Understanding social dynamics and patient experience in out of hospital care: validation of a co-responsibility questionnaire (CoReCare).

Patient experiences are commonly assessed through patient reported experience measures (PREMs). Ambulatory care models extend traditional care into the patients' home, meaning that a triangle of health care professionals, patients, and their families need to be considered when assessing the remote care experience. These intertwined responsibilities are described by co-responsibility. Currently, PREMs don't reflect how elements to remote care impact this remote care experience. Therefore, this study aimed to develop a questionnaire assessing perceived patient-partner co-responsibility as a PREM in remote care. A 30-item questionnaire was assessed among 1000 individuals aged between 18 and 65 years that tried to lose weight with a partner, friend or family member supporting them. Pairwise item correlations, Exploratory Factor Analysis, and Cronbach's alpha were used for validation. 29-items were identified to reflect co-responsibility across 6 factors: empowerment and support, relational aspects, lack of sympathy, co-participation, accepting help and awareness. Cronbach's alpha ranged between 0.66 and 0.93, showing good internal consistency. We present a validated CoReCare Questionnaire to understand the impact of social dynamics on achieving desired health outcomes in a remote care setting. The CoReCare Questionnaire extends current PREMs when aiming to assess and improve the patient experience of a care episode outside of the hospital.

Disease progression in osteosarcoma: a multistate model for the EURAMOS-1 (European and American Osteosarcoma Study) randomised clinical trial.

OBJECTIVES: Investigating the effect of prognostic factors in a multistate framework on survival in a large population of patients with osteosarcoma. Of interest is how prognostic factors affect different disease stages after surgery, with stages of local recurrence (LR), new metastatic disease (NM), LR+NM, secondary malignancy, a second NM, and death. DESIGN: An open-label, international, phase 3 randomised controlled trial. SETTING: 325 sites in 17 countries. PARTICIPANTS: The subset of 1631 metastases-free patients from 1965 patients with high-grade resectable osteosarcoma, from the European and American Osteosarcoma Study. MAIN OUTCOME MEASURES: The effect of prognostic factors on different disease stages, expressed as HRs; predictions of disease progression on an individual patient basis, according to patient-specific characteristics and history of intermediate events. RESULTS: Of 1631 patients, 526 experienced an intermediate event, and 305 died by the end of follow-up. An axial tumour site substantially increased the risk of LR after surgery (HR=10.84, 95% CI 8.46 to 13.86) and death after LR (HR=11.54, 95% CI 6.11 to 21.8). A poor histological increased the risk of NM (HR=5.81, 95% CI 5.31 to 6.36), which sharply declined after 3 years since surgery. Young patients (<12 years) had a lower intermediate event risk (eg, for LR: HR=0.66, 95% CI 0.51 to 0.86), when compared with adolescents (12-18 years), but had an increased risk of subsequent death, while patients aged >18 had a decreased risk of death after event (eg, for death after LR: HR=2.40, 95% CI 1.52 to 3.90; HR=0.35, 95% CI 0.21 to 0.56, respectively). CONCLUSIONS: Our findings suggest that patients with axial tumours should be monitored for LR and patients with poor histological response for NM, and that for young patients (<12) with an LR additional treatment options should be investigated. TRIAL REGISTRATION NUMBER: NCT00134030.

Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques.

BACKGROUND: Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians. METHODS: In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival from transplantation. Of particular interest is also the identification of potential risk factors. A comparison is performed between 3 different Cox models (with all variables, backward selection and LASSO) and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks (PLANNs). For PLANNs, novel extensions to their original specification are tested. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques. RESULTS: Well-established predictive measures are employed from the survival field (C-index, Brier score and Integrated Brier Score) and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death. The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based on the Integrated Brier Score at 10 years. CONCLUSION: In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables. TRIAL REGISTRATION: Retrospective data were provided by the Scientific Registry of Transplant Recipients under Data Use Agreement number 9477 for analysis of risk factors after liver transplantation.

Method to measure the mismatch between target and achieved received dose intensity of chemotherapy in cancer trials: a retrospective analysis of the MRC BO06 trial in osteosarcoma.

OBJECTIVES: In cancer studies, the target received dose intensity (tRDI) for any regimen, the intended dose and time for the regimen, is commonly taken as a proxy for achieved RDI (aRDI), the actual individual dose and time for the regimen. Evaluating tRDI/aRDI mismatches is crucial to assess study results whenever patients are stratified on allocated regimen. The manuscript develops a novel methodology to highlight and evaluate tRDI/aRDI mismatches. DESIGN: Retrospective analysis of a randomised controlled trial, MRC BO06 (EORTC 80931). SETTING: Population-based study but proposed methodology can be applied to other trial designs. PARTICIPANTS: A total of 497 patients with resectable high-grade osteosarcoma, of which 19 were excluded because chemotherapy was not started or the estimated dose was abnormally high (>1.25 × prescribed dose). INTERVENTIONS: Two regimens with the same anticipated cumulative dose (doxorubicin 6×75 mg/m2/week; cisplatin 6×100 mg/m2/week) over different time schedules: every 3 weeks in regimen-C and every 2 weeks in regimen-DI. PRIMARY AND SECONDARY OUTCOME MEASURES: tRDI distribution was measured across groups of patients derived from k-means clustering of treatment data. K-means creates groups of patients who are aRDI-homogeneous. The main outcome is the proportion of tRDI values in groups of homogeneous aRDI. RESULTS: For nearly half of the patients, there is a mismatch between tRDI and aRDI; for 21%, aRDI was closer to the tRDI of the other regimen. CONCLUSIONS: For MRC BO06, tRDI did not predict well aRDI. The manuscript offers an original procedure to highlight the presence of and quantify tRDI/aRDI mismatches. Caution is required to interpret the effect of chemotherapy-regimen intensification on survival outcome at an individual level where such a mismatch is present.The study relevance lies in the use of individual realisation of the intended treatment, which depends on individual delays and/or dose reductions reported throughout the treatment. TRIAL REGISTRATION NUMBER: ISRCTN86294690.

A novel method to address the association between received dose intensity and survival outcome: benefits of approaching treatment intensification at a more individualised level in a trial of the European Osteosarcoma Intergroup.

PURPOSE: There is lack of consensus on the prognostic value of received high dose intensity in osteosarcoma survivorship. Many studies have not shown a clear survival benefit when dose intensity is increased. The aim of this study is to go beyond chemotherapy intensification by arm-wide escalation of intended dose and/or compression of treatment schedule, while conversely addressing the relationship between treatment intensity and survival at the patient level. The study focusses on the difference in outcome results, based on a novel, progressively more individualised approach to dose intensity. METHODS: A retrospective analysis of data from MRC BO06/EORTC 80931 randomised controlled trial for treatment of osteosarcoma was conducted. Three types of post hoc patient groups are formed using the intended regimen: the individually achieved cumulative dose and time on treatment, and the increase of individual cumulative dose over time. Event-free survival is investigated and compared in these three stratifications. RESULTS: The strata of intended regimen and achieved treatment yields equivalent results. Received cumulative dose over time produces groups with evident different survivorship characteristics. In particular, it highlights a group of patients with an estimated 3-year event-free survival much larger (more than 10%) than other patient groups. This group mostly contains patients randomised to an intensified regimen. In addition, adverse events reported by that group show the presence of increased preoperative myelotoxicity. CONCLUSIONS: The manuscript shows the benefits of analyzing studies by using longitudinal data, e.g. recorded per cycle. This has impact on the drafting of future trials by showing why such a level of detail is needed for both treatment and adverse event data. The novel method proposed, based on cumulative dose received over time, shows that longitudinal treatment data might be used to link survival outcome with drug metabolism. This is particularly valuable when pharmacogenetics data for metabolism of cytotoxic agents are not collected. TRIAL REGISTRATION: ISRCTN86294690.

Marginal structural models with dose-delay joint-exposure for assessing variations to chemotherapy intensity.

Marginal structural models are causal models designed to adjust for time-dependent confounders in observational studies with dynamically adjusted treatments. They are robust tools to assess causality in complex longitudinal data. In this paper, a marginal structural model is proposed with an innovative dose-delay joint-exposure model for Inverse-Probability-of-Treatment Weighted estimation of the causal effect of alterations to the therapy intensity. The model is motivated by a precise clinical question concerning the possibility of reducing dosages in a regimen. It is applied to data from a randomised trial of chemotherapy in osteosarcoma, an aggressive primary bone-tumour. Chemotherapy data are complex because their longitudinal nature encompasses many clinical details like composition and organisation of multi-drug regimens, or dynamical therapy adjustments. This manuscript focuses on the clinical dynamical process of adjusting the therapy according to the patient's toxicity history, and the causal effect on the outcome of interest of such therapy modifications. Depending on patients' toxicity levels, variations to therapy intensity may be achieved by physicians through the allocation of either a reduction or a delay of the next planned dose. Thus, a negative feedback is present between exposure to cytotoxic agents and toxicity levels, which acts as time-dependent confounders. The construction of the model is illustrated highlighting the high complexity and entanglement of chemotherapy data. Built to address dosage reductions, the model also shows that delays in therapy administration should be avoided. The last aspect makes sense from the cytological point of view, but it is seldom addressed in the literature.

The ENCCA-WP7/EuroSarc/EEC/PROVABES/EURAMOS 3rd European Bone Sarcoma Networking Meeting/Joint Workshop of EU Bone Sarcoma Translational Research Networks; Vienna, Austria, September 24-25, 2015. Workshop Report.

This report summarizes the results of the 3rd Joint ENCCA-WP7, EuroSarc, EEC, PROVABES, and EURAMOS European Bone Sarcoma Network Meeting, which was held at the Children's Cancer Research Institute in Vienna, Austria on September 24-25, 2015. The joint bone sarcoma network meetings bring together European bone sarcoma researchers to present and discuss current knowledge on bone sarcoma biology, genetics, immunology, as well as results from preclinical investigations and clinical trials, to generate novel hypotheses for collaborative biological and clinical investigations. The ultimate goal is to further improve therapy and outcome in patients with bone sarcomas.