The ‘Survivorship Passport’ for childhood cancer survivors

Abstract Background: Currently, there are between 300,000 and 500,000 childhood cancer survivors (CCSs) in Europe. A significant proportion is at high risk, and at least 60% of them develop adverse health-related outcomes that can appear several years after treatment completion. Many survivors are unaware of their personal risk, and there seems to be a general lack of information among healthcare providers about pathophysiology and natural history of treatment-related complications. This can generate incorrect or delayed diagnosis and treatments

The 'Survivorship Passport' for childhood cancer survivors

Background: Currently, there are between 300,000 and 500,000 childhood cancer survivors (CCSs) in Europe. A significant proportion is at high risk, and at least 60% of them develop adverse health-related outcomes that can appear several years after treatment completion. Many survivors are unaware of their personal risk, and there seems to be a general lack of information among healthcare providers about pathophysiology and natural history of treatment-related complications. This can generate incorrect or delayed diagnosis and treatments. Method: The Survivorship Passport (SurPass) consists of electronic documents, which summarise the clinical history of the childhood or adolescent cancer survivor. It was developed by paediatric oncologists of the PanCare and SIOPE networks and IT experts of Cineca, together with parents, patients, and survivors' organisations within the European Union–funded European Network for Cancer research in Children and Adolescents. It consists of a template of a web-based, simply written document, translatable in all European languages, to be given to each CCS. The SurPass provides a summary of each survivor's clinical history, with detailed information about the original cancer and of treatments received, together with personalised follow-up and screening recommendations based on guidelines published by the International Guidelines Harmonization Group and PanCareSurFup. Results: The SurPass data schema contains a maximum of 168 variables and uses internationally approved nomenclature, except for radiotherapy fields, where a new classification was defined by radiotherapy experts. The survivor-specific screening recommendations are mainly based on treatment received and are automatically suggested, thanks to built-in algorithms. These may be adapted and further individualised by the treating physician in case of special disease and survivor circumstances. The SurPass was tested at the Istituto Giannina Gaslini, Italy, and received positive feedback. It is now being integrated at the institutional, regional and national level. Conclusions: The SurPass is potentially an essential tool for improved and more harmonised follow-up of CCS. It also has the potential to be a useful tool for empowering CCSs to be responsible for their own well-being and preventing adverse events whenever possible. With sufficient commitment on the European level, this solution should increase the capacity to respond more effectively to the needs of European CCS

Optimal Orthogonal Drawings of Planar 3-Graphs in Linear Time

This paper addresses a long standing, widely studied, open question: Given a planar 3-graph G (i.e., a planar graph with vertex degree at most three), what is the best computational upper bound to compute a bend-minimum planar orthogonal drawing of G in the variable embedding setting? In this setting the algorithm can choose among the exponentially many planar embeddings of G the one that leads to an orthogonal drawing with the minimum number of bends. We answer the question by describing a linear-time algorithm that computes a bend-minimum planar orthogonal drawing of G. Also, if G is not K4, the drawing has at most one bend per edge. The existence of an orthogonal drawing Г of a planar 3-graph such that Г has the minimum number of bends and at most one bend per edge was previously unknown

Rectilinear-Upward Planarity Testing of Digraphs

A rectilinear-upward planar drawing of a digraph G is a crossing-free drawing of G where each edge is either a horizontal or a vertical segment, and such that no directed edge points downward. Rectilinear-Upward Planarity Testing is the problem of deciding whether a digraph G admits a rectilinear-upward planar drawing. We show that: (i) Rectilinear-Upward Planarity Testing is NP-complete, even if G is biconnected; (ii) it can be solved in linear time when an upward planar embedding of G is fixed; (iii) the problem is polynomial-time solvable for biconnected digraphs of treewidth at most two, i.e., for digraphs whose underlying undirected graph is a series-parallel graph; (iv) for any biconnected digraph the problem is fixed-parameter tractable when parameterized by the number of sources and sinks in the digraph

Clinical phenotypes and quality of life to define post-COVID-19 syndrome: a cluster analysis of the multinational, prospective ORCHESTRA cohortResearch in context

Summary: Background: Lack of specific definitions of clinical characteristics, disease severity, and risk and preventive factors of post-COVID-19 syndrome (PCS) severely impacts research and discovery of new preventive and therapeutics drugs. Methods: This prospective multicenter cohort study was conducted from February 2020 to June 2022 in 5 countries, enrolling SARS-CoV-2 out- and in-patients followed at 3-, 6-, and 12-month from diagnosis, with assessment of clinical and biochemical features, antibody (Ab) response, Variant of Concern (VoC), and physical and mental quality of life (QoL). Outcome of interest was identification of risk and protective factors of PCS by clinical phenotype, setting, severity of disease, treatment, and vaccination status. We used SF-36 questionnaire to assess evolution in QoL index during follow-up and unsupervised machine learning algorithms (principal component analysis, PCA) to explore symptom clusters. Severity of PCS was defined by clinical phenotype and QoL. We also used generalized linear models to analyse the impact of PCS on QoL and associated risk and preventive factors. CT registration number: NCT05097677. Findings: Among 1796 patients enrolled, 1030 (57%) suffered from at least one symptom at 12-month. PCA identified 4 clinical phenotypes: chronic fatigue-like syndrome (CFs: fatigue, headache and memory loss, 757 patients, 42%), respiratory syndrome (REs: cough and dyspnoea, 502, 23%); chronic pain syndrome (CPs: arthralgia and myalgia, 399, 22%); and neurosensorial syndrome (NSs: alteration in taste and smell, 197, 11%). Determinants of clinical phenotypes were different (all comparisons p < 0.05): being female increased risk of CPs, NSs, and CFs; chronic pulmonary diseases of REs; neurological symptoms at SARS-CoV-2 diagnosis of REs, NSs, and CFs; oxygen therapy of CFs and REs; and gastrointestinal symptoms at SARS-CoV-2 diagnosis of CFs. Early treatment of SARS-CoV-2 infection with monoclonal Ab (all clinical phenotypes), corticosteroids therapy for mild/severe cases (NSs), and SARS-CoV-2 vaccination (CPs) were less likely to be associated to PCS (all comparisons p < 0.05). Highest reduction in QoL was detected in REs and CPs (43.57 and 43.86 vs 57.32 in PCS-negative controls, p < 0.001). Female sex (p < 0.001), gastrointestinal symptoms (p = 0.034) and renal complications (p = 0.002) during the acute infection were likely to increase risk of severe PCS (QoL <50). Vaccination and early treatment with monoclonal Ab reduced the risk of severe PCS (p = 0.01 and p = 0.03, respectively). Interpretation: Our study provides new evidence suggesting that PCS can be classified by clinical phenotypes with different impact on QoL, underlying possible different pathogenic mechanisms. We identified factors associated to each clinical phenotype and to severe PCS. These results might help in designing pathogenesis studies and in selecting high-risk patients for inclusion in therapeutic and management clinical trials. Funding: The study received funding from the Horizon 2020 ORCHESTRA project, grant 101016167; from the Netherlands Organisation for Health Research and Development (ZonMw), grant 10430012010023; from Inserm, REACTing (REsearch &amp; ACtion emergING infectious diseases) consortium and the French Ministry of Health, grant PHRC 20-0424