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Gait Problems in Cerebral Palsy focuses on the surgical care and postoperative rehabilitation of children with cerebral palsy as they grow through early and late childhood, and adolescence. The book describes the neurologic and musculoskeletal issues that are frequently treated in children in each age group. It describes how to evaluate the children, create problem lists, make treatment choices, carry out interventions, and provide rehabilitation therapies. It: Emphasizes the critical importance of dialogue with children and parents, and age-related data derived from the GOAL questionnaire is provided to focus insight on the priorities of patients and families. Presents descriptions of patient and family priorities, decision-making and surgical techniques, and rehabilitation therapies for each age group. Offers consistent chapter structures that emphasize clarity and brevity, supplemented by figures and tables. Highlights the importance of building collaborative multidisciplinary clinical teams. Includes access to numerous videos which demonstrate clinical teams at work with patients, surgical techniques, and rehabilitation activities.   Essential reading for orthopaedic surgeons, physiatrists, physical therapists, kinesiologists, gait analysis experts, and other members of the interdisciplinary team involved.  

Background Neoadjuvant chemoradiotherapy (nCRT) plus surgery is a standard treatment for locally advanced oesophageal cancer. With this treatment, 29% of patients have a pathologically complete response in the resection specimen. This provides the rationale for investigating an active surveillance approach. The aim of this study is to assess the (cost-)effectiveness of active surveillance vs. standard oesophagectomy after nCRT for oesophageal cancer. Methods This is a phase-III multi-centre, stepped-wedge cluster randomised controlled trial. A total of 300 patients with clinically complete response (cCR, i.e. no local or disseminated disease proven by histology) after nCRT will be randomised to show non-inferiority of active surveillance to standard oesophagectomy (non-inferiority margin 15%, intra-correlation coefficient 0.02, power 80%, 2-sided α 0.05, 12% drop-out). Patients will undergo a first clinical response evaluation (CRE-I) 4–6 weeks after nCRT, consisting of endoscopy with bite-on-bite biopsies of the primary tumour site and other suspected lesions. Clinically complete responders will undergo a second CRE (CRE-II), 6–8 weeks after CRE-I. CRE-II will include 18F–FDG-PET-CT, followed by endoscopy with bite-on-bite biopsies and ultra-endosonography plus fine needle aspiration of suspected lymph nodes and/or PET- positive lesions. Patients with cCR at CRE-II will be assigned to oesophagectomy (first phase) or active surveillance (second phase of the study). The duration of the first phase is determined randomly over the 12 centres, i.e., stepped-wedge cluster design. Patients in the active surveillance arm will undergo diagnostic evaluations similar to CRE-II at 6/9/12/16/20/24/30/36/48 and 60 months after nCRT. In this arm, oesophagectomy will be offered only to patients in whom locoregional regrowth is highly suspected or proven, without distant dissemination. The main study parameter is overall survival; secondary endpoints include percentage of patients who do not undergo surgery, quality of life, clinical irresectability (cT4b) rate, radical resection rate, postoperative complications, progression-free survival, distant dissemination rate, and cost-effectiveness. We hypothesise that active surveillance leads to non-inferior survival, improved quality of life and a reduction in costs, compared to standard oesophagectomy. Discussion If active surveillance and surgery as needed after nCRT leads to non-inferior survival compared to standard oesophagectomy, this organ-sparing approach can be implemented as a standard of care.

Background In individually randomised trials we might expect interventions delivered in groups or by care providers to result in clustering of outcomes for participants treated in the same group or by the same care provider. In partially nested randomised controlled trials (pnRCTs) this clustering only occurs in one trial arm, commonly the intervention arm. It is important to measure and account for between-cluster variability in trial design and analysis. We compare analysis approaches for pnRCTs with continuous outcomes, investigating the impact on statistical inference of cluster sizes, coding of the non-clustered arm, intracluster correlation coefficient (ICCs), and differential variance between intervention and control arm, and provide recommendations for analysis. Methods We performed a simulation study assessing the performance of six analysis approaches for a two-arm pnRCT with a continuous outcome. These include: linear regression model; fully clustered mixed-effects model with singleton clusters in control arm; fully clustered mixed-effects model with one large cluster in control arm; fully clustered mixed-effects model with pseudo clusters in control arm; partially nested homoscedastic mixed effects model, and partially nested heteroscedastic mixed effects model. We varied the cluster size, number of clusters, ICC, and individual variance between the two trial arms. Results All models provided unbiased intervention effect estimates. In the partially nested mixed-effects models, methods for classifying the non-clustered control arm had negligible impact. Failure to account for even small ICCs resulted in inflated Type I error rates and over-coverage of confidence intervals. Fully clustered mixed effects models provided poor control of the Type I error rates and biased ICC estimates. The heteroscedastic partially nested mixed-effects model maintained relatively good control of Type I error rates, unbiased ICC estimation, and did not noticeably reduce power even with homoscedastic individual variances across arms. Conclusions In general, we recommend the use of a heteroscedastic partially nested mixed-effects model, which models the clustering in only one arm, for continuous outcomes similar to those generated under the scenarios of our simulations study. However, with few clusters (3–6), small cluster sizes (5–10), and small ICC (≤0.05) this model underestimates Type I error rates and there is no optimal model.

Background Whilst interest in efficient trial design has grown with the use of electronic health records (EHRs) to collect trial outcomes, practical challenges remain. Commonly raised concerns often revolve around data availability, data quality and issues with data validation. This study aimed to assess the agreement between data collected on clinical trial participants from different sources to provide empirical evidence on the utility of EHRs for follow-up in randomised controlled trials (RCTs). Methods This retrospective, participant-level data utility comparison study was undertaken using data collected as part of a UK primary care-based, randomised controlled trial (OPTiMISE). The primary outcome measure was the recording of all-cause hospitalisation or mortality within 3 years post-randomisation and was assessed across (1) Coded primary care data; (2) Coded-plus-free-text primary care data; and (3) Coded secondary care and mortality data. Agreement levels across data sources were assessed using Fleiss’ Kappa (K). Kappa statistics were interpreted using an established framework, categorising agreement strength as follows: <0 (poor), 0.00–0.20 (slight), 0.21–0.40 (fair), 0.41–0.60 (moderate), 0.61–0.80 (substantial), and 0.81–1.00 (almost perfect) agreement. The impact of using different data sources to determine trial outcomes was assessed by replicating the trial’s original analyses. Results Almost perfect agreement was observed for mortality outcome across the three data sources (K = 0.94, 95%CI 0.91–0.98). Fair agreement (weak consistency) was observed for hospitalisation outcomes, including all-cause hospitalisation or mortality (K = 0.35, 95%CI 0.28–0.42), emergency hospitalisation (K = 0.39, 95%CI 0.33–0.46), and hospitalisation or mortality due to cardiovascular disease (K = 0.32, 95%CI 0.19–0.45). The overall trial results remained consistent across data sources for the primary outcome, albeit with varying precision. Conclusion Significant discrepancies according to data sources were observed in recording of secondary care outcomes. Investigators should be cautious when choosing which data source(s) to use to measure outcomes in trials. Future work on linking participant-level data across healthcare settings should consider the variations in diagnostic coding practices. Standardised definitions for outcome measures when using healthcare systems data and using data from different data sources for cross-checking and verification should be encouraged.

To estimate the prevalence of nonadherence to treatment and its relationship with social support and social context in patients with multimorbidity and polypharmacy followed-up in primary care. This was an observational, descriptive, cross-sectional, multicenter study with an analytical approach. A total of 593 patients between 65-74 years of age with multimorbidity (≥3 diseases) and polypharmacy (≥5 drugs) during the last three months and agreed to participate in the MULTIPAP Study. The main variable was adherence (Morisky-Green). The predictors were social support (structural support and functional support (DUFSS)); sociodemographic variables; indicators of urban objective vulnerability; health-related quality of life (EQ-5D-5L-VAS & QALY); and clinical variables. Descriptive, bivariate and multivariate analyses with logistic regression models and robust estimators were performed. Four out of ten patients were nonadherent, 47% had not completed primary education, 28.7% had an income ≤1050 €/month, 35% reported four or more IUVs, and the average perceived health-related quality of life (HRQOL) EQ-5D-5L-VAS was 65.5. The items that measure functional support, with significantly different means between nonadherent and adherent patients were receiving love and affection (-0.23; 95%CI: -0.40;-0.06), help when ill (-0.25; 95%CI: -0.42;-0.08), useful advice (-0.20; 95%CI: -0.37;-0.02), social invitations (-0.22; 95%CI:-0.44;-0.01), and recognition (-0.29; 95%CI:-0.50;-0.08). Factors associated with nonadherence were belonging to the medium vs. low tertile of functional support (0.62; 95%CI: 0.42;0.94), reporting less than four IUVs (0.69; 95%CI: 0.46;1.02) and higher HRQOL perception (0.98; 95%CI: 0.98;0.99). Among patients 65-74 years of age with multimorbidity and polypharmacy, lower functional support was related to nonadherence to treatment. The nonadherence decreased in those patients with higher functional support, lower urban vulnerability and higher perceived health status according to the visual analog scale of health-related quality of life.

Our earlier multicenter randomized controlled trial showed that adjuvant immunotherapy with cytokine-induced killer (CIK) cells resulted in longer recurrence-free survival (RFS) and overall survival (OS) as well in patients who received curative treatment for hepatocellular carcinoma (HCC). In the present study, we determined if the efficacy of CIK cell therapy continued after end of repeated CIK cell injections. We performed a follow-up study of our preceding trial. We included 226 patients: 114 patients in the immunotherapy group (injection of 6.4 × 109 CIK cells, 16 times during 60 weeks) and 112 patients in the control group (no treatment) after potentially curative treatment for HCC. In total, 162 patients (89 of the immunotherapy group and 73 of controls) underwent an extended follow-up for 60 months after randomization of the last patient. The primary endpoint was RFS, and secondary endpoints included OS. During follow-up time of median 68.5 months (interquartile range 45.0–82.2 months), the immunotherapy group continued to show a significantly lower risk of recurrence or death [hazard ratio (HR) 0.67; 95% confidence interval (CI) 0.48–0.94; P = 0.009 by one-sided log-rank test]. At 5 years, RFS rate was 44.8% in the immunotherapy group and 33.1% in the control group. The risk of all-cause death was also lower in the immunotherapy group compared to the control group (HR 0.33; 95% CI 0.15–0.76; P = 0.006). In patients who received curative treatment for HCC, the significant improvement in RFS and OS as a result of adjuvant CIK cell immunotherapy lasted over 5 years without boosting.

AbstractObjectivesTo use the estimates from the Global Burden of Disease Study 2016 to describe patterns of suicide mortality globally, regionally, and for 195 countries and territories by age, sex, and Socio-demographic index, and to describe temporal trends between 1990 and 2016.DesignSystematic analysis.Main outcome measuresCrude and age standardised rates from suicide mortality and years of life lost were compared across regions and countries, and by age, sex, and Socio-demographic index (a composite measure of fertility, income, and education).ResultsThe total number of deaths from suicide increased by 6.7% (95% uncertainty interval 0.4% to 15.6%) globally over the 27 year study period to 817 000 (762 000 to 884 000) deaths in 2016. However, the age standardised mortality rate for suicide decreased by 32.7% (27.2% to 36.6%) worldwide between 1990 and 2016, similar to the decline in the global age standardised mortality rate of 30.6%. Suicide was the leading cause of age standardised years of life lost in the Global Burden of Disease region of high income Asia Pacific and was among the top 10 leading causes in eastern Europe, central Europe, western Europe, central Asia, Australasia, southern Latin America, and high income North America. Rates for men were higher than for women across regions, countries, and age groups, except for the 15 to 19 age group. There was variation in the female to male ratio, with higher ratios at lower levels of Socio-demographic index. Women experienced greater decreases in mortality rates (49.0%, 95% uncertainty interval 42.6% to 54.6%) than men (23.8%, 15.6% to 32.7%).ConclusionsAge standardised mortality rates for suicide have greatly reduced since 1990, but suicide remains an important contributor to mortality worldwide. Suicide mortality was variable across locations, between sexes, and between age groups. Suicide prevention strategies can be targeted towards vulnerable populations if they are informed by variations in mortality rates.