Disease Onset Time Research Articles

Predicting disease Risk by Transformation Models in the Presence of Unspecified Subgroup Membership.

Some biomedical studies lead to mixture data. When a discrete covariate defining subgroup membership is missing for some of the subjects in a study, the distribution of the outcome follows a mixture distribution of the subgroup-specific distributions. Taking into account the uncertain distribution of the group membership and the covariates, we model the relation between the disease onset time and the covariates through transformation models in each sub-population, and develop a nonparametric maximum likelihood based estimation implemented through EM algorithm along with its inference procedure. We further propose methods to identify the covariates that have different effects or common effects in distinct populations, which enables parsimonious modeling and better understanding of the difference across populations. The methods are illustrated through extensive simulation studies and a real data example.

Obciążenie chorobami układu sercowo-naczyniowego u osób hospitalizowanych w szpitalu w Guangzhou w Chinach, w latach 2006–2015

Introduction. To explore the epidemiological characters of cardiovascular disease (CVD) in patients and to give a certain clues for disease prevention and clinical treatment. Material and methods. We carried out an investigation using retrospective study method by the medical records retrieval system, and extracted the data of inpatients suffered from CVD in a hospital in Guangzhou from 2006 to 2015, including age, gender, disease onset time, clinical diagnosis, hospitalization days, cost and so on. Results. The average admissions per 5 years kept increasing in the study period. Most inpatients suffered from the CVD in December (n = 9288, 9.10%), while least in February (n = 7309, 7.16%). Most of the inpatients were 66–75-years- -old (n = 24,891, 24.37%). Gender ratio (male vs. female) of CVD inpatients showed a downward trend came with age. The gender ratio was 1.9 and 0.99 among inpatients below 26-years-old and above 85-years-old, respectively. Most common CVD were ischemic heart diseases (29.46%), cerebrovascular diseases (21.59%) and hypertensive diseases (15.18%). The adjusted hospitalization cost was 4600.30 USD in 2006 and 5473.66 USD in 2006, while the average hospitalization days were 15.63 in 2006 and 9.98 in 2015. Conclusions. More attention should be paid to the middle and elderly people with CVD risk factors, especially the senile women, to reduce the disease burden. The change of gender ratio suggested there may be other significant causes leading to the gender differences besides estrogen.

Estimating Disease Onset Time by Modeling Lab Result Trajectories via Bayes Networks.

The true onset time of a disease, particularly slow-onset diseases like Type 2 diabetes mellitus (T2DM), is rarely observable in electronic health records (EHRs). However, it is critical for analysis of time to events and for studying sequences of diseases. The aim of this study is to demonstrate a method for estimating the onset time of such diseases from intermittently observable laboratory results in the specific context of T2DM. A retrospective observational study design is used. A cohort of 5,874 non-diabetic patients from a large healthcare system in the Upper Midwest United States was constructed with a three-year follow-up period. The HbA1c level of each patient was collected from earliest and the latest follow-up. We modeled the patients' HbA1c trajectories through Bayesian networks to estimate the onset time of diabetes. Due to non-random censoring and interventions unobservable from EHR data (such as lifestyle changes), naïve modeling of HbA1c through linear regression or modeling time-to-event through proportional hazard model leads to a clinically infeasible model with no or limited ability to predict the onset time of diabetes. Our model is consistent with clinical knowledge and estimated the onset of diabetes with less than a six-month error for almost half the patients for whom the onset time could be clinically ascertained. To our knowledge, this is the first study of modeling long-term HbA1c progression in non-diabetic patients and estimating the onset time of diabetes.

Parkinson's disease and risk of brain tumor: a Meta-analysis

Objective To assess the correlation between Parkinson's disease (PD) and brain tumor. Methods Taking Parkinson's disease, tumor, cancer in Chinese, and PD, Parkinson's disease, tumor, cancer, neoplasm in English as the key words, prospective cohort studies and case -control studies on relation between PD and brain tumor were searched by using PubMed, Web of Science, EMBASE/SCOPUS, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Data, and VIP database from January 1965 to July 2016, assisted by manual searching. Quality assessment and Mete-analysis were made by using Newcastle-Ottawa Scale (NOS) and Stata 12.1 software. Results A total of 11 studies with a total number of 350 632 PD patients were included in the overall analysis after excluding duplicate ones and those which did not meet the inclusion criteria from 1832 articles. Meta-analysis showed that compared with healthy people, PD patients had an increased risk of brain tumor ( OR = 1.370, 95%CI: 1.120-1.690; P = 0.003), and the result was consistent after excluding 2 low-quality articles ( OR = 1.360, 95% CI: 1.080-1.720; P = 0.008). Further stratified analyses according to disease onset time and regional difference showed that brain tumor occurrence in PD patients was significantly higher than healthy people only after the diagnosis of PD ( OR = 1.430, 95% CI: 1.120-1.830; P = 0.004). The geographical subgroup analyses showed a higher risk of brain tumor among PD patients in Europe ( OR = 1.420, 95%CI: 1.290-1.560; P = 0.000) and Taiwan area of China in Asia ( OR = 2.590, 95% CI: 1.730-3.880; P = 0.000) compared with healthy people. Funnel plot, Begg test ( P = 0.583) and Egger test ( P = 0.985) showed there was no bias. Conclusions PD patients have a higher risk of brain tumor. DOI: 10.3969/j.issn.1672-6731.2017.01.006

The influence of foreign body surface area on the outcome of chronic osteomyelitis

Reproducible animal models of osteomyelitis close to the clinical scenario are difficult to obtain as the animals either die shortly after inoculation of bacteria or the bone cures itself of infection. Additional materials used as foreign bodies offer increased chances for localized infection due to bacterial attachment and are closer to clinical pathology.Through in vivo experimentation we investigated here the influence of surface area of a series of foreign bodies on the final outcome of the animal model, in terms of reproducibility, survival rate and time necessary for onset of chronic disease. Stainless steel Kirschner wire segments, stainless steel balls and cotton meshes were employed for this purpose.The clinical, microbiological, radiological and histological results obtained were compared with the simple case where no foreign body was used. The follow-up period was 57days. The cotton meshes, which had the highest surface area, were observed to provide the best outcome, with the lowest disease onset time interval (of 1week earlier than the others), the highest survival (of 90%) and disease reproduction rate (90%). The only clinical pattern of the mesh group rabbits was short lived inflammation while the other rabbits presented also some other clinical signs such as rhinorrheas, abscesses, rush and/or dyspnea. Moreover, this model is the most suitable for further treatment studies, as the cotton meshes could be easily removed after disease onset, without any intervention on the bone. This is important, as the treatment would address the bacteria present within the bone parts (marrow, cortex, periosteum etc.) not those forming the biofilm.

Meta-analysis of the relationship between crop yield and soybean rust severity.

Meta-analytic models were used to summarize and assess the heterogeneity in the relationship between soybean yield (Y, kg/ha) and rust severity (S, %) data from uniform fungicide trials (study, k) conducted over nine growing seasons in Brazil. For each selected study, correlation (k=231) and regression (k=210) analysis for the Y-S relationship were conducted and three effect-sizes were obtained from these analysis: Fisher's transformation of the Pearson's correlation coefficient (Zr) and the intercept (β0) and slope (β1) coefficients. These effect-sizes were summarized through random-effect and mixed-effect models, with the latter incorporating study-specific categorical moderators such as disease onset time (DOT) (<R1 or ≥R1 reproductive crop stage), disease pressure (DP) (high=>70%, moderate=>40 and ≤70%, and low=≤40% S the check treatment), and growing season. The overall mean for r- (back-transformed Z-r) was -0.61, based on the random-effects model. DOT and DP explained 14 and 25%, respectively, of the variability in Z-r. Stronger associations (r-=-0.87 and -0.90) were estimated by mixed-effects models for the Zr data from studies with highest DP (DP>70%) and earliest rust onset (DOT<R1), respectively. Overall means (based on a random-effect model) for the regression coefficients β-0 and β-1 were 2,977 and 18 kg/ha/%(-1), respectively. In other words, S as low as 3% would reduce 60 kg/ha for an expected Y of 3,000 kg/ha. In relative terms, each unitary percent increase in S would lead to a 0.6 percentage point (pp) reduction in Y. The three categorical moderator variables explained some (5 to 10%) of the heterogeneity in β-1 but not in β-0 The estimated relative reduction in Y was 0.41 to 0.79 pp/%(-1) across seasons. Highest relative yield reductions (>0.73 pp/%(-1)) were estimated for studies with DOT<R1 and DP>70%; the latter possibly due to high fungicide efficacy when DP is low, thus leading to higher yield differences between fungicide-protected and nontreated plots. The critical-point meta-analytic models can provide general estimates of yield loss based on a composite measure of disease severity. They can also be useful for crop loss assessments and economic analysis under scenarios of varying DOT and weather favorableness for epidemic development.

A Mixed-Effects Model with Time Reparametrization for Longitudinal Univariate Manifold-Valued Data.

Mixed-effects models provide a rich theoretical framework for the analysis of longitudinal data. However, when used to analyze or predict the progression of a neurodegenerative disease such as Alzheimer's disease, these models usually do not take into account the fact that subjects may be at different stages of disease progression and the interpretation of the model may depend on some implicit reference time. In this paper, we propose a generative statistical model for longitudinal data, described in a univariate Riemannian manifold setting, which estimates an average disease progression model, subject-specific time shifts and acceleration factors. The time shifts account for variability in age at disease-onset time. The acceleration factors account for variability in speed of disease progression. For a given individual, the estimated time shift and acceleration factor define an affine reparametrization of the average disease progression model. This statistical model has been used to analyze neuropsychological assessments scores and cortical thickness measurements from the Alzheimer's Disease Neuroimaging Initiative database. The numerical results showed that we can distinguish between slow versus fast progressing and early versus late-onset individuals.

Modeling Alzheimer's Disease Progression Using Disease Onset Time and Disease Trajectory Concepts Applied to CDR‐SOB Scores From ADNI

Disease-onset time (DOT) and disease trajectory concepts were applied to derive an Alzheimer's disease (AD) progression population model using the clinical dementia rating scale—sum of boxes (CDR-SOB) from the AD neuroimaging initiative (ADNI) database. The model enabled the estimation of a DOT and a disease trajectory for each patient. The model also allowed distinguishing fast and slow-progressing subpopulations according to the functional assessment questionnaire, normalized hippocampal volume, and CDR-SOB score at study entry. On the basis of these prognostic factors, 81% of the mild cognitive impairment (MCI) subjects could correctly be assigned to slow or fast progressers, and 77% of MCI to AD conversions could be predicted whereas the model described correctly 84% of the conversions. Finally, synchronization of the biomarker-time profiles on estimated individual DOT virtually expanded the population observation period from 3 to 8 years. DOT-disease trajectory model is a powerful approach that could be applied to many progressive diseases.

Summarizing Phenotype Evolution Patterns from Report Cases

The need to represent and manage time is implicit in several reasoning processes in medicine. However, this is predominantly obvious in the field of many neurodegenerative disorders, which are characterized by insidious onsets, progressive courses and variable combinations of clinical manifestations in each patient. Therefore, the availability of tools providing high level descriptions of the evolution of phenotype manifestations from patient data is crucial to promote early disease recognition and optimize the diagnostic process. Although many case reports published in the literature do not provide exhaustive temporal information except only key time references, such as disease onset, diagnosis or monitoring time, automatically comparing cases described by temporal clinical manifestation sequences can provide valuable knowledge about the data evolution. In this paper, we demonstrate the usefulness of representing patient case reports of a neurodegenerative disorder as a set of temporal clinical manifestations semantically annotated with a domain phenotype ontology and registered with a time-stamped value. Novel techniques are presented to query and match sets of different manifestation sequences from multiple patient cases, with the aim of automatically inferring phenotype evolution patterns of generic patients for clinical studies. The method was applied to 25 patient report cases from a Spanish study of the domain of cerebrotendinous xanthomatosis. Five evolution patterns were automatically generated to analyze the patient data. The results were evaluated against 49 relevant conclusions drawn from the study, with a precision of 93 % and a recall of 70 %.

Canine atopic dermatitis in the Middle East: clinical signs, signalment and common allergens

&amp;nbsp; The aim of the study was to examine for the first time the various aspects of canine atopic dermatitis (CAD) presenting in a Middle Eastern country. Medical records of 164 dogs diagnosed with CAD were evaluated. Associations between signalment, lifestyle, clinical signs and allergens were evaluated statistically. Labrador Retriever, German Shepherd dog, Boxer, French Bulldog, Golden Retriever and Shar-Pei breeds were presented more frequently than the regular hospital population (P &amp;lt; 0.0001), and had an earlier disease onset time (P &amp;lt; 0.01). In 22 dogs (13%) signs of CAD were noticed at less than six months of age. Most dogs (75%) lived indoors. Most dogs (83%) had lesions on the ventral part of the body and 68% had foot lesions. After excluding flea allergy dermatitis by implementing strict flea control, 60% of the dogs presented with dorsal distribution of skin lesions. Dogs with ventral lesions were younger when clinical signs first appeared (P &amp;lt; 0.05). Most of the dogs were allergic to dust and/or dust mites (75.6%), with weeds and trees as the next most common allergens. CAD is similar worldwide, but geographic differences may be attributable to genetic pools and allergen loading. This study also shows that early onset of clinical signs, especially in breeds predisposed to CAD and with a dorsal distribution of skin lesions, should not rule out the diagnosis of CAD. &amp;nbsp;

Comparison of familial and sporadic multiple sclerosis in Iraqi patients

Background: Multiple sclerosis (MS) is one of the increasing prevalent neurologic disorders. Epidemiologic and family studies implicate genetic and environmental factors in determiningsusceptibility to MS. The exact effect of the former is intended for investigation in our study.Objectives: The objective of the study is to compare the demographic features, clinical presenting features, and clinical course between familial and sporadic cases of MS.Materials and Methods: this is a retrospective cohort study conducted in Multiple Sclerosis Center in the Medical City in Baghdad. The records of the MS center in Baghdad Teaching Hospital were surveyed, and data from 13 patients with positive family history of MS, and 13 patients with out family history of MS was analyzed.Results: Regarding the clinical presentation, for those with family history of MS the common presenting symptoms were sensory symptoms and transverse myelitis, and those without family history of MS was pyramidal, for those with family history of MS 11 patients had initial course of relapsing remitting MS ( 84.6%) ,of them 4 patients progressed into secondary progressive MS (36.4%); 2 patients had primary progressive MS as initial course, for those with negative family history of MS 12 patients had initial course of relapsing remitting MS, of them 5 patients progressed into Secondary progressive MS (41.6%); 1 patients had primary progressive MS as initial course (7.7 %.). No significant difference was found in the investigated parameters, except for the inverse relation between age of onset and lag time to diagnosis.Conclusion: Familial MS do not significantly differ from sporadic MS in terms of the demographic patterns and clinical course and presentation. This is not the case for the relationship between the age of disease onset and lag time to diagnosis.

Reassessment of motor-behavioural test analyses enables the detection of early disease-onset in a transgenic mouse model of amyotrophic lateral sclerosis

As a model for amyotrophic lateral sclerosis (ALS), transgenic hSOD1 G93A mice constitute the standard tool for evaluating future therapeutic strategies. Due to axonal retraction from neuromuscular junctions, the animals suffer from muscle wasting leading to weakness and paralysis of the extremities, which in early stages can be detected by measuring weight loss. Suspecting that underlying mechanisms might yield subtle neuromuscular abnormalities ahead of weight loss onset, we wanted to determine a behavioural test to detect disease onset time earlier. We compared the monitoring of weight with the “forced” examination of grip strength and the investigation of freely behaving animals within an open field. Additionally, we compared two different data analysis methods: (1) two-way ANOVA with Bonferroni correction (2) break point analysis calculating symptom onset time points for each animal. Break point analysis revealed onset times that significantly preceded those obtained by standard two-way ANOVA. Open field analysis of freely moving animals could not give an advantage over weight loss measurements. Grip strength assessment of hindlimbs detected disease onset 36 days before the first evidence of weight loss, providing a maximal treatment window of 84 days on average before the death of male animals. We conclude that grip strength analysis of hindlimbs is a very sensitive and reproducible motor behavioural test, which can even be applied to small cohorts of animals. Combined with break point analysis, it represents the method of choice to detect early disease onset in hSOD1 G93A mice.

Osteoporose und Knochenfestigkeit: Gender-Aspekte

Although postmenopausal and elderly women are more frequently affected by osteoporosis, men are not protected from the disease. Age-related osteoporosis involves several gender-specific clinical aspects such as disease onset time and different dynamics of bone loss. Men benefit from larger bones and a time-delay of age-related changes in bone density and quality. Moreover, secondary osteoporosis is more common in males than in females. High-resolution peripheral quantitative computed tomography (HR-pQCT) and high-resolution magnetic resonance imaging (HR-MRI) represent novel research tools for a noninvasive quantification of bone microstructure which is of interest for musculoskeletal gender studies. For optimal design of such studies, researchers should be aware of technical pitfalls and site-specificity of bone microstructure.

Robust combination of multiple diagnostic tests for classifying censored event times

Recent advancement in technology promises to yield a multitude of tests for disease diagnosis and prognosis. When there are multiple sources of information available, it is often of interest to construct a composite score that can provide better classification accuracy than any individual measurement. In this paper, we consider robust procedures for optimally combining tests when test results are measured prior to disease onset and disease status evolves over time. To account for censoring of disease onset time, the most commonly used approach to combining tests to detect subsequent disease status is to fit a proportional hazards model (Cox, 1972) and use the estimated risk score. However, simulation studies suggested that such a risk score may have poor accuracy when the proportional hazards assumption fails. We propose the use of a nonparametric transformation model (Han, 1987) as a working model to derive an optimal composite score with theoretical justification. We demonstrate that the proposed score is the optimal score when the model holds and is optimal "on average" among linear scores even if the model fails. Time-dependent sensitivity, specificity, and receiver operating characteristic curve functions are used to quantify the accuracy of the resulting composite score. We provide consistent and asymptotically Gaussian estimators of these accuracy measures. A simple model-free resampling procedure is proposed to obtain all consistent variance estimators. We illustrate the new proposals with simulation studies and an analysis of a breast cancer gene expression data set.

Influence of feeding ratio and size on susceptibility to microsporidial gill disease caused by Loma salmonae in rainbow trout, Oncorhynchus mykiss (Walbaum)

Two trials were designed to quantify the effect of feeding ratio and fish size on the cohabitation transmission of Loma salmonae, the causative agent of microsporidial gill disease (MGD) in salmonids, Oncorhynchus spp. To evaluate the effect of feeding ratio on disease onset, groups of 45 rainbow trout, O. mykiss (Walbaum) (RBT), were fed daily at 1% (low), 2% (medium) or 4% (high) of the fish biomass in the tank. There were three tanks at each feeding level: two tanks were exposed to the pathogen and one was a control. For the second objective, 300 RBT were separated into seven tanks so that the weight classifications were small (17-23 g), medium (32-38 g) and large (57-63 g). Each size class was done in duplicate with one control tank containing medium-sized fish. Separately for each trial, on day 0 post-exposure (PE) five highly infectious RBT were added to each tank (not including the control tanks) to begin the cohabitation exposure period. Beginning on day 21 PE and continuing biweekly until days 70 and 77 PE for the feeding and fish size trials, respectively, each fish was evaluated for visible branchial xenomas to determine disease onset time. Using survival analysis, the survival curves for the low, medium and high feedings were not significantly different from each other. However, there were significant differences amongst the small, medium and large weight classes in the size trial. The median numbers of days to the development of branchial xenomas was 31, 38 and 42 for small, medium and large size fish, respectively. On any given day, a medium or large sized fish had a hazard ratio for developing branchial xenomas of 0.66 and 0.63, respectively, compared with a small fish. In addition to host species and host strain differences, fish size is now considered a host risk factor for the development of MGD.

Dietary fiber affects the onset of Clostridium difficile disease in hamsters

Clostridium difficile is a leading cause of antibiotic-associated diarrhea in adults. The infection is usually initiated after antibiotics perturb the colonic microecology. The treatment of severe disease is a course of vancomycin or metronidazole, but non-antibiotic modalities to help in disease prevention and treatment are needed. Four fiber supplements used by humans were tested in ampicillin treated hamsters and their influence on Clostridium difficile disease was determined. After ampicillin dosing and inoculation of C. difficile, cellulose fiber and polycarbophil (both at p<0.03) lengthened time to disease onset, while psyllium (p<0.01) and soy fiber (p<0.02) decreased disease onset time. Several mechanisms of fiber action were examined, including changes in the number or activity of the gastrointestinal microflora, number of C. difficile toxin A receptors, and post-dose levels of antibiotic in the cecum. Although the tested fiber supplements did not prevent C. difficile disease, this study shows that the dietary fibers can modulate C. difficile disease onset times in this hamster model by different mechanisms.

Noncancer Disease Incidence in the Atomic Bomb Survivors: 1958-1986

Using the longitudinal data of the Adult Health Study (AHS) cohort collected during 1958-1986, we examined for the first time the relationship between exposure to ionizing radiation and the incidence of 19 nonmalignant disorders in the A-bomb survivors. Affected individuals were ascertained through the three-digit codes of the International Classification of Diseases which are encoded in the AHS database subsequent to diagnoses made on the basis of general laboratory tests, physical examinations, and history-taking conducted during biennial AHS examinations. The disease onset time was estimated using the mid-point between the AHS examination data when the disease was initially reported and the previously attended disease-free examination date. Dosimetry System 86 organ doses judged to be most appropriate were used. Tests of dose effects were performed assuming a linear relative risk model with stratified background incidence. For the entire study period, significant excess risk was detected for uterine myoma (P < 0.001), chronic liver disease and cirrhosis (P = 0.006), and thyroid disease (P < 0.0001), defined broadly as the presence of one or more of certain noncancerous thyroid conditions. The incidence of myocardial infarction was shown to be increased (P = 0.03) in later years (1968-1986) among the younger heavily exposed AHS subjects, confirming the results of the recent Life Span Study (LSS) noncancer mortality report on coronary heart disease. The findings for uterine myoma may serve as additional evidence indicating benign tumor growth as a possible consequence of radiation exposure. Our results indicating the involvement of radiation in the development of liver diseases are consistent with the report of increased mortality from liver cirrhosis with radiation dose in the LSS cohort. An effect of age at exposure was detected for nonmalignant thyroid disease (P = 0.02), with an increased risk for those exposed who were under 20 years of age, but not for older survivors. Thus the AHS data suggest that thyroid glands in the young are more radiosensitive not only to the development of malignancies, but also to the development of nonmalignant disorders as well. The findings hold independently of the dose effects observed for thyroid cancer. This study also shows that for the period 1958-1986 new occurrences of lens opacity are not increased with radiation dose (P = 0.39) in the AHS subjects.