GMC must consider case against paediatricians who suspected parents of fabricating child’s illness

A couple suspected of fabricating their daughter’s illness and threatened with having her taken into care have won a High Court ruling that the General Medical Council must reconsider their complaint against the two paediatricians who raised the concerns.

The girl, now 15 years old, was eventually diagnosed with chronic fatigue syndrome. The local council agreed to withdraw the care proceedings and was ordered to pay the family’s costs after an independent expert appointed by the court and the doctor treating the girl made the diagnosis.

Her father, named only as Mr F to protect his daughter’s identity, lodged a complaint with the GMC against the paediatricians, who were named in the High Court judgment as Dr A and Dr B.

Mr F’s complaint included an allegation that the doctors had changed their minds and accepted that chronic fatigue syndrome was the correct diagnosis but had not immediately informed the local authority or the court hearing the case.

The charges were drawn up and the case went to the GMC’s preliminary proceedings committee (PPC), but, in July 2004, that committee decided not to refer the case to the professional conduct committee and threw it out.

Mr F sought a judicial review, arguing that the allegations were sufficient, if proved, to support a finding of serious professional misconduct. The GMC was willing to send the case back to the PPC, but the two doctors intervened as interested parties to oppose the application.

Mr Justice Sullivan ruled that the committee had failed to deal with the allegations and should have made further inquiries. He said that the charges as formulated had raised a specific allegation that the doctors had engaged in deceitful conduct, which had to be dealt with in the committee’s reasoning, and sent the case back to the committee.

A spokesman for the GMC said, “We note the decision handed down by Mr Justice Sullivan. The case will be referred back to the PPC for consideration.”

You can read the rest of this article here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1459583/

Source: Dyer C. GMC must consider case against paediatricians who suspected parents of fabricating child’s illness. BMJ. 2006 May 13;332(7550):1110. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1459583/ (Full article)

Chronic fatigue syndrome: an update focusing on phenomenology and pathophysiology

Abstract:

PURPOSE OF REVIEW: Chronic fatigue syndrome is a controversial condition especially concerning its clinical definition and aetiopathogenesis. Most recent research progress has been made in phenomenology and pathophysiology and we focused our review on these two areas.

RECENT FINDINGS: The phenomenology research supports the notion of a discrete fatigue syndrome which can be distinguished from depression and anxiety. The current case definition, however, may need an improvement based on empirical data. Recent advances in understanding the pathophysiology of chronic fatigue syndrome continue to demonstrate the involvement of the central nervous system. Hyperserotonergic state and hypoactivity of the hypothalamic-pituitary-adrenal axis constitute other findings, but the question of whether these alterations are a cause or consequence of chronic fatigue syndrome still remains unanswered. Immune system involvement in the pathogenesis seems certain but the findings on the specific mechanisms are still inconsistent. Genetic studies provide some evidence of the syndrome being a partly genetic condition, but environmental effects seem to be still predominant and identification of specific genes is still at a very early stage.

SUMMARY: The recent findings suggest that further research is needed in improving the current case definition; investigating overlaps and boundaries among various functional somatic syndromes; answering the question of whether the pathophysiologic findings are a cause or consequence; and elucidating the involvement of the central nervous system, immune system and genetic factors.

 

Source: Cho HJ, Skowera A, Cleare A, Wessely S. Chronic fatigue syndrome: an update focusing on phenomenology and pathophysiology. Curr Opin Psychiatry. 2006 Jan;19(1):67-73. https://www.ncbi.nlm.nih.gov/pubmed/16612182

 

Clinical methodology and its implications for the study of therapeutic interventions for chronic fatigue syndrome: a commentary

Abstract:

Chronic fatigue syndrome (CFS) is a complex, multisymptom illness of unknown etiology. A variety of operational case definitions based on symptom report have been developed that share some common clinical features. Patients often come to clinical presentation after months or, more typically, years of symptomatic distress. Comorbid presentation with psychiatric illnesses has been noted.

Due to these fundamental issues, the impact of patient selection and the specification of the methods of outcome assessment loom large in therapeutic studies of CFS. While a substantial body of research has focused on increasing our understanding of the basic pathobiology of CFS, there have been comparatively fewer studies that have addressed the problems of patient characterization and outcome assessment. The role of clinical methodology in the study of the therapeutics of CFS is not trivial, and may confound our understanding of pragmatic recommendations for treatment.

 

Source: Demitrack MA. Clinical methodology and its implications for the study of therapeutic interventions for chronic fatigue syndrome: a commentary. Pharmacogenomics. 2006 Apr;7(3):521-8. https://www.ncbi.nlm.nih.gov/pubmed/16610962

 

Interpreter of maladies: redescription mining applied to biomedical data analysis

Abstract:

Comprehensive, systematic and integrated data-centric statistical approaches to disease modeling can provide powerful frameworks for understanding disease etiology. Here, one such computational framework based on redescription mining in both its incarnations, static and dynamic, is discussed.

The static framework provides bioinformatic tools applicable to multifaceted datasets, containing genetic, transcriptomic, proteomic, and clinical data for diseased patients and normal subjects. The dynamic redescription framework provides systems biology tools to model complex sets of regulatory, metabolic and signaling pathways in the initiation and progression of a disease.

As an example, the case of chronic fatigue syndrome (CFS) is considered, which has so far remained intractable and unpredictable in its etiology and nosology. The redescription mining approaches can be applied to the Centers for Disease Control and Prevention’s Wichita (KS, USA) dataset, integrating transcriptomic, epidemiological and clinical data, and can also be used to study how pathways in the hypothalamic-pituitary-adrenal axis affect CFS patients.

 

Source: Waltman P, Pearlman A, Mishra B. Interpreter of maladies: redescription mining applied to biomedical data analysis. Pharmacogenomics. 2006 Apr;7(3):503-9. https://www.ncbi.nlm.nih.gov/pubmed/16610960

 

Allostatic load is associated with symptoms in chronic fatigue syndrome patients

Abstract:

OBJECTIVES: To further explore the relationship between chronic fatigue syndrome (CFS) and allostatic load (AL), we conducted a computational analysis involving 43 patients with CFS and 60 nonfatigued, healthy controls (NF) enrolled in a population-based case-control study in Wichita (KS, USA). We used traditional biostatistical methods to measure the association of high AL to standardized measures of physical and mental functioning, disability, fatigue and general symptom severity. We also used nonlinear regression technology embedded in machine learning algorithms to learn equations predicting various CFS symptoms based on the individual components of the allostatic load index (ALI).

METHODS: An ALI was computed for all study participants using available laboratory and clinical data on metabolic, cardiovascular and hypothalamic-pituitary-adrenal (HPA) axis factors. Physical and mental functioning/impairment was measured using the Medical Outcomes Study 36-item Short Form Health Survey (SF-36); current fatigue was measured using the 20-item multidimensional fatigue inventory (MFI); frequency and intensity of symptoms was measured using the 19-item symptom inventory (SI). Genetic programming, a nonlinear regression technique, was used to learn an ensemble of different predictive equations rather just than a single one. Statistical analysis was based on the calculation of the percentage of equations in the ensemble that utilized each input variable, producing a measure of the ‘utility’ of the variable for the predictive problem at hand. Traditional biostatistics methods include the median and Wilcoxon tests for comparing the median levels of subscale scores obtained on the SF-36, the MFI and the SI summary score.

RESULTS: Among CFS patients, but not controls, a high level of AL was significantly associated with lower median values (indicating worse health) of bodily pain, physical functioning and general symptom frequency/intensity. Using genetic programming, the ALI was determined to be a better predictor of these three health measures than any subcombination of ALI components among cases, but not controls.

 

Source: Goertzel BN, Pennachin C, de Souza Coelho L, Maloney EM, Jones JF, Gurbaxani B. Allostatic load is associated with symptoms in chronic fatigue syndrome patients. Pharmacogenomics. 2006 Apr;7(3):485-94. https://www.ncbi.nlm.nih.gov/pubmed/16610958

 

Combinations of single nucleotide polymorphisms in neuroendocrine effector and receptor genes predict chronic fatigue syndrome

Abstract:

OBJECTIVE: This paper asks whether the presence of chronic fatigue syndrome (CFS) can be more accurately predicted from single nucleotide polymorphism (SNP) profiles than would occur by chance.

METHODS: Specifically, given SNP profiles for 43 CFS patients, together with 58 controls, we used an enumerative search to identify an ensemble of conjunctive rules that predict whether a patient has CFS.

RESULTS: The accuracy of the rules reached 76.3%, with the highest accuracy rules yielding 49 true negatives, 15 false negatives, 28 true positives and nine false positives (odds ratio [OR] 8.94, p < 0.0001). Analysis of the SNPs used most frequently in the overall ensemble of rules gave rise to a list of ‘most important SNPs’, which was not identical to the list of ‘most differentiating SNPs’ that one would calculate via studying each SNP independently. The top three genes containing the SNPs accounting for the highest accumulated importances were neuronal tryptophan hydroxylase (TPH2), catechol-O-methyltransferase (COMT) and nuclear receptor subfamily 3, group C, member 1 glucocorticoid receptor (NR3C1).

CONCLUSION: The fact that only 28 out of several million possible SNPs predict whether a person has CFS with 76% accuracy indicates that CFS has a genetic component that may help to explain some aspects of the illness.

 

Source: Goertzel BN, Pennachin C, de Souza Coelho L, Gurbaxani B, Maloney EM, Jones JF. Combinations of single nucleotide polymorphisms in neuroendocrine effector and receptor genes predict chronic fatigue syndrome. Pharmacogenomics. 2006 Apr;7(3):475-83. https://www.ncbi.nlm.nih.gov/pubmed/16610957

 

Chronic fatigue syndrome and high allostatic load

Abstract:

STUDY POPULATION: We examined the relationship between chronic fatigue syndrome (CFS) and allostatic load in a population-based, case-control study of 43 CFS patients and 60 nonfatigued, healthy controls from Wichita, KS, USA.

METHODS: An allostatic load index was computed for all study participants using available laboratory and clinical data, according to a standard algorithm for allostatic load. Logistic regression analysis was used to compute odds ratios (ORs) as estimates of relative risk in models that included adjustment for matching factors and education; 95% confidence intervals (CIs) were computed to estimate the precision of the ORs.

RESULTS: CFS patients were 1.9-times more likely to have a high allostatic load index than controls (95% CI = 0.75, 4.75) after adjusting for education level, in addition to matching factors. The strength of this association increased in a linear trend across categories of low, medium and high levels of allostatic load (p = 0.06).

CONCLUSION: CFS was associated with a high level of allostatic load. The three allostatic load components that best discriminated cases from controls were waist:hip ratio, aldosterone and urinary cortisol.

 

Source: Maloney EM, Gurbaxani BM, Jones JF, de Souza Coelho L, Pennachin C, Goertzel BN. Chronic fatigue syndrome and high allostatic load. Pharmacogenomics. 2006 Apr;7(3):467-73. https://www.ncbi.nlm.nih.gov/pubmed/16610956

 

Linear data mining the Wichita clinical matrix suggests sleep and allostatic load involvement in chronic fatigue syndrome

Abstract:

OBJECTIVES: To provide a mathematical introduction to the Wichita (KS, USA) clinical dataset, which is all of the nongenetic data (no microarray or single nucleotide polymorphism data) from the 2-day clinical evaluation, and show the preliminary findings and limitations, of popular, matrix algebra-based data mining techniques.

METHODS: An initial matrix of 440 variables by 227 human subjects was reduced to 183 variables by 164 subjects. Variables were excluded that strongly correlated with chronic fatigue syndrome (CFS) case classification by design (for example, the multidimensional fatigue inventory [MFI] data), that were otherwise self reporting in nature and also tended to correlate strongly with CFS classification, or were sparse or nonvarying between case and control. Subjects were excluded if they did not clearly fall into well-defined CFS classifications, had comorbid depression with melancholic features, or other medical or psychiatric exclusions. The popular data mining techniques, principle components analysis (PCA) and linear discriminant analysis (LDA), were used to determine how well the data separated into groups. Two different feature selection methods helped identify the most discriminating parameters.

RESULTS: Although purely biological features (variables) were found to separate CFS cases from controls, including many allostatic load and sleep-related variables, most parameters were not statistically significant individually. However, biological correlates of CFS, such as heart rate and heart rate variability, require further investigation.

CONCLUSIONS: Feature selection of a limited number of variables from the purely biological dataset produced better separation between groups than a PCA of the entire dataset. Feature selection highlighted the importance of many of the allostatic load variables studied in more detail by Maloney and colleagues in this issue [1] , as well as some sleep-related variables. Nonetheless, matrix linear algebra-based data mining approaches appeared to be of limited utility when compared with more sophisticated nonlinear analyses on richer data types, such as those found in Maloney and colleagues [1] and Goertzel and colleagues [2] in this issue.

 

Source: Gurbaxani BM, Jones JF, Goertzel BN, Maloney EM. Linear data mining the Wichita clinical matrix suggests sleep and allostatic load involvement in chronic fatigue syndrome. Pharmacogenomics. 2006 Apr;7(3):455-65. https://www.ncbi.nlm.nih.gov/pubmed/16610955

 

Exploration of the gene expression correlates of chronic unexplained fatigue using factor analysis

Abstract:

OBJECTIVE: To identify biomarkers of chronic fatigue syndrome (CFS) and related disorders through analysis of microarray data, pathology test results and self-report symptom profiles.

METHOD: To empirically derive the symptom domains of the illnesses, factor analysis was performed on responses to self-report questionnaires (multidimensional fatigue inventory, Centers for Disease Control and Prevention (CDC) symptom inventory and Zung depression scale) before validation with independent datasets. Gene expression patterns that distinguished subjects across each factor dimension were then sought.

RESULTS: A four-factor solution was favored, featuring ‘fatigue’ and ‘mood disturbance’ factors. Scores on these factors correlated with measures of disability on the Short Form (SF)-36. A total of 57 genes that distinguished subjects along each factor dimension were identified, although the separation was significant only for subjects beyond the extreme (15th and 85th) percentiles of severity. Clustering of laboratory parameters with expression of these genes revealed associations with serum measurements of pH, electrolytes, glucose, urea, creatinine, and liver enzymes (aspartate amino transferase [AST] and alanine amino transferase [AST]); as well as hematocrit and white cell count.

CONCLUSION: CFS is a complex syndrome that cannot simply be associated with changes in individual laboratory tests or expression levels of individual genes. No clear association with gene expression and individual symptom domains was found. However, analysis of such multifacetted datasets is likely to be an important means to elucidate the pathogenesis of CFS.

 

Source: Fostel J, Boneva R, Lloyd A. Exploration of the gene expression correlates of chronic unexplained fatigue using factor analysis. Pharmacogenomics. 2006 Apr;7(3):441-54. https://www.ncbi.nlm.nih.gov/pubmed/16610954

 

Gene expression profile exploration of a large dataset on chronic fatigue syndrome

Abstract:

OBJECTIVE: To gain understanding of the molecular basis of chronic fatigue syndrome (CFS) through gene expression analysis using a large microarray data set in conjunction with clinically administrated questionnaires.

METHOD: Data from the Wichita (KS, USA) CFS Surveillance Study was used, comprising 167 participants with two self-report questionnaires (multidimensional fatigue inventory [MFI] and Zung depression scale [Zung]), microarray data, empiric classification, and others. Microarray data was analyzed using bioinformatics tools from ArrayTrack.

RESULTS: Correspondence analysis was applied to the MFI questionnaire to select the 23 samples having either the most or the least fatigue, and to the Zung questionnaire to select the 26 samples having either the most or least depression; ten samples were common, resulting in a total of 39 samples. The MFI and Zung-based CFS/non-CFS (NF) classifications on the 39 samples were consistent with the empiric classification. Two differentially-expressed gene lists were determined, 188 fatigue-related genes and 164 depression-related genes, which shared 24 common genes and involved 11 common pathways. Principal component analysis based on 24 genes clearly separates 39 samples with respect to their likelihood to be CFS. Most of the 24 genes are not previously reported for CFS, yet their functions are consistent with the prevailing model of CFS, such as immune response, apoptosis, ion channel activity, signal transduction, cell-cell signaling, regulation of cell growth and neuronal activity. Hierarchical cluster analysis was performed based on 24 genes to classify 128 (=167-39) unassigned samples. Several of the 11 identified common pathways are supported by earlier findings for CFS, such as cytokine-cytokine receptor interaction and neuroactive ligand-receptor interaction. Importantly, most of the 11 common pathways are interrelated, suggesting complex biological mechanisms associated with CFS.

CONCLUSION: Bioinformatics is critical in this study to select definitive sample groups, analyze gene expression data and gain insight into biological mechanisms. The 24 identified common genes and 11 common pathways could be important in future studies of CFS at the molecular level.

 

Source: Fang H, Xie Q, Boneva R, Fostel J, Perkins R, Tong W. Gene expression profile exploration of a large dataset on chronic fatigue syndrome. Pharmacogenomics. 2006 Apr;7(3):429-40. https://www.ncbi.nlm.nih.gov/pubmed/16610953