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At least 91 records · Page 5Linked to original sources
PubMed2000

A full genome scan for age-related maculopathy.

Age-related macular degeneration or age-related maculopathy (ARM) is a major public health issue, as it is the leading cause of irreversible vision loss in the elderly in the Western world. Using three diagnostic models, we have genotyped markers in 16 plausible candidate regions and have carried out a genome-wide screen for ARM susceptibility loci. A panel of 225 ARM families comprising up to 212 affected sib pairs was genotyped for 386 markers. Under our most stringent diagnostic model, the regions with the strongest evidence of linkage were on chromosome 9 near D9S301 and on 10 near D10S1230, with peak multipoint heterogeneity LOD scores (HLOD) of 1.87 and 1. 42 and peak GeneHunter-Plus non-parametric LOD scores (GHP LOD) of 1. 69 and 1.83. After expanding our initial set of families to 364 ARM families with up to 329 affected sib pairs, the linkage signal on chromosome 9 vanished, while the chromosome 10 signal decreased to a GHP LOD of about 1.0, with a SimIBD P -value of 0.008 under the broadest diagnostic model with marker D10S1236. After error filtration, the GHP LOD increased to 1.27 under our most stringent model and 1.42 under our broadest model, peaking near D10S1236. This peak was seen consistently across all three diagnostic models. Our analyses also excluded up to nine different candidate regions and identified a few other regions of potential linkage, suitable for further studies. Of particular interest was the region on chromosome 5 near D5S1480, where a reasonable candidate gene, glutathione peroxidase 3, resides.

Chromosomes, Human, Pair 10↗
PubMed1991

Diagnosing pelvic inflammatory disease. A comprehensive analysis and considerations for developing a new model.

OBJECTIVE: --To examine the accuracy of existing diagnostic indicators for pelvic inflammatory disease and to develop guidelines for a new diagnostic model. DATA SOURCES: --Studies were identified for the period 1969 through 1990. A Medline search of the English-language literature was conducted using the subject terms pelvic inflammatory disease or salpingitis and diagnosis. In addition, abstracts and bibliographies of articles and books were reviewed. STUDY SELECTION: --Studies were selected if pelvic inflammatory disease was diagnosed using laparoscopic findings or narrow clinical rules. Of the 15 reports identified, 12 were included in this analysis. The selected studies were grouped by a quality rating based on subject selection, definition of pelvic inflammatory disease, data analysis, and other measures. DATA EXTRACTION: --Diagnostic findings were divided into four categories: historical (symptoms), clinical examination (signs), laboratory, and combinations of the above. Sensitivity and specificity were extracted using raw data. Data were classified by quality rating. DATA SYNTHESIS: --Historical findings were usually not statistically significant predictors of pelvic inflammatory disease, and when they were they tended toward low sensitivity and high specificity, while clinical findings were somewhat more sensitive and about as specific. Several laboratory tests showed consistent value in pelvic inflammatory disease diagnosis, with high sensitivity and specificity. Combinations of indicators permitted high sensitivity or high specificity but not both simultaneously. CONCLUSIONS: --No single or combination diagnostic indicator was found to reliably predict pelvic inflammatory disease. Combining published evidence with practical clinical considerations, a diagnostic approach is proposed that emphasizes diagnostic sensitivity when clinical presentation is mild and more thorough evaluation when a woman is severely ill. Research is needed to evaluate the accuracy and acceptability of specific diagnostic models and to investigate new diagnostic indicators.

Female↗
PubMed2006

Diagnosis of gastric cancer by serum proteomic fingerprinting.

BACKGROUND & AIMS: Accurate serum biomarkers for gastric cancer currently are lacking. We attempted to identify potential diagnostic serum markers for gastric cancer with the use of the surface-enhanced laser desorption/ionization ProteinChip technology. METHODS: The study was divided into 3 phases: (1) discovery of potential diagnostic markers using sera of gastric cancer patients and controls, (2) development of a diagnostic model, and (3) independent validation of the diagnostic model using a different cohort of gastric cancer and control patients. The serum proteins/peptides were analyzed with 2 types of ProteinChip arrays, IMAC30 arrays loaded with copper (II) ion and CM10 (weak cation exchange) arrays. RESULTS: In the discovery set, peak intensities of 31 surface-enhanced laser desorption/ionization proteomic features were significantly higher in gastric cancer patients. The tumor-specific nature of 6 proteomic features with the mass/charge (m/z) values of 5098, 8592, 8610, 11,468, 11,804, and 50,140 was verified by their lower peak intensities in postoperative sera. After excluding the sodium adduct peak (8610 m/z) of the 8592 m/z protein, the peak intensities of the tumor-specific proteomic features were used to develop a linear regression model for calculating a diagnostic index. The area under the receiver operating characteristic curve of the corresponding diagnostic index was 0.92 (95% confidence interval, 0.85-0.99) in the independent validation set. At a specificity of 95%, the sensitivity for gastric cancer detection was 83%. CONCLUSIONS: A unique serum proteomic fingerprint can be detected in the sera of gastric cancer patients, which may be useful in the noninvasive diagnosis of gastric cancer.

Adult↗
PubMed2026

Development and preliminary validation of plasma cell-free DNA methylation-based diagnostic prediction model for colorectal cancer detection.

BACKGROUND: Colorectal cancer (CRC) is a common malignancy associated with genetic and epigenetic alterations. Several methylation biomarkers have been investigated for non-invasive CRC detection; however, their reported performance varies across clinical settings, and the detection of early-stage or precancerous disease and discrimination from non-malignant colorectal conditions remain challenging. This exploratory study aimed to identify reproducible CRC-associated plasma cell-free DNA (cfDNA) methylation regions and to develop and preliminarily evaluate diagnostic prediction model for distinguishing CRC from healthy controls and benign samples. METHODS: Public CRC tissue methylation datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify reproducible CRC-associated methylation alterations. Plasma cfDNA methylation was profiled using methyl-CpG-binding-domain enrichment followed by paired-end sequencing in patients with CRC, patients with colorectal polyps, and healthy controls. After quality-control filtering, 30 CRC and healthy-control samples were randomly allocated at the participant level in a 7:3 ratio to a development set comprising 10 patients with CRC and 11 healthy controls and a held-out test set comprising 4 patients with CRC and 5 healthy controls. Hypermethylated regions were selected using least absolute shrinkage and selection operator (LASSO) logistic regression. The 12-region model was evaluated in the held-out test set and subsequently applied to 10 colorectal polyp samples without refitting or recalibration. RESULTS: Tissue methylation analysis identified reproducible CRC-associated alterations across independent datasets. In the plasma development set, 707 differentially methylated regions (DMRs) were identified between CRC and healthy-control samples, including 324 hypermethylated and 383 hypomethylated regions. LASSO regression selected a 12-region hypermethylation signature. In the held-out test set, the model achieved an area under the curve (AUC) of 0.85 [95% confidence interval (CI): 0.579-1.000]. At the development-set-derived threshold, sensitivity was 75.0% (3/4), specificity was 60.0% (3/5), and accuracy was 66.7% (6/9). When the original model was applied to colorectal polyp samples, model scores were significantly higher in both CRC and polyp samples than in healthy controls, while CRC samples showed a tendency toward higher scores than polyp samples. CONCLUSIONS: This exploratory study identified a 12-region plasma cfDNA hypermethylation signature associated with CRC and developed a LASSO-based diagnostic prediction model that showed preliminary discrimination between CRC and healthy controls in a small held-out test set. By integrating tissue methylation evidence with plasma cfDNA profiling, this study expands the repertoire of candidate region-level methylation markers for blood-based CRC detection.

Colorectal cancer (CRC)↗
PubMedDate not supplied

Refinements to the Diagnostic Cost Group (DCG) model.

The Diagnostic Cost Group (DCG) model, originally developed by Ash et al. (1986, 1989), has been proposed as an alternative to the existing payment system for reimbursing Medicare health maintenance organizations, the Adjusted Average Per Capita Cost (AAPCC). The DCG model is a linear regression model that uses both demographic and diagnostic information to predict total plan payments for health care. This paper extends previous work by estimating the model using 1984-85 data and by developing a more thorough method for classifying hospitalizations by degrees of discretion. It also explores the loss of predictive power resulting from not using diagnoses for the most discretionary hospitalizations for calculating payments. The paper examines a number of extensions and refinements to the basic DCG model.

Aged↗
PubMed2005

[Diagnostic application of serum protein pattern and artificial neural network software in breast cancer].

BACKGROUND & OBJECTIVE: The progress in proteomics provides a novel platform for early diagnosis of cancer, and screening for new tumor biomarkers. This study was designed to develop and evaluate a diagnostic model of breast cancer with surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS) ProteinChip array technology and artificial neural network software. METHODS: SELDI-TOF-MS ProteinChip was used to detect serum protein patterns of 49 patients with breast cancer, and 33 healthy women. Diagnostic model was developed, and validated using artificial neural network software. RESULTS: An intact diagnostic model from all 253 discrepant protein peaks, and a terse model from the top-scored 4 peaks were built. The diagnostic sensitivity, and specificity of the intact model were 83.33% (15/18), and 88.89% (8/9)u the detection rates of breast cancer of stage I, and stage II-IV using the intact model were 90.00% (9/10), and 75.00% (6/8). The diagnostic sensitivity, and specificity of the terse model were 76.47% (13/17), and 90.00% (9/10)u the detection rates of breast cancer of stage I, and stage II-IV using the terse model were 100.00% (3/3), and 71.43% (10/14). The diagnostic values of these 2 models were similar (P>0.05). Their diagnostic abilities to breast cancer of stage I were not worse than those to breast cancer of stage II-IV (P>0.05). CONCLUSION: High sensitivity and specificity achieved by this method show great potential for early diagnosis of breast cancer, and screening for new tumor biomarkers.

Adult↗
PubMed2003

Primer on multiple regression models for diagnostic imaging research.

This article provides an introduction to multiple regression analysis and its application in diagnostic imaging research. We begin by examining why multiple regression models are needed in the evaluation of diagnostic imaging technologies. We then examine the broad categories of available models, notably multiple linear regression models for continuous outcomes and logistic regression models for binary outcomes. The purpose of this article is to elucidate the scientific logic, meaning, and interpretation of multiple regression models by using examples from the diagnostic imaging literature.

Confounding Factors, Epidemiologic↗
PubMed2004

A cautionary note on the robustness of latent class models for estimating diagnostic error without a gold standard.

Modeling diagnostic error without a gold standard has been an active area of biostatistical research. In a majority of the approaches, model-based estimates of sensitivity, specificity, and prevalence are derived from a latent class model in which the latent variable represents an individual's true unobserved disease status. For simplicity, initial approaches assumed that the diagnostic test results on the same subject were independent given the true disease status (i.e., the conditional independence assumption). More recently, various authors have proposed approaches for modeling the dependence structure between test results given true disease status. This note discusses a potential problem with these approaches. Namely, we show that when the conditional dependence between tests is misspecified, estimators of sensitivity, specificity, and prevalence can be biased. Importantly, we demonstrate that with small numbers of tests, likelihood comparisons and other model diagnostics may not be able to distinguish between models with different dependence structures. We present asymptotic results that show the generality of the problem. Further, data analysis and simulations demonstrate the practical implications of model misspecification. Finally, we present some guidelines about the use of these models for practitioners.

Biometry↗
PubMed1998

[Integrating qualitative analysis with quantitative grey relation analysis in the comprehensive evaluation for solving medical problems].

This paper integrates qualitativ analysis with quantitative grey relation analysis in the comprehensive evaluation for solving medical problems, especially diagnostic problems, and good results are obtained. In it, some new medical concepts such as diagnostic value, diagnostic grey degree and information cover rate are defined. The given example demonstrates that when used in clinical diagnosis, this diagnostic model makes a feature of high diagnostic coincidence rate as well as low misdiagnosis rate and it overcomes the common disability of general medical diagnostic model in quantitative analysis.

Clinical Competence↗
PubMed1997

Latent variable modeling of diagnostic accuracy.

Latent class analysis has been applied in medical research to assessing the sensitivity and specificity of diagnostic tests/diagnosticians. In these applications, a dichotomous latent variable corresponding to the unobserved true disease status of the patients is assumed. Associations among multiple diagnostic tests are attributed to the unobserved heterogeneity induced by the latent variable, and inferences for the sensitivities and specificities of the diagnostic tests are made possible even though the true disease status is unknown. However, a shortcoming of this approach to analyses of diagnostic tests is that the standard assumption of conditional independence among the diagnostic tests given a latent class is contraindicated by the data in some applications. In the present paper, models incorporating dependence among the diagnostic tests given a latent class are proposed. The models are parameterized so that the sensitivities and specificities of the diagnostic tests are simple functions of model parameters, and the usual latent class model obtains as a special case. Marginal models are used to account for the dependencies within each latent class. An accelerated EM gradient algorithm is demonstrated to obtain maximum likelihood estimates of the parameters of interest, as well as estimates of the precision of the estimates.

Analysis of Variance↗
PubMed1995

Observer-based dynamic patient modeling for diagnostic computer programs in long term treatment.

This article shows the dynamic patient model concept, the major argument of which is that the state of the patient could change during the computer aided diagnostic procedure. It introduces the definition of quasi parallel diagnostic systems and quasi parallel sensitive points. A kind of verification procedure can be seen for classical diagnostics, examining diagnostic systems by temporally behavior. The rest of the paper represents an adaptive diagnostic structure, convenient for observing and monitoring a dynamically modeled patient in long term treatment. The basis of the structure shown here is the observer theory presented and applied previously in signal processing.

Diagnosis, Computer-Assisted↗
PubMed1992

Genetic linkage analysis of bipolar affective disorder in an Old Order Amish pedigree.

We have used genetic linkage analysis in an effort to identify a gene responsible for bipolar affective disorder (BAD) in an Old Order Amish pedigree. The initial study of this pedigree showed strong evidence for linkage of the chromosome 11p15 markers HRAS1 and the insulin gene (INS) to BAD, whereas a second report found no evidence for linkage. We have independently determined the INS and HRAS1 genotypes from 81 individuals in this pedigree. A polymerase chain reaction (PCR) assay was used to score INS alleles that are difficult to distinguish from one another by conventional agarose gel electrophoresis. In addition, we used four separate diagnostic models to score individuals with psychiatric illness as either affected or unaffected. No evidence of significant linkage between BAD and the markers was found with either two-point or multipoint analysis regardless of which diagnostic model was used. However, exclusion of the region of chromosome 11 between INS and RAS1 as a possible location for the BAD gene in this family depended on the diagnostic model. Further genetic linkage studies with additional DNA markers that span the genome are necessary to determine the chromosomal location of the BAD gene in this family.

Base Sequence↗
PubMed2002

The Wellcome trust UK-Irish bipolar affective disorder sibling-pair genome screen: first stage report.

We have completed the first stage of a two-stage genome wide screen designed to identify chromosomal regions that may harbour susceptibility genes for bipolar affective disorder. The first stage screening sample included 509 subjects from 151 nuclear families recruited within the United Kingdom and Republic of Ireland. This sample contained 154 narrowly defined affected sibling pairs (DSM-IV BPI) and 258 broadly defined affected sibling pairs (DSM-IV BPI, SABP, BPII, BPNOS or MDD(R)), approximately two thirds of all families contained at least one other additional typed individual. All individuals were genotyped using 398 highly polymorphic microsatellite markers from Applied Biosystems's Linkage Mapping Set Version 2. The average inter-marker distance was 9.6 cM and the mean heterozygosity was 0.78. Analysis of these data using non-parametric linkage methods (MAPMAKER/SIBS) found no evidence for loci of major effect and no regions reached genome-wide significance for either suggestive or significant linkage. We identified 19 points across the genome where the MLS exceeded a value set for follow up in our second stage screen (MLS > or = 0.74 (equivalent to a nominal pointwise significance of 5%) under the narrowest diagnostic model). These points were on chromosomes 2, 3, 4, 6, 7, 9, 10, 12, 17, 18 & X. Some of these points overlapped with previous linkage reports both within bipolar affective disorder and other psychiatric illnesses. Under the narrowest diagnostic model, the single most significant multipoint linkage was on chromosome 18 at marker D18S452 (MLS=1.54). Overall the highest MLS was 1.70 on chromosome 2 at marker D2S125, under the broadest diagnostic model.

Adult↗
PubMed2000

[Management of breech delivery in term pregnancy: own diagnostic and prognostic model].

Using own diagnostic and prognostic model, 1157 pregnant women with breech presentation in term were analysed. In 1008 cases (87.1%) they delivered according to prognosis established formerly. Manual support in delivery was required in 748 cases (64.6%). Elective caesarean section was performed in 260 women (22.5%). In 149 cases (12.9%) immediate caesarean section appeared, because of additional reasons, despite normal delivery prognosis.

Breech Presentation↗
PubMed2004

Age-related maculopathy: a genomewide scan with continued evidence of susceptibility loci within the 1q31, 10q26, and 17q25 regions.

Age-related maculopathy (ARM), or age-related macular degeneration, is one of the most common causes of visual impairment in the elderly population of developed nations. In a combined analysis of two previous genomewide scans that included 391 families, containing up to 452 affected sib pairs, we found linkage evidence in four regions: 1q31, 9p13, 10q26, and 17q25. We now have added a third set of families and have performed an integrated analysis incorporating 530 families and up to 736 affected sib pairs. Under three diagnostic models, we have conducted linkage analyses using parametric (heterogeneity LOD [HLOD] scores under an autosomal dominant model) and nonparametric (Sall statistic) methods. There is ongoing evidence of susceptibility loci within the 1q31, 10q26, and 17q25 regions. If we treat the third set of families as a replication set, then two regions (10q26 and 17q25) are replicated, with LOD scores >1.0. If we pool all our data together, then four regions (1q31, 2q14.3, 10q26, and 17q25) show HLOD or Sall scores > or =2.0. Within the 1q31 region, we observed an HLOD of 2.72 (genomewide P=.061) under our least stringent diagnostic model, whereas the 17q25 region contained a maximal HLOD of 3.53 (genomewide P=.007) under our intermediate diagnostic model. We have evaluated our results with respect to the findings from several new independent genomewide linkage studies and also have completed ordered subset analyses (OSAs) with apolipoprotein E alleles, smoking history, and age at onset as stratifying covariates. The OSAs generate the interesting hypothesis that the effect of smoking on the risk of ARM is accentuated by a gene in the 10q26 region--a region implicated by four other studies.

Chromosomes, Human, Pair 1↗