BCU Imaging Biobank, an Innovative Digital Resource for Biomedical Research Collecting Imaging and Clinical Data From Human Healthy and Pathological Subjects

To examine the validity of hybrid quality measures that use both clinical registry and administrative claims data, capitalizing on the strengths of each data source. Previous studies demonstrate substantial disagreement between clinical registry and administrative claims data on the occurrence of postoperative complications. Clinical data have greater validity than claims data for quality measurement but can be burdensome for hospitals to collect. American College of Surgeons National Surgical Quality Improvement Program records were linked to Medicare inpatient claims (2005-2008). National Quality Forum-endorsed risk-adjusted measures of 30-day postoperative complications or death assessed hospital quality for patients undergoing colectomy, lower extremity bypass, or all surgical procedures. Measures use hierarchical multivariable logistic regression to identify statistical outliers. Measures were applied using clinical data, claims data, or a hybrid of both data sources. Kappa statistics assessed agreement on determinations of hospital quality. A total of 111,984 patients participated from 206 hospitals. Agreement on hospital quality between clinical and claims data was poor. Hybrid models using claims data to risk-adjust complications identified by clinical data had moderate agreement with all clinical data models, whereas hybrid models using clinical data to risk-adjust complications identified by claims data had routinely poor agreement with all clinical data models. Assessments of hospital quality differ substantially when using clinical registry versus administrative claims data. A hybrid approach using claims data for risk adjustment and clinical data for complications may be a valid alternative with lower data collection burden. For quality measures focused on postoperative complications to be meaningful, such policies should require, at a minimum, collection of clinical outcomes data.

P.45 Adult SMA REACH: an integrated model to facilitate transition of data and longitudinal data collection of clinician and patient entered data

BackgroundThis retrospective study aimed to investigate the epidemiology of burns to the hand, including the causes, demographic data, management, and outcome in a single center in Southwest China between 2012 and 2017.Material/MethodsA retrospective study included 470 patients with hand burns who were treated at a single hospital in Southwest China between 2012 and 2017. Demographic, injury-related, and clinical data were obtained from the clinical electronic data collection system.ResultsIn 470 patients, men were more commonly admitted to hospital with hand burns (73.62%). Children under 10 years (29.57%) were the main patient group. Hospital admissions occurred in the coldest months, from December to March (55.11%). In 60.21% of cases, hand burns occurred outside the workplace. Fire (40.42%), electricity (30.85%), and hot liquids (20.21%) were the main causes of hand burns. Data from 428 patients showed that burns with a larger total body surface area and deeper burns were associated with surgery and amputation. Burn depth was a risk factor for skin grafting, and lack of burn cooling before hospital admission increased the risk of amputation. Data from 117 patients with localized burns showed that full-thickness burns and lack of cooling before admission were associated with an increased hospital stay.ConclusionsThe findings suggest that in Southwest China, prevention programs for children aged 0–9 years, injuries occurring in winter and non-workplace sites, and fire burns were imperative.

Data validation and quality framework for building a european multimodal real-world dataset for clinical outcome research in sickle cell disease

BackgroundA prospective Aboriginal Birth Cohort (ABC) study has been underway in Australia's Northern Territory since 1987. Inclusion of oral epidemiological information in a follow-up study required flexible and novel approaches with unconventional techniques. Documenting these procedures may be of value to researchers interested in including oral health components in remotely-located studies. The objectives are to compare and describe dental data collection methods in wave III of the ABC study with a more conventional oral health investigation.MethodsThe Australian National Survey of Adult Oral Health (NSAOH) was considered the 'conventional' study. Differences between this investigation and the dental component of the ABC study were assessed in terms of ethics, location, recruitment, consent, privacy, equipment, examination, clinical data collection and replication. In the ABC study, recording of clinical data by different voice recording techniques were described and assessed for ease-of-use portability, reliability, time-efficiency and cost-effectiveness.ResultsConventional investigation recruitment was by post and telephone. Participants self presented. Examinations took place in dental clinics, using customised dental chairs with standard dental lights attached. For all examinations, a dental assistant recorded dental data directly onto a laptop computer. By contrast, follow-up of ABC study participants involved a multi-phase protocol with reliance on locally-employed Indigenous advocates bringing participants to the examination point. Dental examinations occurred in settings ranging from health centre clinic rooms to improvised spaces outdoors. The dental chair was a lightweight, portable reclining camp chair and the dental light a fire-fighter's head torch with rechargeable batteries. The digital voice recorder was considered the most suitable instrument for clinical dental data collection in the ABC study in comparison with computer-based voice-recording software.ConclusionOral health examinations among indigenous populations residing in predominantly remote locations are more logistically challenging than are surveys of the general population. However, lack of resources or conventional clinical infrastructures need not compromise the collection of dental data in such studies. Instead, there is a need to be flexible and creative in establishing culturally-sensitive environments with available resources, and to consider non-conventional approaches to data gathering.

China is a populous country but lacks epidemiological data on keratoconus (KC). The present study aimed to investigate the clinical data, demographic data, and visual function (VF) data of KC patients in Central China. A total of 524 KC eyes in 307 KC patients (217 bilateral and 90 unilateral) from Henan Eye Hospital were included in the current study. Demographic and VF data were assessed with questionnaires administered by well-trained staff during face-to-face interviews. Visual acuity value was examined by a qualified optometrist, and the clinical data were measured by professional clinicians. The distributions of sex, residence and education level of KC patients were compared by Chi-square tests, and the ratios of people wearing glasses and rigid gas permeable (RGP) lenses were compared by McNemar tests. General linear models/Chi-squared tests were used to compare the clinical and demographic data according to KC severity. Spearman’s correlation analysis was used to test the associations between the data and KC severity. The mean age at diagnosis was 20.98 ± 6.06 years, and males had a higher ratio of KC than females (P < 0.001). Patients in rural areas had a higher rate of KC than those in urban areas (P = 0.039), and the proportion of KC patients with a higher education level (above high school) was high (P < 0.001). A total of 68.40% of the patients reported eye rubbing and 3.52% had a positive family history. The percentage of people wearing glasses was higher than that of patients wearing RGP lenses (P < 0.001). The total VF score of KC patients was 69.35 ± 15.25. The thinnest corneal thickness (TCT) and stiffness parameter at the first applanation (SP-A1) values were inversely correlated with KC severity (P < 0.05). The mean, steep, and max keratometry (Km, Ks and Kmax) values, the RGP lens use and keratoplasty were positively correlated with KC severity (all P < 0.05). The total VF score of the eye with better VA decreased as the severity increased (r = − 0.21, P = 0.002). The present study comprehensively describes various associated features of KC patients from a tertiary hospital in Central China, providing a reference for understanding the characteristics of KC patients in China.

Research in Alzheimer disease (AD) is rapidly moving toward the point of the earliest identification of the underlying disease processes. These include the accumulation of AB plaques, tau tangles and neuron as well as synaptic loss, and it is likely that these do not all occur contemporaneously. Many investigators contend that, by the time the clinical symptoms appear, sufficient AD pathology and neurodegeneration have occurred, which if irreversible, may reduce the efficacy of disease modifying therapy for clinically manifest AD.1 As such, efforts are underway to try to identify the onset of these pathological processes that culminate in clinically manifest AD dementia. However, to accomplish this, the underlying pathology must be detected, possibly through the use of neuroimaging and chemical biomarker measures. In this issue of JAMA, Mattsson and colleagues2 report their evaluation of the utility of cerebrospinal fluid (CSF) markers for AD in a large multicenter study.

Cardio-mechanical monitoring techniques, such as Seismocardiography (SCG) and Gyrocardiography (GCG), have received an ever-growing interest in recent years as potential alternatives to Electrocardiography (ECG) for heart rate monitoring. Wearable SCG and GCG devices based on lightweight accelerometers and gyroscopes are particularly appealing for continuous, long-term monitoring of heart rate and its variability (HRV). Heartbeat detection in cardio-mechanical signals is usually performed with the support of a concurrent ECG lead, which, however, limits their applicability in standalone cardio-mechanical monitoring applications. The complex and variable morphology of SCG and GCG signals makes the ECG-free heartbeat detection task quite challenging; therefore, only a few methods have been proposed. Very recently, a template matching method based on normalized cross-correlation (NCC) has been demonstrated to provide very accurate detection of heartbeats and estimation of inter-beat intervals in SCG and GCG signals of pathological subjects. In this study, the accuracy of HRV indices obtained with this template matching method is evaluated by comparison with ECG. Tests were performed on two public datasets of SCG and GCG signals from healthy and pathological subjects. Linear regression, correlation, and Bland-Altman analyses were carried out to evaluate the agreement of 24 HRV indices obtained from SCG and GCG signals with those obtained from ECG signals, simultaneously acquired from the same subjects. The results of this study show that the NCC-based template matching method allowed estimating HRV indices from SCG and GCG signals of healthy subjects with acceptable accuracy. On healthy subjects, the relative errors on time-domain indices ranged from 0.25% to 15%, on frequency-domain indices ranged from 10% to 20%, and on non-linear indices were within 8%. The estimates obtained on signals from pathological subjects were affected by larger errors. Overall, GCG provided slightly better performances as compared to SCG, both on healthy and pathological subjects. These findings provide, for the first time, clear evidence that monitoring HRV via SCG and GCG sensors without concurrent ECG is feasible with the NCC-based template matching method for heartbeat detection.

Collagen supplements are rising in the market as collagen has been demonstrated to be an important protein in the human aging process. Also, it is safe and easily absorbed in the body. Hence the aim of this study was to examine the effectiveness and safety of a collagen and antioxidant-rich treatment compared to a placebo in relation to various skin and hair indicators in healthy adult human subjects. Forty healthy adult non-pregnant/non-lactating women (aged 38-50 years) provided their informed consent in writing before their participation. Skin Radiance Collagen (SRC) treatment and a placebo were assessed for efficacy before application on Day 1, and post-application on Days 28 and 56, to measure changes in skin elasticity, hydration, brightness, pigmentation; texture, wrinkles, dryness, smoothness, fine lines, changes in the crow's feet region; as well as hair strength and hair fall. It was observed after 56 days that therapy with SRC, compared to placebo, produced a substantial effect on reduction of wrinkle depth and fine lines by 48.11% and 39%, respectively, with p-value <0.01 in the test group. There was a 15.69% improvement in skin hydration observed and 28% reduction in hair fall with p-value <0.01. SRC, a combination of collagen with hyaluronic acid (HA), biotin, and vitamins C and E, showed a significant improvement in skin and hair health, including improvements in skin elasticity, skin hydration, reduction in crow's feet area wrinkles and fine lines, hair fall, and decrease in roughness, leading to improved skin texture. Vitamin C in the formulation also acts as a collagen builder for the body and helps in preventing oxidative stress in the body. The test treatment SRC was found to be efficacious and safe in healthy human adult subjects.

Integration of various sources of information for prediction of disease progression is an unmet need in glaucoma diagnostics. We designed a deep learning-based prognostic model incorporating clinical and structural data for forecasting functional glaucoma progression and compared its performance to clinicians. Retrospective, comparative cohort study of prognostic accuracy. We included 1599 eyes (908 patients) with definite or suspected glaucoma with ≥5 24-2 visual fields (VF) and 3 or more years of follow-up. VF mean deviation (MD) rates of change were estimated with linear regression. Sequential MD rates of change were estimated with each series spanning only 5 years of follow-up. VF progression was declared when four sequential statistically significant negative MD slopes were observed, and slope for the entire follow-up was significant. A convolutional neural network pretrained on ImageNet was designed to predict VF progression using baseline clinical and demographic data, disc photographs, and optical coherence tomography-derived global and sectoral retinal nerve fiber layer and macular thickness measurements. In addition, average intraocular pressure and treatment information during follow-up were put into the model. The same data for a subset of patients was provided to two clinicians to independently predict future progression. The model was validated on a separate cohort of eyes in which optical coherence tomography imaging was done with a different device (291 eyes). Model's area under receiver operating characteristic curves (AUC), accuracy, and area under the precision and recall curves. Average (SD) baseline MD and number of VF exams were -3.5 (4.9) dB and 10.1 (4.7). 399 eyes (25%) deteriorated. The best-performing model incorporated baseline disc photographs, and retinal nerve fiber layer and macular thickness: AUC, 0.839 (0.771-0.906), accuracy, 76.0% (62.0%-85.0%), and area under the precision and recall curves, 0.558 (0.385-0.733). Deep learning model significantly outperformed clinical graders (AUC : 0.629 [0.531-0738], P < .001 and 0.680 [0.584-0.776], P = .001, for grader one and two, respectively). Model performance was similar on the validation cohort (AUC: 0.754 [0.671-0.837], and accuracy: 77% [71%-82%], respectively, P = .122). The model performed well when predicting fast-progression, defined as MD rate <-1.0 dB/y (AUC: 0.869 [0.792-0.947]). Our newly designed deep learning model can combine baseline demographic and clinical data with widely available structural measurements and provide clinically relevant information for the prediction of glaucoma progression.

Development and significance of automated history-taking software for clinical medicine, clinical research and basic medical science.

In Africa, sickle cell disease phenotypes' genetic contributors remain understudied due to the dearth of databases that pair biospecimens with demographic and clinical details. The absence of biorepositories in these settings can exacerbate this issue. This article documents the physical verification process of biospecimens in the biorepository, connecting them to patient clinical and demographic data and aiding in the planning of future genomic and clinical research studies' experience from the Muhimbili Sickle Cell Program in Dar es Salaam, Tanzania. The biospecimen database was updated with the current biospecimen position following the physical verification and then mapping this information to its demographic and clinical data using demographic identifiers. The biorepository stored 74,079 biospecimens in three -80°C freezers, including 63,345 from 5159 patients enrolled in the cohort between 2004 and 2016. Patients were identified by a control (first visit), entry (when confirmed sickle cell homozygous), admission (when hospitalized), and follow-up numbers (subsequent visits). Of 63,345 biospecimens, follow-ups were 46,915 (74.06%), control 8067 (12.74%), admission 5517 (8.71%), and entry 2846 (4.49%). Of these registered patients, females were 2521 (48.87%) and males were 2638 (51.13%). The age distribution was 1-59 years, with those older than 18 years being 577 (11.18%) and children 4582 (88.82%) of registered patients. The notable findings during the process include a lack of automated biospecimen checks, laboratory information management system, and tubes with volume calibration; this caused the verification process to be tedious and manual. Biospecimens not linked to clinical and demographic data, date format inconsistencies, and lack of regular updating of a database on exhausted biospecimens and updates when biospecimens are moved between positions within freezers were other findings that were found. A well-organized biorepository plays a crucial role in answering future research questions. Enforcing standard operating procedures and quality control will ensure that laboratory users adhere to the best biospecimen management procedures.

Many readmission prediction models have marginal accuracy and are based on clinical and demographic data that exclude patient response data. The objective of this study was to evaluate the accuracy of a 30-day hospital readmission prediction model that incorporates patient response data capturing the patient experience. This was a prospective cohort study of 30-day hospital readmissions. A logistic regression model to predict readmission risk was created using patient responses obtained during interviewer-administered questionnaires as well as demographic and clinical data. Participants (N = 846) were admitted to 2 inpatient adult medicine units at Massachusetts General Hospital from 2012 to 2016. The primary outcome was the accuracy (measured by receiver operating characteristic) of a 30-day readmission risk prediction model. Secondary analyses included a readmission-focused factor analysis of individual versus collective patient experience questions. Of 1754 eligible participants, 846 (48%) were enrolled and 201 (23.8%) had a 30-day readmission. Demographic factors had an accuracy of 0.56 (confidence interval [CI], 0.50–0.62), clinical disease factors had an accuracy of 0.59 (CI, 0.54–0.65), and the patient experience factors had an accuracy of 0.60 (CI, 0.56–0.64). Taken together, their combined accuracy of receiver operating characteristic = 0.78 (CI, 0.74–0.82) was significantly more accurate than these factors were individually. The individual accuracy of patient experience, demographic, and clinical data was relatively poor and consistent with other risk prediction models. The combination of the 3 types of data significantly improved the ability to predict 30-day readmissions. This study suggests that more accurate 30-day readmission risk prediction models can be generated by including information about the patient experience.

Ancestry component as a major predictor of lithium response in the treatment of bipolar disorder

Background: Advanced fibrosis (F2–F4) drives morbidity and mortality in metabolic dysfunction-associated steatotic liver disease (MASLD). Population-wide screening is impractical due to patient volume and health care costs. We hypothesized that machine learning (ML) algorithms trained on routine demographic and clinical data could identify patients at risk of significant fibrosis, reducing reliance on blood draws or transient elastography (TE). Methods: As part of the Liver Beware study, 4,193 patients prospectively underwent TE. Clinical and demographic data, such as age, BMI, race, diabetes, and hypertension, were collected immediately prior to elastography. Data were split into training (60%), validation (20%), and test (20%) sets. Six ML algorithms were evaluated: logistic regression, logistic regression with SMOTE, XGBoost, random forest, SVM, and ensemble voting classifier. Performance was assessed by accuracy, sensitivity, specificity, precision, and area under the curve (AUC). Results: XGBoost had the most well-balanced test performance with 72.2% accuracy, 59.7% sensitivity, 73.4% specificity, 17.4% precision, and AUC of 0.72. Random forest had the highest accuracy (91.1%) but low sensitivity (1.4%). XGBoost identified obesity, diabetes, and hypertension as the leading predictors of risk of fibrosis. Conclusions: ML algorithms based on readily available demographic and clinical data can identify patients at high risk of fibrosis with acceptable accuracy. This scalable approach enables triaging for further testing such as TE, trading marginal AUC reduction for maximal accessibility compared with biomarker-dependent scores (eg, SAFE, Agile 4/3+). Implementation and cost-effectiveness studies are needed to refine referral thresholds and evaluate real-world impact.