Imperfect Diagnosis Research Articles

Rare liver diseases are not rare in China.

Rare diseases, also known as orphan diseases, are a group of diseases that affect a relatively small number of people. Low incidence rates, atypical symptoms, imperfect diagnosis criteria and few treatment options impose medical, psychological and financial stress on the local healthcare system. The spectrum of liver diseases in China has changed in the past decades due to successful control of once highly prevalent viral hepatitis B and C. Furthermore, the increased awareness and improved availability of specific laboratory tests have also facilitated the diagnosis of rare diseases such as autoimmune, cholestatic and genetic liver diseases. Finally, considering the huge population, the total number of many rare liver diseases in China is not as rare as once deemed. In this mini-review article, we will outline the current clinical and epidemiological profiles of some rare liver diseases that are no longer rare in China.

Imperfect diagnosis: The truncated legacies of Zika testing.

When the Zika virus burst onto the international scene in the second half of 2015, the development of diagnostic tools was seen as an urgent global health priority. Diagnostic capacity was restricted to a small number of reference laboratories, and none of the few available molecular or serological tests had been validated for extensive use in an outbreak setting. In the early weeks of the crisis, key funders stepped in to accelerate research and development efforts, and the WHO took responsibility for steering diagnostic standardization, a role it had successfully played during the West Africa Ebola virus outbreak. Yet when the WHO declared the end of the Zika Public Health Emergency of International Concern in November 2016, diagnostic capacity remained patchy, and few tools were available at the scale required in the countries that bore the brunt of the epidemic, particularly Brazil. This article analyses the limited impact of global R&D efforts on the availability of Zika diagnostic options where they were most needed and for those most vulnerable: women who might have been exposed to the virus during their pregnancy and children born with suspected congenital Zika syndrome. The truncated legacies of testing during the Zika crisis reveal some of the fault lines in the global health enterprise, particularly the limits of ‘emergency R&D’ to operate in geopolitical contexts that do not conform to the ideal type of a humanitarian crisis, or to tackle technical issues that are inextricably linked to domestic struggles over the scope and distribution of biological citizenship. Diagnostic shortcomings, we argue, lie at the heart of the stunning transformation, in less than two years, in the status of Zika: from international public health emergency to neglected disease.

Analysis on Space accessibility of Medical Service Facilities in Central Wuhan Based on Improvement of 2SFCA

Aiming at the current imperfect hierarchical diagnosis and treatment system and the unreasonable spatial distribution of medical facilities. This article introduces 2SFCA model improved by the Gaussian distance decay function and multiple stage radius to evaluate the accessibility of medical facilities in Wuhan. The results indicate that: firstly, compared with the traditional 2SFCA model, the improved 2SFCA model is more sensitive to the identification of spatial accessibility, and the transition effect of the identification results is more obvious, which can accurately reflect the spatial distribution characteristics of the accessibility of medical facilities in Wuhan. Secondly, the distribution of medical facilities in Wuhan is obvious and the main factors that affect the accessibility value in different urban areas are quite different. Thirdly, the high value areas of medical facilities in the main urban area of Wuhan are mainly concentrated within the second ring line, the transition effect between the second and third ring lines is obvious, and the low value areas are mainly concentrated outside the third ring line. In the end, the main reasons for the low accessibility of the area outside the Third Ring Road are the low service quantity and low service ability of the community health service institutions and the high traffic resistance. The research results can provide a reference for the layout and planning of medical institutions in the main urban area of Wuhan.

Errors in the management of patients with Alzheimer's disease: analysis of problems and ways of their solution

Epidemiological data show that Alzheimer's disease (AD) is the most common cause of acquired cognitive impairment (CI). At the same time, according to statistics, vascular CI and vascular dementia predominate in Russia, which is mainly due to imperfect diagnosis, when any pathological condition associated with cerebral dysfunction in a patient with vascular risk factors is interpreted as dyscirculatory encephalopathy or chronic brain ischemia. However, this can be asthenoneurotic syndrome, migraine, vestibular dysfunction, and a number of neurodegenerative diseases, the most common condition of which is certainly AD. What is more, the treatment of age-related diseases, with the exception of acute vascular disease, is receiving manifestly inadequate attention. All this leads to the lack of a unified methodology for the management of these patients, to the impossibility to have adequate primary medical care, to the low detection rate of CI, to the prescription of drugs without appropriate indications, and to the denial of psychological correction methods. The review highlights the challenges facing the management of patients with AD and the possible ways of their solution.

Machine diagnostic service centre design under imperfect diagnosis with uncertain error cost consideration

As modern machines are always highly customised, it is important to diagnose the specific maintenance requirement of each machine before performing regular maintenance tasks. However, the diagnosis cannot always be accurate. In this study, we focus on a diagnostic service design problem considering imperfect diagnosis with uncertain error cost, which increases in the inaccuracy of the diagnosis. The service system is modelled as a multiple server queue, with servers performing a sequential diagnosis and customers deciding whether or not to use the service. We consider the case where the expert skill level is exogenous, uncertain and endogenous, respectively. The results suggests that (1) when the expert skill level is exogenous, the congestion of the system increases in expert skill level. In addition, the error costs for the two major stakeholders-the service centre and the customer-may affect the optimal service time and number of experts in different ways; (2) when expert skill level becomes more uncertain, the service centre should improve the optimal service time further and will see a further erosion of the profit; and (3) different from the exogenous expert skill level, high skill level experts always accompany with long service time when expert skill level is endogenous.

Contribution of Ambulatory Blood Pressure Monitoring to the Management of Arterial Hypertension at Blaise Compaoré University Hospital

Background: Hypertension is a major public health concern in Burkina Faso. Its management relies on in-office medical setting blood pressure monitoring which is known to be an imperfect diagnosis tool. Objective: This study aims to assess the contribution of ambulatory blood pressure monitoring in the management of hypertension at Blaise Compaore University Hospital. Methods: A monocentric descriptive retrospective study was conducted in the cardiology outpatient unit of Blaise Compaore University Hospital. Patients aged at least 18 years who underwent ambulatory blood pressure monitoring between March 2013 and June 2015 were enrolled. Hypertension was defined as follows: average blood pressure over 24 hours > 130/80 mmHg, or diurnal blood pressure > 135/85 mmHg, or nocturnal blood pressure >120/70 mmHg. Data were analyzed using the SPSS 20.0 software. The threshold for significance was set at 5% for a validity interval at 95%. Chi square test was used for the statistical analysis. Results: A total of 122 patients were enrolled. The main reasons for ambulatory monitoring were the evaluation of hypertensive therapy (51.6%) and the diagnosis confirmation (39.3%). The results of the ambulatory monitoring found 61.4% of the patients with hypertension and 37.7% with normal blood pressure. A treatment adjustment was made for 36.1% of the patients; an initiation of antihypertensive therapy was undertaken for 24.6%, an abstention from drug therapy was recommended for 23.8%, and the pursuit of previous treatment for 13.1% of the patients. Conclusion: Ambulatory blood pressure monitoring should be used more often in order to optimize the management of hypertension in our current practice.

A Focus on Medication Error

A medication error is any avoidable adverse incident that happens due to negligence or incorrect administration of the medication to the patients that lead to an erroneous or imperfect diagnosis of a disease, injury, syndrome, behaviour, infection or other illness. The cause of medication error can be associated to healthcare professionals’ careless practice, outdated health care products, improper follow-ups of the procedures and systems. There are various factors involved that promote such events to happen, for example, an error in dosage or route of administration, incorrect prescription, problem in reading handwritten prescription, failure to administer the correct formulation for a particular disease, use of expired drugs, incorrect timing for administration of the drug, lack of awareness of adverse effects of certain drug combinations, shortage of information about a patient's drug allergies or sensitivities, problem with product labelling, packaging, compounding, dispensing, distribution, monitoring, administration and nomenclature, that is, confusion about different drugs with similar names.

Sepsis mandates: improving inpatient care while advancing quality improvement.

Sepsis mandates: improving inpatient care while advancing quality improvement.

Adenocarcinoma with Minimal Deviation of the Cervix: What Management?

The cervix adenocarcinoma results in an average 15% of cervical carcinomas. It is most often associated with infection with HPV type 16 or 18. Screening has its particularities compared to squamous cell carcinoma. The minimal deviation adenocarcinoma is a rare histological entity of cervix adenocarcinoma. Cervical cytology provides an imperfect diagnosis of glandular cells compared to squamous lesions, although the 2001 Bethesda system still allows a better management of patients with abnormal glandular cells; the diagnosis is based on histological study. We report a case of 46 years-old woman that was treated for minimal deviation adenocarcinoma of the cervix. It is likely that in future, the screening either cytological or viral must be completed by a specific molecular marker of these lesions.

Analysis of bivariate binary data with possible chances of wrong ascertainment

In familial data, ascertainment correction is often necessary to decipher genetic bases of complex human diseases. This is because families usually are not drawn at random or are not selected according to well-defined rules. While there has been much progress in identifying genes associated with a certain phenotype, little attention has been paid so far for familial studies on exploring common genetic influences on different phenotypes of interest. In this study, we develop a powerful bivariate analytical approach that can be used for a complex situation with paired binary traits. In addition, our model has been framed to accommodate the possibility of imperfect diagnosis as traits may be wrongly observed. Thus, the primary focus is to see whether a particular gene jointly influences both phenotypes. We examine the plausibility of this theory in a sample of families ascertained on the basis of at least one affected individual. We propose a bivariate binary mixed model that provides a novel and flexible way to account for wrong ascertainment in families collected with multiple cases. A hierarchical Bayesian analysis using Markov Chain Monte Carlo (MCMC) method has been carried out to investigate the effect of covariates on the disease status. Results based on simulated data indicate that estimates of the parameters are biased when classification errors and/or ascertainment are ignored.

Genetic parameters for somatic cell score according to udder infection status in Valle del Belice dairy sheep and impact of imperfect diagnosis of infection

BackgroundSomatic cell score (SCS) has been promoted as a selection criterion to improve mastitis resistance. However, SCS from healthy and infected animals may be considered as separate traits. Moreover, imperfect sensitivity and specificity could influence animals' classification and impact on estimated variance components. This study was aimed at: (1) estimating the heritability of bacteria negative SCS, bacteria positive SCS, and infection status, (2) estimating phenotypic and genetic correlations between bacteria negative and bacteria positive SCS, and the genetic correlation between bacteria negative SCS and infection status, and (3) evaluating the impact of imperfect diagnosis of infection on variance component estimates.MethodsData on SCS and udder infection status for 1,120 ewes were collected from four Valle del Belice flocks. The pedigree file included 1,603 animals. The SCS dataset was split according to whether animals were infected or not at the time of sampling. A repeatability test-day animal model was used to estimate genetic parameters for SCS traits and the heritability of infection status. The genetic correlation between bacteria negative SCS and infection status was estimated using an MCMC threshold model, implemented by Gibbs Sampling.ResultsThe heritability was 0.10 for bacteria negative SCS, 0.03 for bacteria positive SCS, and 0.09 for infection status, on the liability scale. The genetic correlation between bacteria negative and bacteria positive SCS was 0.62, suggesting that they may be genetically different traits. The genetic correlation between bacteria negative SCS and infection status was 0.51. We demonstrate that imperfect diagnosis of infection leads to underestimation of differences between bacteria negative and bacteria positive SCS, and we derive formulae to predict impacts on estimated genetic parameters.ConclusionsThe results suggest that bacteria negative and bacteria positive SCS are genetically different traits. A positive genetic correlation between bacteria negative SCS and liability to infection was found, suggesting that the approach of selecting animals for decreased SCS should help to reduce mastitis prevalence. However, the results show that imperfect diagnosis of infection has an impact on estimated genetic parameters, which may reduce the efficiency of selection strategies aiming at distinguishing between bacteria negative and bacteria positive SCS.

The imperfect diagnosis of acute respiratory distress syndrome*

Medical-Surgical Intensive Care Unit, San Francisco General Hospital, University of California, San Francisco, California The author has no financial interests to disclose.

The impact of inaccurate reporting of condom use and imperfect diagnosis of sexually transmitted disease infection in studies of condom effectiveness: a simulation-based assessment.

The effectiveness of condoms in reducing sexually transmitted disease (STD) infection risk has been debated in the face of equivocal epidemiologic evidence. We assessed the potential magnitude of bias in condom effectiveness studies resulting from inaccurate reporting of condom use behavior and misdiagnosis of STD infection status. The goal of this study was to illustrate the magnitude of bias in condom effectiveness studies in the presence of inaccurate condom use reporting and diagnostic misclassification. We used probabilistic simulations to mimic plausible outcomes for hypothetical prospective and retrospective condom effectiveness studies subject to both inaccurate reporting of the participants' true condom use and diagnostic error. The simulations were conducted by generating a series of binomial (yes, no) random variables corresponding with STD infection status and accurate diagnosis of infection. The simulation results illustrate that failure to address reporting and diagnostic errors can lead to a substantial bias in studies of condom effectiveness. This bias resulted in a roughly 25% to 30% reduction in the probability of detecting a true 2-fold reduction of infection risk resulting from using condoms. Inaccurate reporting of condom use and reliance on imperfect diagnostic tests can substantially bias observable measures of condom effectiveness. The potential for these biases should be addressed in the design and analysis of effectiveness studies.

Partially Supervised Learning Using an EM‐Boosting Algorithm

Training data in a supervised learning problem consist of the class label and its potential predictors for a set of observations. Constructing effective classifiers from training data is the goal of supervised learning. In biomedical sciences and other scientific applications, class labels may be subject to errors. We consider a setting where there are two classes but observations with labels corresponding to one of the classes may in fact be mislabeled. The application concerns the use of protein mass-spectrometry data to discriminate between serum samples from cancer and noncancer patients. The patients in the training set are classified on the basis of tissue biopsy. Although biopsy is 100% specific in the sense that a tissue that shows itself to have malignant cells is certainly cancer, it is less than 100% sensitive. Reference gold standards that are subject to this special type of misclassification due to imperfect diagnosis certainty arise in many fields. We consider the development of a supervised learning algorithm under these conditions and refer to it as partially supervised learning. Boosting is a supervised learning algorithm geared toward high-dimensional predictor data, such as those generated in protein mass-spectrometry. We propose a modification of the boosting algorithm for partially supervised learning. The proposal is to view the true class membership of the samples that are labeled with the error-prone class label as missing data, and apply an algorithm related to the EM algorithm for minimization of a loss function. To assess the usefulness of the proposed method, we artificially mislabeled a subset of samples and applied the original and EM-modified boosting (EM-Boost) algorithms for comparison. Notable improvements in misclassification rates are observed with EM-Boost.

Stabilization of Active Fault Tolerant Control Systems with Imperfect Fault Detection and Diagnosis

In active Fault Tolerant Control Systems (FTCSs), a Fault Detection and Diagnosis (FDD) algorithm is used to monitor system performance, detect the occurrence of faults and estimate post-fault system parameters. Usually, estimation techniques employed by the FDD algorithm will provide system parameters with some inaccuracies. As a result, the system to be controlled will include parameter uncertainties. In this paper, exponential stabilization of FTCSs for systems with time-varying unknown-but-bounded parameter uncertainties is considered. In particular, an algorithm that provides a necessary and sufficient condition for exponential stabilization in the mean square is derived. The stabilizing controller is calculated, based on the solutions of matrix Riccati-like algebraic equations. Several interesting special cases are derived from the general result. A numerical example is presented to demonstrate the theoretical developments.

Methodological Problems of Clinical Trials in Multi-Infarct Dementia

In about 30% of the cases suffering from MID an unequivocal diagnosis can be made only on the basis of histopathological evidence. The imperfect diagnosis has a relevant impact on the methodology to be followed in therapeutic trial. The following points have to be considered in a therapeutic trial: (1) distribution of the patients expected to come to the observation, (2) the expected effectiveness of the treatment, (3) the likelihood of obtaining statistical significant results and (4) the required size of the study. The diagnostic inaccuracy gives rise to a decrease in the chance of yielding a statistically significant result and on the other hand produces an increase in the required number of subjects to be submitted to the trial.

A Statistical Evaluation of Recurrent Medical Examinations

In this paper we consider the efficiency of different policies for scheduling medical examinations in a search for diseases like cancer, which start without overt symptoms. We treat both periodic and random examinations and allow for imperfect diagnosis depending on how long the disease has been present. The results of our theory are then checked against a rough data fit to the incidence curve of cervical cancer.