Differential Diagnostic Model Research Articles

ДОСЛІДЖЕННЯ МОДЕЛІ ДИФЕРЕНЦІЙНОЇ ДІАГНОСТИКИ МЕТОДОМ ПОТЕНЦІЙНИХ ФУНКЦІЙ ДЛЯ КЛАСИФІКАЦІЇ НЕВРОЛОГІЧНИХ ХВОРОБ

Informatization in medicine offers a lot of opportunities to enhance quality of medical support, accuracy of diagnosis and provides the use of accumulated experience. Modern program systems are utilized now as additional tools to get appropriate advice.This article offers the way to provide help for neurology department doctor of NCSH «OKHMATDYT» during diagnosis determining. It was decided to design the program system for this purpose based on differential diagnostic model.The key problems in differential diagnosis are symptoms similarity between each other in one disease group and the absence of key symptom. Therefore the differential diagnostic model is needed. It is constructed using the potential function method in characteristics space. This characteristics space is formed by 100-200 points - patients with their symptoms.The main feature of this method here is that the decision function is building during recognition step united with learning that became possible with the help of modern powerful computers.

Leukemia inhibitory factor receptor is a novel immunomarker in distinction of well-differentiated HCC from dysplastic nodules.

Differential diagnosis of well-differentiated hepatocellular carcinoma (WD-HCC) and high-grade dysplastic nodules (HGDNs) represents a challenge for pathologists. Several immunohistochemistry markers have been identified to distinguish hepatocellular carcinoma (HCC) from HGDNs. However, sensitivity or specificity of the individual marker is still limited. In this study, we analyzed dynamic alteration of leukemia inhibitory factor receptor (LIFR) and CD34 during hepatocarcinogenesis from dysplastic nodules to small HCC. The diagnostic performance of LIFR and CD34 combination in WD-HCC and HGDNs was investigated by logistic regression models and validated in an independent validation cohort. LIFR was decreased and CD34 was increased along with stepwise progression of hepatocarcinogenesis from low-grade dysplastic nodules (LGDNs) to small HCC. The sensitivity and specificity of the LIFR and CD34 combination for WD-HCC detection were 93.5% and 90.5%, respectively. In addition, colony formation assay was used to explore the role of LIFR in tumorigenesis. Silencing of LIFR could significantly promote colony formation of HCC cells, whereas ectopic overexpression of LIFR resulted in impaired ability of colony formation of HCC cells. These findings indicate that LIFR and CD34 combination may be used as an available differential diagnostic model for WD-HCC from HGDNs in clinical practice.

A novel differential diagnostic model for multiple primary lung cancer: Differentially-expressed gene analysis of multiple primary lung cancer and intrapulmonary metastasis.

The incidence of synchronous multiple primary lung cancer (MPLC) is increasing. However, present diagnostic methods are unable to satisfy the individualized treatment requirements of patients with MPLC. The present study aimed to establish a quantitative mathematical model and analyze its diagnostic value for distinguishing between MPLC and cases of the histologically similar disease, intrapulmonary metastasis (IPM). The sum value of the differential expression ratios of four proteins, namely p53, p16, p27 and c-erbB2, was evaluated by immunohistochemically-staining specimens of primary cancers, second separate cancers, metastatic lymph nodes and metastatic cancers. The sum value of the differential expression ratio of the four proteins from the primary tumor and the lymph-node metastasis or metastatic cancer was <90 in the 11 patients with a single metastatic cancer and in the 30 patients with lymph-node metastasis, but was >90 in the 14 patients with different histological types of MPLC. Therefore, a quantitative differentially-expressed gene mathematical model was established as follows: Sum of the differential expression ratios = p16T1 − T + p27T1 − T2 + C-erbB2T1 − T2 + p53T1 − T2, where T1 is the primary cancer and T2 is the lymph node metastasis, metastatic cancer or the second separate cancer. The quantitative differentially-expressed gene mathematical model is considered to be a useful tool for distinguishing between MPLC and IPM.

Diagnostic model of anterior open bite--a new way to predict skeletal type: a cross-sectional study.

The aim of this study was to determine a differential diagnostic model for anterior open bite (AOB) in children in order to predict evolution with growth in cases of skeletal or dysfunctional dysplasia. The sample was composed of 128 subjects divided into 2 groups according to overbite values: a control group with normal occlusion and 2.5 ± 1mm overbite, and an AOB group with a negative overbite. Measurements of hyoid bone position, gonial angle, SN/Go-Gn, FMA, height of alveolar processes, upper lip height, height of the anterior nasal spine to stomion superior, upper incisor and menton, antegonial notch, symphysis anatomy, condyle anatomy, convexity, facial axis, lower oropharyngeal area and curve of Spee were obtained for all subjects, based on lateral cephalometric radiographs and lateral photos. All data were analyzed, and multivariate logistic regression was applied. The area under the receiver operating characteristic (ROC) curve was calculated for the models obtained by logistic regression, in order to evaluate them. Statistical analysis was performed in IBM(®) SPSS(®) Statistics version 21.0. The results demonstrated that the AOB model was capable of predicting skeletal anterior open bite with an accuracy of 93%. The seven variables selected were: lower face height (LFH), height of mandibular alveolar process, antegonial notch, symphysis direction, symphysis thickness, gonial angle and facial axis. Measurements of facial morphology made it possible to construct a model able to differentiate, with a small margin of error, between the two distinct forms of AOB: skeletal and dysfunctional.

Validation of a differential diagnostic model of diabetic nephropathy and non‐diabetic renal diseases and the establishment of a new diagnostic model 糖尿病肾病和非糖尿病性肾脏疾病的鉴别诊断模型的验证及新模型的建立

The aims of the present study were to validate the differential diagnostic model of diabetic nephropathy (DN) and non-diabetic renal diseases (NDRD) established in 2003 and to establish a new diagnostic model suitable for the current clinical characteristics of DN. We examined 200 patients with Type 2 diabetes who underwent kidney biopsy from 2004 to 2012. The 2003 differential diagnostic model based on the data collected from 1993 to 2003 was evaluated by the diagnostic test and changes in the clinical differentiation parameters of DN and NDRD were analyzed. Logistic regression, receiver operating characteristics (ROC) curve, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) analysis were applied. The 2003 diagnostic model showed an accuracy of 77.5%. A significantly elevated incidence of hematuria, longer history of diabetes, and reduced level of glycated hemoglobin (HbA1c) were observed in the DN group from 2004 to 2012 compared with DN group from 1993 to 2003. Histories of diabetes mellitus (Dm), systolic blood pressure (Bp), HbA1c (Gh), hematuria (Hu), diabetic retinopathy (Dr), and hemoglobin (Hb) are independently related to DN. Thus, a new diagnostic model was constructed as follows: PDN = exp (0.846 + 0.022 Dm + 0.033Bp + 2.050 Gh-2.664 Hu-0.078 Hb + 2.942Dr)/[1 + exp (0.846 + 0.022 Dm + 0.033 Bp + 2.050 Gh-2.664 Hu-0.078 Hb + 2.942 Dr)].Validation tests determined that the accuracy of the new model were 90.9%. Changes in people with DN, clinical characteristics have reduced the diagnostic efficacy of the 2003 diagnostic model. The newly established model can provide a better, more current differentiation between DN and NDRD.

The differential diagnostic model for serous peptidomics in HBV carriers established by MALDI-TOF-MS analysis

ObjectivesHepatitis B virus (HBV) can result in asymptomatic carrier (AsC) state or chronic inflammation of liver, which depends on the host immunity. We therefore investigated the peptidomic profiling in the process of HBV infection. Design and methodsIn this study, serum from 116 HBV infected (AsC and chronic hepatitis), 60 HBV-immunized and 70 normal subjects was treated with MB-WCX (weak cation exchange based magnetic beads) kits and analyzed by the Clinprot/Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry (MALDI-TOF-MS) techniques. Purified serous proteins were subjected to FT-ICR-MS analysis, and Western blot further confirmed the results. ResultsThe specific model comprised of two peptides m/z 2882.89 and 4476.12 could distinguish HBV infected from healthy (HBV-immunized and normal) group and showed 95.5% of the sensitivity and 95.4% of the specificity by cross-validation analysis. 40/56 HBV infected and 43/50 healthy subjects could be correctly classified by the model. The area under the receiving operating curves (AUROC) of m/z 2882.89 and 4476.12, identified as subunits of fibrinogen beta chain (FBG) Bβ10–42 and nucleophosmin (NPM) respectively, were both up to 0.88 when discriminating AsC from the healthy group. The expression of Bβ10–42 and NPM decreased significantly in the plasma of HBV infected individuals by Western blot analysis. ConclusionsThere were specific serum peptide profilings for host responses to HBV infection, and m/z 2882.89 and 4476.12 could be valuable follow-up and prognostic tools for HBV infection.

A Study on the Differential Diagnostic Model for Newborn Hearing Loss Screening

A Study on the Differential Diagnostic Model for Newborn Hearing Loss Screening

A novel differential diagnostic model based on multiple biological parameters for immunoglobulin A nephropathy

BackgroundImmunoglobulin A nephropathy (IgAN) is the most common form of glomerulonephritis in China. An accurate diagnosis of IgAN is dependent on renal biopsies, and there is lack of non-invasive and practical classification methods for discriminating IgAN from other primary kidney diseases. The objective of this study was to develop a classification model for the auxiliary diagnosis of IgAN using multiparameter analysis with various biological parameters.MethodsTo establish an optimal classification model, 121 cases (58 IgAN vs. 63 non-IgAN) were recruited and statistically analyzed. The model was then validated in another 180 cases.ResultsOf the 57 biological parameters, there were 16 parameters that were significantly different (P < 0.05) between IgAN and non-IgAN. The combination of fibrinogen, serum immunoglobulin A level, and manifestation was found to be significant in predicting IgAN. The validation accuracies of the logistic regression and discriminant analysis models were 77.5 and 77.0%, respectively at a predictive probability cut-off of 0.5, and 81.1 and 79.9%, respectively, at a predictive probability cut-off of 0.40. When the predicted probability of the equation containing the combination of fibrinogen, serum IgA level, and manifestation was more than 0.59, a patient had at least an 85.0% probability of having IgAN. When the predicted probability was lower than 0.26, a patient had at least an 88.5% probability of having non-IgAN. The results of the net reclassification improvement certificated serum Immunoglobulin A and fibrinogen had classification power for discriminating IgAN from non-IgAN.ConclusionsThese models possess potential clinical applications in distinguishing IgAN from other primary kidney diseases.

Validation of nursing diagnoses - the Differential Diagnostic Validation Model as a strategy

ABSTRACT Objective: to analyze the contribution and limits of the use of Richard Fehring’s differential diagnostic validation model , as a methodological strategy in the validation of diagnoses . Method: theoretical review about the differential diagnostic validation model for the validation of NANDA-I nursing diagnoses. Results: the nursing diagnosis constitutes the step that precedes and promotes the appropriate nursing intervention . The correct identification of the diagnosis is fundamental for effective interventions . The presentation of a structured classification of diagnoses, such as for example the taxonomy II of NANDA-I, facilitates the clinical judgment of the nurse. It is necessary that the diagnoses are clear and that the defining characteristics are specific, and the validation studies contribute to this objective. Conclusions: the differential diagnostic validation model permits the validation of differences between diagnoses with very similar meanings; however, it is little known and applied in the validation studies. It is a methodology that can be useful for the accuracy of diagnoses and for contribution to the current discussion of taxonomy II and its new proposals. Descriptors: nursing; nursing diagnosis; validation studies. RESUMO Objetivo : analisar a contribuicao e limites no uso do modelo diferencial de diagnosticos de Richard Fehring, como estrategia metodologica na validacao de diagnosticos. Metodo: revisao teorica sobre o modelo diferencial para a validacao de diagnosticos de enfermagem da NANDA-I. Resultados: o diagnostico de enfermagem constitui a etapa que precede e favorece a intervencao adequada do enfermeiro. A identificacao correta do diagnostico e fundamental para que as intervencoes sejam eficazes. A apresentacao sistematizada dos diagnosticos em classificacoes, como por exemplo a taxonomia II da NANDA-I, facilita o julgamento clinico do enfermeiro. E necessario que os diagnosticos sejam claros e que suas caracteristicas definidoras sejam especificas, e os estudos de validacao contribuem para esse objetivo. Conclusao: o modelo de validacao diferencial permite validar as diferencas entre diagnosticos com significados muito semelhantes; contudo, esta pouco divulgado e aplicado nos estudos de validacao. E uma metodologia que pode ser util para a acuracia dos diagnosticos e para a contribuicao na discussao atual da taxonomia II e suas novas propostas. Descritores: enfermagem; diagnostico de enfermagem; estudos de validacao. RESUMEN Objetivo: analizar la contribucion y limites en el uso del modelo diferencial de diagnosticos de Richard Fehring, como estrategia metodologica en la validacion de los diagnosticos. Metodologia: revision teorica sobre el modelo diferencial para validacion de diagnosticos de enfermeria de NANDA-I. Resultados: el diagnostico enfermero es la etapa que precede y promueve la adecuada intervencion de la enfermera. La identificacion de un diagnostico correcto es fundamental para que las intervenciones sean de clasificacion efectiva. La presentacion estructurada de diagnosticos, tales como la taxonomia II de NANDA-I, facilita el juicio clinico de las enfermeras. Se espera que los diagnosticos sean claros y que sus caracteristicas definitorias sean especificas y son los estudios de validacion que contribuyen a este objetivo . Conclusiones: el modelo diferencial de validacion de diagnostico se utiliza para validar el diagnostico diferencial con las diferencias entre los significados muy similares, sin embargo, es poco conocido y aplicado en los estudios de validacion. Se trata de una metodologia que puede ser util para la exactitud del diagnostico y por su contribucion actual a la discusion de la II taxonomia y sus nuevas propuestas. Parte superior do formulario. Descriptores: diagnosticos de enfermeria; enfermeria; estudios de validacion.

A differential diagnostic model based on the morphological parameters of exfoliated cells for differentiating malignant pleural and peritoneal effusions from the benigns

A differential diagnostic model based on the morphological parameters of exfoliated cells for differentiating malignant pleural and peritoneal effusions from the benigns

A differential diagnostic model of diabetic nephropathy and non-diabetic renal diseases

Renal diseases in diabetes include diabetic nephropathies (DN) and non-diabetic renal diseases (NDRD). The clinical differentiation between these two categories is usually not so clear and effective. This study aims to develop a quantified differential diagnostic model. We consecutively screened the diabetic patients with overt proteinuria but no severe renal failure for kidney biopsy from 1993 to 2003. The finally enrolled 110 patients were divided into two groups according to pathological features (60 in DN group and 50 in NDRD group). Clinical and laboratory data were compared between two groups. Then a diagnostic model was developed based on the logistic regression analysis. Forty-six percent of patients were NDRD including a variety of pathological types. Many differences between DN and NDRD were found by comparison of the clinical indices. In the final logistic regression analysis, only diabetes duration (Dm), systolic blood pressure (Bp), HbA1c (Gh), haematuria (Hu) and diabetic retinopathy (Dr) showed statistical significance. Based on the logistic regression model: pi = e(z)/(1 + e(z)), a diagnostic model was constructed as follows: P(DN) = exp(-13.5922 + 0.0371Dm + 0.0395Bp + 0.3224Gh - 4.4552Hu + 2.9613Dr)/ [1 + exp(-13.5922 + 0.0371Dm + 0.0395Bp + 0.3224Gh - 4.4552Hu + 2.9613Dr)]. P(DN) was the probability of DN diagnosis (P(DN) >or= 0.5 as DN, P(DN) < 0.5 as NDRD). Validation tests showed that this model had good sensitivity (90%) and specificity (92%). This diagnostic model may be helpful to clinical differentiation of DN and NDRD in type 2 diabetic patients with overt proteinuria.

ARモデルを応用した病理的振戦鑑別診断支援システムの基礎研究

人の前腕の振戦加速度データに対するAR (autoregressive)モデルの適用が,パーキンソン病と類縁疾患との鑑別診断に有効であるかどうかを検討した.ARモデルの次数は,赤池に従って, FPE (final prediction error)を最小にする7に設定した.対象とした被験者は,パーキンソン病患者18名,本態性振戦患者20名,およびコントロールとして健常高齢者13名である.その結果,振戦疾患患者と健常高齢者の振戦については,実軸上に存在する特性根の符号,及び利き腕の振戦の基本共振周波数における周波数帯域幅に高度な有意差があり,パーキンソン病患者と本態性振戦患者の振戦については,利き腕の振戦の基本共振周波数における周波数帯域幅の分散に有意差があったので報告する.