The objective of the analysis herein was to develop and validate an easy to use myeloma risk score (revised Myeloma Comorbidity Index) that allows for risk prediction of overall survival and progression-free survival differences in a large patient cohort. We conducted a comprehensive comorbidity, frailty and disability evaluation in 801 consecutive myeloma patients, including comorbidity risks obtained at diagnosis. The cohort was examined within a training and validation set. Multivariate analysis determined renal, lung and Karnofsky Performance Status impairment, frailty and age as significant risks for overall survival. These were combined in a weighted revised Myeloma Comorbidity Index, allowing for the identification of fit (revised Myeloma Comorbidity Index ≤3 [n=247, 30.8%]), intermediate-fit (revised Myeloma Comorbidity Index 4-6 [n=446, 55.7%]) and frail patients (revised Myeloma Comorbidity Index >6 [n=108, 13.5%]): these subgroups, confirmed via validation analysis, showed median overall survival rates of 10.1, 4.4 and 1.2 years, respectively. The revised Myeloma Comorbidity Index was compared to other commonly used comorbidity indices (Charlson Comorbidity Index, Hematopoietic Cell Transplantation-Specific Comorbidity Index, Kaplan-Feinstein Index): if each were divided in risk groups based on 25% and 75% quartiles, highest hazard ratios, best prediction and Brier scores were achieved with the revised Myeloma Comorbidity Index. The advantages of the revised Myeloma Comorbidity Index include its accurate assessment of patients' physical conditions and simple clinical applicability. We propose the revised Myeloma Comorbidity Index to be tested with the "reference" International Myeloma Working Group frailty score in multicenter analyses and future clinical trials. The study was registered at the German Clinical Trials Register (DRKS-00003868).

Integrating multiple molecular sources into a clinical risk prediction signature by extracting complementary information.

BACKGROUND: High-throughput technology allows for genome-wide measurements at different molecular levels for the same patient, e.g. single nucleotide polymorphisms (SNPs) and gene expression. Correspondingly, it might be beneficial to also integrate complementary information from different molecular levels when building multivariable risk prediction models for a clinical endpoint, such as treatment response or survival. Unfortunately, such a high-dimensional modeling task will often be complicated by a limited overlap of molecular measurements at different levels between patients, i.e. measurements from all molecular levels are available only for a smaller proportion of patients. RESULTS: We propose a sequential strategy for building clinical risk prediction models that integrate genome-wide measurements from two molecular levels in a complementary way. To deal with partial overlap, we develop an imputation approach that allows us to use all available data. This approach is investigated in two acute myeloid leukemia applications combining gene expression with either SNP or DNA methylation data. After obtaining a sparse risk prediction signature e.g. from SNP data, an automatically selected set of prognostic SNPs, by componentwise likelihood-based boosting, imputation is performed for the corresponding linear predictor by a linking model that incorporates e.g. gene expression measurements. The imputed linear predictor is then used for adjustment when building a prognostic signature from the gene expression data. For evaluation, we consider stability, as quantified by inclusion frequencies across resampling data sets. Despite an extremely small overlap in the application example with gene expression and SNPs, several genes are seen to be more stably identified when taking the (imputed) linear predictor from the SNP data into account. In the application with gene expression and DNA methylation, prediction performance with respect to survival also indicates that the proposed approach might work well. CONCLUSIONS: We consider imputation of linear predictor values to be a feasible and sensible approach for dealing with partial overlap in complementary integrative analysis of molecular measurements at different levels. More generally, these results indicate that a complementary strategy for integrating different molecular levels can result in more stable risk prediction signatures, potentially providing a more reliable insight into the underlying biology.

Geriatric assessment in multiple myeloma patients: validation of the International Myeloma Working Group (IMWG) score and comparison with other common comorbidity scores.

This first validation of the International Myeloma Working Group geriatric assessment in 125 newly diagnosed multiple myeloma patients was performed using the International Myeloma Working Group score based on age, the Charlson Comorbidity Index and cognitive and physical conditions (Activities of Daily Living / Instrumental Activities of Daily Living) to classify patients as fit, intermediate-fit or frail. We verified the International Myeloma Working Group score's impact on outcome, and whether additional tools complement it. Since our prior analyses determined renal, lung and Karnofsky performance impairment as multivariate risks, and the inclusion of frailty, age and cytogenetics complements this, we included the revised myeloma comorbidity index, the Charlson Comorbidity Index, the Hematopoietic Cell Transplantation-Comorbidity Index and the Kaplan-Feinstein Index in this assessment. Multivariate analysis confirmed cytogenetics, Activities of Daily Living, Instrumental Activities of Daily Living and the Charlson Comorbidity Index as risks: 3-year overall survival for fit, intermediate-fit and frail patients was 91%, 77% and 47%, respectively. Using the Charlson Comorbidity Index, the Hematopoietic Cell Transplantation-Comorbidity Index, the Kaplan-Feinstein Index and the revised Myeloma Comorbidity Index allowed us to define fit and frail patients with distinct progression-free and overall survival rates, with the most pronounced differences evidenced via the International Myeloma Working Group score, the Charlson Comorbidity Index and the revised Myeloma Comorbidity Index. Since the Charlson Comorbidity Index is included in the International Myeloma Working Group score, we propose the latter and the revised Myeloma Comorbidity Index for future frailty measurements. Both are useful instruments for identifying myeloma patients with a geriatric risk profile and have a strong prognostic value for functional decline and overall survival. The study was registered as: (clinicaltrials.gov Identifier: 00003686).

Identifying Prognostic SNPs in Clinical Cohorts: Complementing Univariate Analyses by Resampling and Multivariable Modeling.

Clinical cohorts with time-to-event endpoints are increasingly characterized by measurements of a number of single nucleotide polymorphisms that is by a magnitude larger than the number of measurements typically considered at the gene level. At the same time, the size of clinical cohorts often is still limited, calling for novel analysis strategies for identifying potentially prognostic SNPs that can help to better characterize disease processes. We propose such a strategy, drawing on univariate testing ideas from epidemiological case-controls studies on the one hand, and multivariable regression techniques as developed for gene expression data on the other hand. In particular, we focus on stable selection of a small set of SNPs and corresponding genes for subsequent validation. For univariate analysis, a permutation-based approach is proposed to test at the gene level. We use regularized multivariable regression models for considering all SNPs simultaneously and selecting a small set of potentially important prognostic SNPs. Stability is judged according to resampling inclusion frequencies for both the univariate and the multivariable approach. The overall strategy is illustrated with data from a cohort of acute myeloid leukemia patients and explored in a simulation study. The multivariable approach is seen to automatically focus on a smaller set of SNPs compared to the univariate approach, roughly in line with blocks of correlated SNPs. This more targeted extraction of SNPs results in more stable selection at the SNP as well as at the gene level. Thus, the multivariable regression approach with resampling provides a perspective in the proposed analysis strategy for SNP data in clinical cohorts highlighting what can be added by regularized regression techniques compared to univariate analyses.

Conditional Survival: A Useful Concept to Provide Information on How Prognosis Evolves over Time.

Conditional survival (CS) is defined as the probability of surviving further t years, given that a patient has already survived s years after the diagnosis of a chronic disease. It is the simplest form of a dynamic prediction in which other events in the course of the disease or biomarker values measured up to time s can be incorporated. CS has attracted attention in recent years either in an absolute or relative form where the latter is based on a comparison with an age-adjusted normal population being highly relevant from a public health perspective. In its absolute form, CS constitutes the quantity of major interest in a clinical context. Given a clinical cohort of patients with a particular type of cancer, absolute CS can be estimated by conditional Kaplan-Meier estimates in strata defined, for example, by age and disease stage or by a conditional version of the Cox and other regression models for time-to-event data. CS can be displayed as a function of the prediction time s in parametric as well as nonparametric fashion. We illustrate the use of absolute CS in a large clinical cohort of patients with multiple myeloma. For investigating CS, it is necessary to ensure almost complete long-term follow-up of the patients enrolled in the clinical cohort and to consider potential age-stage migration as well as changing treatment modalities over time. CS provides valuable and relevant information on how prognosis develops over time. It also serves as a starting point for identifying factors related to long-term survival.

Lowering the alemtuzumab dose in reduced intensity conditioning allogeneic hematopoietic cell transplantation is associated with a favorable early intense natural killer cell recovery.

BACKGROUND AIMS: The anti-CD52 monoclonal antibody alemtuzumab is employed in allogeneic hematopoietic cell transplantation (alloHCT) for the prevention of graft-versus-host disease (GVHD). However, its optimal dosing in this setting has not been determined yet. We compared three different alemtuzumab dose levels in reduced intensity conditioning (RIC) alloHCT with respect to lymphocyte recovery and outcome. METHODS: In 127 consecutive patients with predominantly advanced stage hematologic malignancies, a first alloHCT after RIC was performed, applying a fludarabine-based protocol (in 93% FBM: fludarabine, bis-chloroethyl-nitrosourea [BCNU], and melphalan). For GVHD prophylaxis, cyclosporine and alemtuzumab at three different dose levels (40 mg, 20 mg, 10 mg) were administered. Recovery of the peripheral blood (PB) lymphocyte sub-populations and clinical outcome were determined with regard to the alemtuzumab dose. RESULTS: Natural killer (NK) cell concentrations in PB around day +30 correlated inversely with the alemtuzumab dose, whereas other PB lymphocyte subtypes remained essentially unaffected by dosing of alemtuzumab. Lower alemtuzumab doses were associated with a tendency toward improved overall survival mainly during the early post-transplantation months. With regard to the PB NK cell concentration around day +30, "early intense NK cell reconstituters" tended to show an overall survival benefit. CONCLUSIONS: An alemtuzumab dose reduction to only 10-20 mg provides sufficient GVHD prophylaxis and supports improved NK cell regeneration early after alloHCT in PB ("NK cell saving effect"), which may have a positive effect on overall survival.

Treatment option of bendamustine in combination with rituximab in elderly and frail patients with aggressive B-non-Hodgkin lymphoma: rational, efficacy, and tolerance.

We analyzed the safety and efficacy of rituximab plus bendamustine (R-B) in elderly and frail patients with aggressive B-non-Hodgkin lymphoma (a-B-NHL). Few reports have as yet reported on R-B in a-B-NHL, albeit its value for indolent lymphoma vs. R-CHOP has impressively been shown. We assessed 20 consecutive patients with a-B-NHL receiving R-B as first-line or relapse treatment after (R)-CHOP in our department. Besides patient- and lymphoma-specific characteristics, comorbidity indices were determined. The median patient age was 72 years (51-86), the median Karnofsky performance status was 55 % (40-90 %), and according to the international prognostic index, 15 had high-intermediate or high-risk disease. The comorbidity indices revealed a median Kaplan-Feinstein index of 3 (range 1-3), Charlson comorbidity index of 4 (range 0-9), hematopoietic cell transplantation-specific comorbidity index of 3 (range 0-11), and Freiburg comorbidity index of 2 (range 0-2). Moreover, eight patients had echocardiographic and laboratory signs of cardiac insufficiency, all leading to R-B rather than R-CHOP treatment. The overall response rate was 55 %, with complete response and partial response rates of 20 and 35 %, respectively. In our frail and elderly patient cohort, R-B therapy was well-tolerated. Median progression free survival and overall survival were 8.3 months (95 % confidence interval [CI], 2.8--not reached [n.r.]) and 19.4 months (95 % CI, 4.6--n.r.), respectively. We conclude that R-B is a feasible and safe therapy option in a-B-NHL patients not qualifying for R-CHOP but needs to be further assessed in larger subsequent trials, these currently being under way.

Use of systemic antifungals in daily clinical practice in the haematology and oncology setting: results of a prospective observational analysis.

PURPOSE: To assess the role of systemic antifungal drugs as well as the frequency of potential drug interactions and adverse drug events of commonly used antifungals in an unselected haematology/oncology patient cohort. METHODS: A prospective analysis was performed in our haematology/oncology department between October 2006 and September 2009. Data were obtained from 250 consecutive patients who received treatment and/or prophylaxis with fluconazole (n = 191), liposomal amphotericin B (n = 105), voriconazole (n = 62), caspofungin (n = 27) and/or posaconazole (n = 22). We performed detailed reviews of patient charts and laboratory values in close cooperation with treating physicians and nursing staff and participated regularly in ward and chart rounds. Potential drug interactions were assessed using the electronic database Micromedex® 1.0 (Healthcare Series). RESULTS: In terms of adverse drug events, caspofungin (56%) and voriconazole (58%) revealed a slightly more favourable safety profile than liposomal amphotericin B (66%) and posaconazole (64%). We confirmed frequent nephrotoxic effects with the use of liposomal amphotericin B (20%). Regarding potential drug interactions, 97 (66%) of 147 evaluated patients were exposed to at least 1 of 22 different potentially interacting drug combinations involving systemic antifungal agents. Cyclosporine was the most prevalent potentially interacting drug in our cohort. CONCLUSIONS: Systemic antifungal drugs are widely used in the haematology/oncology setting and exhibit numerous potential drug interactions and adverse events in cancer patients. Our results highlight the challenges related to antifungal drugs and should valuably contribute to a safe and efficient application of this increasingly important class of drugs.

Nonparametric inference for the cumulative incidence function of a competing risk, with an emphasis on confidence bands in the presence of left-truncation.

Motivated by a study on pregnancy outcome, a computationally simple resampling procedure for nonparametric analysis of the cumulative incidence function of a competing risk is investigated for left-truncated data. We also modify the original procedure to producing the more desirable Greenwood-type variance estimates. These approaches are used to construct simultaneous confidence bands of the cumulative incidence functions which is otherwise hampered by the complicated nature of the covariance process. Simulation results and a real data example are provided.

Therapeutic drug monitoring of posaconazole in hematology patients: experience with a new high-performance liquid chromatography-based method.

Parallel administration of the proton pump inhibitor (PPI) esomeprazole has been shown to decrease oral bioavailability of posaconazole in healthy volunteers. We prospectively analyzed serum samples (n = 59) obtained from hematology patients (n = 27) under posaconazole prophylaxis. Patients treated concomitantly with pantoprazole had significantly lower posaconazole levels than patients without PPI treatment (median levels of 630 microg/liter versus 1,125 microg/liter, respectively). These results suggest that drug monitoring is relevant when posaconazole and pantoprazole are administered concomitantly.