Overview of data-synthesis in systematic reviews of studies on outcome prediction models

Background: Many prognostic models have been developed. Different types of models, i.e. prognostic factor and outcome prediction studies, serve different purposes, which should be reflected in how the results are summarized in reviews. Therefore we set out to investigate how authors of reviews synthesize and report the results of primary outcome prediction studies. Methods: Outcome prediction reviews published in MEDLINE between October 2005 and March 2011 were eligible and 127 Systematic reviews with the aim to summarize outcome prediction studies written in English were identified for inclusion. Characteristics of the reviews and the primary studies that were included were independently assessed by 2 review authors, using standardized forms. Results: After consensus meetings a total of 50 systematic reviews that met the inclusion criteria were included. The type of primary studies included (prognostic factor or outcome prediction) was unclear in two-thirds of the reviews. A minority of the reviews reported univariable or multivariable point estimates and measures of dispersion from the primary studies. Moreover, the variables considered for outcome prediction model development were often not reported, or were unclear. In most reviews there was no information about model performance. Quantitative analysis was performed in 10 reviews, and 49 reviews assessed the primary studies qualitatively. In both analyses types a range of different methods was used to present the results of the outcome prediction studies. Conclusions: Different methods are applied to synthesize primary study results but quantitative analysis is rarely performed. The description of its objectives and of the primary studies is suboptimal and performance parameters of the outcome prediction models are rarely mentioned. The poor reporting and the wide variety of data synthesis strategies are prone to influence the conclusions of outcome prediction reviews. Therefore, there is much room for improvement in reviews of outcome prediction studies. (aut.ref.

Prediction of persistent shoulder pain in general practice: Comparing clinical consensus from a Delphi procedure with a statistical scoring system

<p>Abstract</p> <p>Background</p> <p>In prognostic research, prediction rules are generally statistically derived. However the composition and performance of these statistical models may strongly depend on the characteristics of the derivation sample. The purpose of this study was to establish consensus among clinicians and experts on key predictors for persistent shoulder pain three months after initial consultation in primary care and assess the predictive performance of a model based on clinical expertise compared to a statistically derived model.</p> <p>Methods</p> <p>A Delphi poll involving 3 rounds of data collection was used to reach consensus among health care professionals involved in the assessment and management of shoulder pain.</p> <p>Results</p> <p>Predictors selected by the expert panel were: symptom duration, pain catastrophizing, symptom history, fear-avoidance beliefs, coexisting neck pain, severity of shoulder disability, multisite pain, age, shoulder pain intensity and illness perceptions. When tested in a sample of 587 primary care patients consulting with shoulder pain the predictive performance of the two prognostic models based on clinical expertise were lower compared to that of a statistically derived model (Area Under the Curve, AUC, expert-based dichotomous predictors 0.656, expert-based continuous predictors 0.679 vs. 0.702 statistical model).</p> <p>Conclusions</p> <p>The three models were different in terms of composition, but all confirmed the prognostic importance of symptom duration, baseline level of shoulder disability and multisite pain. External validation in other populations of shoulder pain patients should confirm whether statistically derived models indeed perform better compared to models based on clinical expertise.</p

The search for stable prognostic models in multiple imputed data sets

<p>Abstract</p> <p>Background</p> <p>In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition.</p> <p>Methods</p> <p>Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping.</p> <p>Results</p> <p>Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model.</p> <p>Conclusion</p> <p>In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability.</p

Development of a novel analytical approach combining the quantification of amino acids, organic acids and glucose using HPLC-UV-Vis and HPLC-MS with screening via NMR

A simple, rapid, sensitive and selective procedure based on the combination of HPLC-UV-Vis and HPLC-MS has been developed and single laboratory partially validated for the determination of a set of 13 analytes present in a commercially available IVF medium utilising small sample volumes (20–30 μL). The composition fingerprint of the complex sample obtained by NMR spectroscopy in 11 minutes provided identification based on a screening of the metabolomic profile. HPLC-MS allowed the glucose–sodium adduct to be measured accurately and the working and linear ranges achieved were 0.028–0.389 mmol L−1 with a detection limit of 13 μM. HPLC-UV-Vis allowed accurate concentrations of pyruvic and lactic acids with linear ranges over 0.005–0.1 mmol L−1 with a limit of detection of 28 μM for pyruvic acid to be determined in 8 minutes, while lactic acid presented a linear range over 0.1–2 mmol L−1 with a limit of detection of 1.2 mM possible. The use of HPLC-UV-Vis allowed the chromatographic separation of 8 amino acids (aspartate, glutamate, serine, glycine, asparagine, glutamine, alanine, and proline), the dipeptide alanyl-glutamine and taurine previous to a chemical derivatization, providing a total run time of 40 minutes. The method was partially validated to show a linear range of 0.028–0.280 mmol L−1 with detection limits ranging between 1 and 30 μM. Development of the analytical approach provided determination and quantification of a set of 13 analytes from a very complex sample. Although well established analytical techniques were used here, combinatory methodologies were partially validated for the first time to this purpose. The novelty of the combination of techniques relies on a screening tool and a strategy to the future evaluation and an improved assessment of human embryo viability.M. Gómez-Mingot is grateful to the University of Alicante for a Fellowship and funding from the Ministerio de Educación y Ciencia MEC Spain, project (CTQ2007-62345). Luis A. Alcaraz was financed by the Spanish Ministry of Science and Innovation throughout the Juan de la Cierva program

Dynamics of career choice among students in undergraduate medical courses. A BEME systematic review: BEME Guide No. 33

<div><p></p><p><b>Introduction:</b> Due to the lack of a theoretically embedded overview of the recent literature on medical career decision-making, this study provides an outline of these dynamics. Since differences in educational routes to the medical degree likely affect career choice dynamics, this study focuses on medical career decision-making in educational systems with a Western European curriculum structure.</p><p><b>Methods:</b> A systematic search of electronic databases (Medline, Embase) was conducted from January 2008 to November 2014. A panel of seven independent reviewers performed the data extraction, quality assessment and data synthesis using the Bland–Meurer model of medical specialty choice as a reference.</p><p><b>Results:</b> Fifty-seven studies met the inclusion criteria for the review. Factors associated with specialty preference or career choice can be classified in five main categories: (1) medical school characteristics (e.g., curriculum structure), (2) student characteristics (e.g., age, personality), (3) student values (e.g., personal preference), (4) career needs to be satisfied (e.g., expected income, status, and work–life balance), and (5) perception of specialty characteristics (e.g., extracurricular or curricular experiences). Especially career needs and perceptions of specialty characteristics are often associated with medical career decision-making.</p><p><b>Conclusion:</b> Our results support that medical career decisions are formed by a matching of perceptions of specialty characteristics with personal needs. However, the process of medical career decision-making is not yet fully understood. Besides identifying possible predictors, future research should focus on detecting interrelations between hypothesized predictors and identify the determinants and interrelations at the various stages of the medical career decision-making process.</p></div

Metabolic cost and mechanical work for the step-to-step transition in walking after successful total ankle arthroplasty

The aim of this study was to investigate whether impaired ankle function after total ankle arthroplasty (TAA) affects the mechanical work during the step-to-step transition and the metabolic cost of walking. Respiratory and force plate data were recorded in 11 patients and 11 healthy controls while they walked barefoot at a fixed walking speed (FWS, 1.25 m/s) and at their self-selected speed (SWS). At FWS metabolic cost of transport was 28% higher for the TAA group, but at SWS there was no significant increase. During the step-to-step transition, positive mechanical work generated by the trailing TAA leg was lower and negative mechanical work in the leading intact leg was larger. Despite the increase in mechanical work dissipation during double support, no significant differences in total mechanical work were found over a complete stride. This might be a result of methodological limitations of calculating mechanical work. Nevertheless, mechanical work dissipated during the step-to-step transition at FWS correlated significantly with metabolic cost of transport: r = .540. It was concluded that patients after successful TAA still experienced an impaired lower leg function, which contributed to an increase in mechanical energy dissipation during the step-to-step transition, and to an increase in the metabolic demand of walking. © 2009 Elsevier B.V. All rights reserved

LRP10 genetic variants in familial Parkinson's disease and dementia with Lewy bodies: a genome-wide linkage and sequencing study

Background: Most patients with Parkinson's disease, Parkinson's disease dementia, and dementia with Lewy bodies do not carry mutations in known disease-causing genes. The aim of this study was to identify a novel gene implicated in the development of these disorders. Methods: Our study was done in three stages. First, we did genome-wide linkage analysis of an Italian family with dominantly inherited Parkinson's disease to identify the disease locus. Second, we sequenced the candidate gene in an international multicentre series of unrelated probands who were diagnosed either clinically or pathologically with Parkinson's disease, Parkinson's disease dementia, or dementia with Lewy bodies. As a control, we used gene sequencing data from individuals with abdominal aortic aneurysms (who were not examined neurologically). Third, we enrolled an independent series of patients diagnosed clinically with Parkinson's disease and controls with no signs or family history of Parkinson's disease, Parkinson's disease dementia, or dementia with Lewy bodies from centres in Portugal, Sardinia, and Taiwan, and screened them for specific variants. We also did mRNA and brain pathology studies in three patients from the international multicentre series carrying disease-associated variants, and we did functional protein studies in in-vitro models, including neurons from induced pluripotent stem-like cells. Findings: Molecular studies were done between Jan 1, 2008, and Dec 31, 2017. In the initial kindred of ten affected Italian individuals (mean age of disease onset 59·8 years [SD 8·7]), we detected significant linkage of Parkinson's disease to chromosome 14 and nominated LRP10 as the disease-causing gene. Among the international series of 660 probands, we identified eight individuals (four with Parkinson's disease, two with Parkinson's disease dementia, and two with dementia with Lewy bodies) who carried different, rare, potentially pathogenic LRP10 variants; one carrier was found among 645 controls with abdominal aortic aneurysms. In the independent series, two of these eight variants were detected in three additional Parkinson's disease probands (two from Sardinia and one from Taiwan) but in none of the controls. Of the 11 probands from the international and independent cohorts with LRP10 variants, ten had a positive family history of disease and DNA was available from ten affected relatives (in seven of these families). The LRP10 variants were present in nine of these ten relatives, providing independent—albeit limited—evidence of co-segregation with disease. Post-mortem studies in three patients carrying distinct LRP10 variants showed severe Lewy body pathology. Of nine variants identified in total (one in the initial family and eight in stage 2), three severely affected LRP10 expression and mRNA stability (1424+5delG, 1424+5G→A, and Ala212Serfs*17, shown by cDNA analysis), four affected protein stability (Tyr307Asn, Gly603Arg, Arg235Cys, and Pro699Ser, shown by cycloheximide-chase experiments), and two affected protein localisation (Asn517del and Arg533Leu; shown by immunocytochemistry), pointing to loss of LRP10 function as a common pathogenic mechanism. Interpretation: Our findings implicate LRP10 gene defects in the development of inherited forms of α-synucleinopathies. Future elucidation of the function of the LRP10 protein and pathways could offer novel insights into mechanisms, biomarkers, and therapeutic targets. Funding: Stichting ParkinsonFonds, Dorpmans-Wigmans Stichting, Erasmus Medical Center, ZonMw—Memorabel programme, EU Joint Programme Neurodegenerative Disease Research (JPND), Parkinson's UK, Avtal om Läkarutbildning och Forskning (ALF) and Parkinsonfonden (Sweden), Lijf and Leven foundation, and cross-border grant of Alzheimer Netherlands–Ligue Européene Contre la Maladie d'Alzheimer (LECMA)