Using machine learning modeling to identify childhood abuse victims on the basis of personality inventory responses

Trauma is very common and associated with significant co-morbidity world-wide, particularly PTSD and frequently other mental health disorders. However, it can be challenging to identify victims of abuse as self-reports can be difficult to elicit due to emotional distress. Better confirmation of a history of significant mistreatment can assist significantly in treatment planning. We evaluate an alternate approach based on machine-learning techniques applied to personality inventory data (Minnesota Personality Inventory, Adolescent Version; MMPI-A) obtained concurrently to examine convergence with reports of past trauma exposure. The Childhood Trauma Questionnaire (CTQ) was administered to 733 child and adolescent inpatients. Statistical and information-theory measures showed that each type of abuse - sexual, physical, and emotional - had a unique "fingerprint" of MMPI-A profiles. In contrast to our previous findings in terms of specific correlations with IQ, individuals positive for Sexual abuse had the fewest MMPI-A elevations, followed by Physical abuse, while those reporting Emotional abuse had the greatest number of elevations. We developed an initial classifier Machine Learning (ML) model for predicting a history of abuse that demonstrates equivalent sensitivity compared to other widely used screening measures. In addition, we show via PCA and cluster analysis that the different levels of severity of emotional abuse present with unique mixtures of personality trait characteristics. Thus, this type of ML mediated analysis could permit at-scale detection of those at potential high risk of a history of abuse by use of real-time information, using a variety of nontransparent data sources.

Sentence Repetition as a Diagnostic Tool for Developmental Language Disorder: A Systematic Review and Meta-Analysis

PURPOSE

This systematic review and multilevel meta-analysis examines the accuracy of sentence repetition (SR) tasks in distinguishing between typically developing (TD) children and children with developmental language disorder (DLD). It explores variation in the way that SR tasks are administered and/or evaluated and examines whether variability in the reported ability of SR to detect DLD is related to these differences.

METHOD

Four databases were searched to identify studies that had used an SR task on groups of monolingual children with DLD and TD children. Searches produced 3,459 articles, of which, after screening, 66 were included in the systematic review. A multilevel meta-analysis was then conducted using 46 of these studies. Multiple preregistered subgroup analyses were conducted in order to explore the sources of heterogeneity.

RESULTS

The systematic review found a great deal of methodological variation, with studies spanning 19 languages, 39 SR tasks, and four main methods of production scoring. There was also variation in study design, with different sampling (clinical and population sampling) and matching (age and language matching) methods. The overall meta-analysis found that, on average, TD children outperformed children with DLD on the SR tasks by 2.08 SDs. Subgroup analyses found that effect size only varied as a function of the matching method and language of the task.

CONCLUSIONS

Our results indicate that SR tasks can distinguish children with DLD from both age- and language-matched samples of TD children. The usefulness of SR appears robust to most kinds of task and study variation.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25864405.

Predicting sexually transmitted infections among men who have sex with men in Zimbabwe using deep learning and ensemble machine learning models

There is a substantial increase in sexually transmitted infections (STIs) among men who have sex with men (MSM) globally. Unprotected sexual practices, multiple sex partners, criminalization, stigmatisation, fear of discrimination, substance use, poor access to care, and lack of early STI screening tools are among the contributing factors. Therefore, this study applied multilayer perceptron (MLP), extremely randomized trees (ExtraTrees) and XGBoost machine learning models to predict STIs among MSM using bio-behavioural survey (BBS) data in Zimbabwe. Data were collected from 1538 MSM in Zimbabwe. The dataset was split into training and testing sets using the ratio of 80% and 20%, respectively. The synthetic minority oversampling technique (SMOTE) was applied to address class imbalance. Using a stepwise logistic regression model, the study revealed several predictors of STIs among MSM such as age, cohabitation with sex partners, education status and employment status. The results show that MLP performed better than STI predictive models (XGBoost and ExtraTrees) and achieved accuracy of 87.54%, recall of 97.29%, precision of 89.64%, F1-Score of 93.31% and AUC of 66.78%. XGBoost also achieved an accuracy of 86.51%, recall of 96.51%, precision of 89.25%, F1-Score of 92.74% and AUC of 54.83%. ExtraTrees recorded an accuracy of 85.47%, recall of 95.35%, precision of 89.13%, F1-Score of 92.13% and AUC of 60.21%. These models can be effectively used to identify highly at-risk MSM, for STI surveillance and to further develop STI infection screening tools to improve health outcomes of MSM.

Predictive validity of the Developmental Coordination Disorder Questionnaire as a screening tool to identify motor skill problems: A systematic review and meta-analysis

BACKGROUND

The Developmental Coordination Disorder Questionnaire (DCDQ) has been used to screen children who probably have developmental coordination disorder (DCD).

AIMS

We systematically reviewed studies on the predictive validity of the DCDQ and performed a meta-analysis on its diagnostic accuracy.

METHODS AND PROCEDURES

Literature was searched through four electronic databases: MEDLINE, Embase, CINAHL, and PsycArticles. A total of 27 studies was selected based on the inclusion criteria. The sensitivity and specificity of the DCDQ were assessed using summary receiver operating characteristic (sROC) curves. Subgroup analyses were conducted according to the DCDQ type, reference standard, and participant type.

OUTCOMES AND RESULTS

Overall, the DCDQ has a sensitivity of 0.70 and a specificity of 0.77, showing moderate diagnostic accuracy (area under the curve, 0.80). Subgroup analysis showed that the revised version of the DCDQ had higher diagnostic accuracy than the original version. When the reference standard was the Diagnostic and Statistical Manual of Mental Disorders, the sensitivity and specificity of the DCDQ were 0.87 and 0.83, respectively. The diagnostic accuracy was higher in clinical samples compared to the general population.

CONCLUSIONS AND IMPLICATIONS

This study demonstrated that the DCDQ has adequate diagnostic accuracy, suggesting it can help screen children with motor skill deficits.

New data-driven method to predict the therapeutic indication of redeemed prescriptions in secondary data sources: a case study on antiseizure medications users aged ≥65 identified in Danish registries

OBJECTIVES

We aimed to develop a new data-driven method to predict the therapeutic indication of redeemed prescriptions in secondary data sources using antiepileptic drugs among individuals aged ≥65 identified in Danish registries.

DESIGN

This was an incident new-user register-based cohort study using Danish registers.

SETTING

The study setting was Denmark and the study period was 2005-2017.

PARTICIPANTS

Participants included antiepileptic drug users in Denmark aged ≥65 with a confirmed diagnosis of epilepsy.

PRIMARY AND SECONDARY OUTCOME MEASURES

Sensitivity served as the performance measure of the algorithm.

RESULTS

The study population comprised 8609 incident new users of antiepileptic drugs. The sensitivity of the algorithm in correctly predicting the therapeutic indication of antiepileptic drugs in the study population was 65.3% (95% CI 64.4 to 66.2).

CONCLUSIONS

The algorithm demonstrated promising properties in terms of overall sensitivity for predicting the therapeutic indication of redeemed antiepileptic drugs by older individuals with epilepsy, correctly identifying the therapeutic indication for 6 out of 10 individuals using antiepileptic drugs for epilepsy.

Diagnostic accuracy of the pediatric CAM-ICU, pre-school CAM-ICU, Pediatric Anesthesia Emergence Delirium and Cornell Assessment of Pediatric Delirium for detecting delirium in the pediatric intensive care unit: A systematic review and meta-analysis

OBJECTIVES

Delirium is a frequent complication of critical illness, affecting 34% of children admitted to pediatric intensive care units. The commonly used tools for detecting delirium in the pediatric intensive care unit include the Pediatric Confusion Assessment Method for the intensive care unit (ICU, pCAM-ICU), Preschool Confusion Assessment Method for the ICU (psCAM-ICU), Pediatric Anesthesia Emergence Delirium and the Cornell Assessment of Pediatric Delirium.

DATA SOURCES

We searched four electronic databases for relevant articles from inception to March 1, 2023.

STUDY SELECTION

All full-text observational studies examining the sensitivity and specificity of the four tools for screening delirium in the pediatric intensive care units were included.

DATA EXTRACTION

Two researchers independently identified articles, extracted data, and retrieved the diagnostic accuracy parameters of the pediatric CAM-ICU, pre-school CAM-ICU, Pediatric Anesthesia Emergence Delirium, and Cornell Assessment of Pediatric Delirium relative to standard references. A bivariate diagnostic statistical analysis with a random-effects model was performed.

DATA SYNTHESIS

Four, five, three and seven studies on the pediatric CAM-ICU, pre-school CAM-ICU, Pediatric Anesthesia Emergence Delirium, and Cornell Assessment of Pediatric Delirium, respectively, were identified. Due to the limited number of Pediatric Anesthesia Emergence Delirium articles included, no pooled diagnostic accuracy was produced. The pooled sensitivity was 0.73, 0.84, and 0.95 for the pediatric CAM-ICU, pre-school CAM-ICU, and Cornell Assessment of Pediatric Delirium, respectively, whereas the pooled specificity was 0.98, 0.90, and 0.81, respectively. The Cornell Assessment of Pediatric Delirium had greater sensitivity compared to both the pediatric CAM-ICU and pre-school CAM-ICU (both p = 0.04) and lower specificity than the pediatric CAM-ICU did (p < 0.001). Age, sample size, and mechanical ventilation use were significant moderators of the specificity of the pediatric CAM-ICU (p < 0.001, <0.001, and = 0.001, respectively).

CONCLUSIONS

Our data indicate that the Cornell Assessment of Pediatric Delirium is a more dependable instrument than the pediatric CAM-ICU and pre-school CAM-ICU for detecting pediatric intensive care delirium occurrence. More studies on the Pediatric Anesthesia Emergence Delirium are warranted.

IMPLICATIONS FOR CLINICAL PRACTICE

Healthcare providers are suggested adopting the Cornell Assessment of Pediatric Delirium into daily routine for the early detection of delirium in pediatric intensive care units.

On repeated diagnostic testing in screening for a medical condition: How often should the diagnostic test be repeated?

In screening large populations a diagnostic test is frequently used repeatedly. An example is screening for bowel cancer using the fecal occult blood test (FOBT) on several occasions such as at 3 or 6 days. The question that is addressed here is how often should we repeat a diagnostic test when screening for a specific medical condition. Sensitivity is often used as a performance measure of a diagnostic test and is considered here for the individual application of the diagnostic test as well as for the overall screening procedure. The latter can involve an increasingly large number of repeated applications, but how many are sufficient? We demonstrate the issues involved in answering this question using real data on bowel cancer at St Vincents Hospital in Sydney. As data are only available for those testing positive at least once, an appropriate modeling technique is developed on the basis of the zero-truncated binomial distribution which allows for population heterogeneity. The latter is modeled using discrete nonparametric maximum likelihood. If we wish to achieve an overall sensitivity of 90%, the FOBT should be repeated for 2 weeks instead of the 1 week that was used at the time of the survey. A simulation study also shows consistency in the sense that bias and standard deviation for the estimated sensitivity decrease with an increasing number of repeated occasions as well as with increasing sample size.

[Overview on utility of in-house tests for detection of systemic infectious diseases in dogs]

For detection of infectious diseases, several point-of-care (POC) tests are on the market in addition to methods performed in commercial laboratories. These POC tests are based on enzyme-linked immunosorbent assay (ELISA) or other immunochromatographic technologies and present results within few minutes in veterinary practice. This article gives an overview of the utility of numerous POC tests of different manufacturers for detection of parvovirus antigen in feces, Dirofilaria (D.) immitis antigen in blood as well as antibodies against Borrelia (B.) burgdorferi, Anaplasma (A.) spp., Ehrlichia (E.) spp., Leptospira (L.) spp. and Leishmania (L.) infantum in blood (single or in different combinations). Sensitivity and specificity of these tests are important for their usefulness in veterinary practice. Furthermore, presence of antibodies or detection of antigen has to correlate with the presence of clinical signs. POC tests for detection of canine parvovirus antigen have a very high specificity, the sensitivity of all evaluated POC tests, however, is very low. POC tests for detection of D. immitis antigen have a very high sensitivity and specificity. As they detect antigen from the uterus of female adult parasites, test results are negative when only very few female or only male adults are present. POC tests for detection of antibodies against B. burgdorferi only indicate contact with Borrelia spp. and do not prove clinical Lyme disease, as the infection only extremely rarely causes clinical signs. POC tests for detection of antibodies against A. phagocytophilum are also not suitable for diagnosis of clinical anaplasmosis. Infections with A. phagocytophilum only lead to clinical disease in very rare cases and in these, clinical signs occur before the development of antibodies. POC tests for detection of antibodies against E. canis have a very high sensitivity as well as specificity. POC tests for detection of antibodies against L. infantum and Leptospira species (spp.) show a very high specificity and a high sensitivity. However, Leptospira spp. antibody-positive results may occur following vaccination, as the POC tests cannot distinguish between field and vaccination strains.

Bias in Laboratory Medicine: The Dark Side of the Moon

Physicians increasingly use laboratory-produced information for disease diagnosis, patient monitoring, treatment planning, and evaluations of treatment effectiveness. Bias is the systematic deviation of laboratory test results from the actual value, which can cause misdiagnosis or misestimation of disease prognosis and increase healthcare costs. Properly estimating and treating bias can help to reduce laboratory errors, improve patient safety, and considerably reduce healthcare costs. A bias that is statistically and medically significant should be eliminated or corrected. In this review, the theoretical aspects of bias based on metrological, statistical, laboratory, and biological variation principles are discussed. These principles are then applied to laboratory and diagnostic medicine for practical use from clinical perspectives.

Validation of the stratify scale for the prediction of falls among hospitalized adults in a tertiary hospital in Colombia: a retrospective cohort study

The STRATIFY scale has been implemented as a preventive strategy for predicting the risk of accidental falls among hospitalized adults. However, there is still uncertainty about its accuracy. This study aimed to perform an external validation of the STRATIFY fall prediction scale in hospitalized adults in one tertiary care hospital in Bogotá, Colombia. The study was a retrospective cohort of adult hospitalized patients in a high-level complexity care hospital. The sample selected included admitted patients (age ≥ 18), consecutively by the institution between 2018 and 2020, with an evaluation of the fall risk measured by the STRATIFY score given to each at the time of hospital admission. For assessing the scale's feasibility, its discriminative capability was obtained by calculating sensitivity, specificity, likelihood ratios, predictive values, and area under the ROC curve. The evaluation included 93,347 patient hospital records (mean 56.9 years, 50.2% women). The overall sensitivity score was 0.672 [IC 95% 0.612-0.723], the specificity score was 0.612 [IC 95% 0.605-0.615], and the positive likelihood ratio was 1.73 [IC 95% 1.589-1.891]. The area under the ROC curve was 0.69 [IC 95% 0.66-0.72]. Subgroups of age obtained similar results. Applying the STRATIFY scale at hospital admission resulted in a lower performance of the tool-predict falls in hospitalized patients. It is necessary to implement an individual evaluation of the risk factors for falls in order to structure appropriate care plans to prevent and improve hospital safety.

Validity of a Mobile Application to Diagnose Temporomandibular Disorders

This study aimed to assess the diagnostic accuracy of a mobile application by comparing its diagnoses to those of Orofacial Pain and Oral Medicine specialists and further imaging results (CBCT and MRI) in 500 patients with temporomandibular disorder (TMD). The research focused on three diagnostic categories: the initial specialist diagnoses, the final diagnoses after imaging, and the mobile app's diagnoses. The concordance rates, sensitivities, specificities, and positive predictive values of the diagnoses were examined, with further imaging serving as the gold standard. The mobile app demonstrated a high concordance rate compared to both the final (0.93) and the initial specialists' diagnoses (0.86). The sensitivities, specificities, and positive predictive values also indicated strong reliability, affirming the app's diagnostic validity. Although the concordance rate was slightly lower when comparing the app's diagnoses to the imaging results (CBCT and MRI), the specialists' diagnoses yielded similar results. The study suggests that user-friendly diagnostic mobile applications, based on the diagnostic criteria for TMD, could enhance the clinical management of TMD. Given the reliability of mobile applications for diagnostic purposes, their wider implementation could facilitate the provision of appropriate and timely treatments for patients with TMD.

MRT-ModSeq - Rapid Detection of RNA Modifications with MarathonRT

Chemical modifications are essential regulatory elements that modulate the behavior and function of cellular RNAs. Despite recent advances in sequencing-based RNA modification mapping, methods combining accuracy and speed are still lacking. Here, we introduce MRT-ModSeq for rapid, simultaneous detection of multiple RNA modifications using MarathonRT. MRT-ModSeq employs distinct divalent cofactors to generate 2-D mutational profiles that are highly dependent on nucleotide identity and modification type. As a proof of concept, we use the MRT fingerprints of well-studied rRNAs to implement a general workflow for detecting RNA modifications. MRT-ModSeq rapidly detects positions of diverse modifications across a RNA transcript, enabling assignment of m1acp3Y, m1A, m3U, m7G and 2'-OMe locations through mutation-rate filtering and machine learning. m1A sites in sparsely modified targets, such as MALAT1 and PRUNE1 could also be detected. MRT-ModSeq can be trained on natural and synthetic transcripts to expedite detection of diverse RNA modification subtypes across targets of interest.

Physical Examination Maneuvers for SLAP Lesions: A Systematic Review and Meta-analysis of Individual and Combinations of Maneuvers

BACKGROUND

Superior labrum anterior to posterior (SLAP) lesions are targeted on physical examination using a variety of provocative maneuvers.

PURPOSE/HYPOTHESIS

The purpose was to conduct a systematic review on the performance of physical examination maneuvers in diagnosing SLAP lesions and to perform a meta-analysis comparing the sensitivity and specificity of these examinations both individually and in combination. The null hypothesis stated that there would be no significant difference in the sensitivity or specificity of the included physical examination tests, neither individually nor in combination.

STUDY DESIGN

Meta-analysis and systematic review; Level of evidence, 4.

METHODS

A systematic review was performed with the inclusion criteria of studies that reported either the sensitivities and specificities or the number of true-positive, true-negative, false-positive, or false-negative results for at least 1 maneuver for identifying SLAP lesions. A meta-analysis was performed to determine the sensitivity and specificity of individual maneuvers. Additional analysis determined the performance of these maneuvers when combined in series and parallel. In series, all must be present to be considered positive. In parallel, any single positive test forces the overall combination to be considered positive. Only tests that were included in ≥3 studies were considered in the meta-analysis and those included in ≥4 studies were considered in the combination analysis.

RESULTS

Overall, 862 studies were identified, 18 of which were included in the systematic review and meta-analysis. The physical examinations included were the O'Brien (n = 16), speed (n = 8), Yergason (n = 6), anterior slide (n = 8), crank (n = 7), Jobe (n = 5), dynamic labral shear (n = 3), Kim 2 (n = 3), and biceps groove tenderness tests (n = 3). All combinations of 2 to 5 maneuvers in both series and parallel were considered. The O'Brien and crank test combination was the most sensitive 2-test combination in both parallel and series. The Yergason and anterior slide test combination was the most specific 2-test combination in parallel and series.

CONCLUSION

This systematic review and meta-analysis reports an updated meta-analysis considering the sensitivity and specificity of common physical examination maneuvers used in the diagnosis of SLAP lesions and considers these values for tests in both series and parallel combinations. The present analysis demonstrates improved specificities when tests are considered in series and improved sensitivities when considered in parallel combination.

Myocardial infarction detection using ITD, DWT and deterministic learning based on ECG signals

Nowadays, cardiovascular diseases (CVD) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention issue. Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgement and several electrocardiographic (ECG) or vectorcardiographic (VCG) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study, we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector, which is decomposed into a series of proper rotation components (PRCs) by using the intrinsic time-scale decomposition (ITD) method. The novel cardiac vector may fully reflect the pathological alterations provoked by MI and may be correlated to the disparity of cardiac system dynamics between healthy and MI subjects. ITD is employed to measure the variability of cardiac vector and the first PRCs are extracted as predominant PRCs which contain most of the cardiac vector's energy. Second, four levels discrete wavelet transform with third-order Daubechies (db3) wavelet function is employed to decompose the predominant PRCs into different frequency bands, which combines with three-dimensional phase space reconstruction to derive features. The properties associated with the cardiac system dynamics are preserved. Since the frequency components above 40 Hz are lack of use in ECG analysis, in order to reduce the feature dimension, the advisable sub-band (D4) is selected for feature acquisition. Third, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the tenfold cross-validation style, the achieved average classification accuracy is reported to be 98.20%. Results verify the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

Cloning and expression of Fasciola hepatica enolase gene and efficacy of recombinant protein in the serodiagnosis of sheep fasciolosis

Fasciolosis caused by Fasciola hepatica is a disease of zoonotic importance that is common worldwide and can cause serious problems in farm animals, some wild animals and humans. The development of diagnostic kits for the correct diagnosis of fasciolosis in sheep is important in terms of preventing yield losses. With this study, it is aimed to clone and express the enolase gene to be isolated from adult F. hepatica and to determine the effectiveness of the recombinant antigen in the serodiagnosis of sheep fasciolosis. For this aim, primers were designed to amplify the enolase gene from the F. hepatica enolase sequence, mRNA was isolated from F. hepatica adult fluke obtained from an infected sheep followed by cDNA was obtained. Enolase gene was amplified by PCR and the product was cloned and then expressed. The efficiency of the purified recombinant protein was displayed by Western blot (WB) and ELISA using positive and negative sheep sera. As a result, the sensitivity and specificity rates of the recombinant FhENO antigen were 85% and %82.8 by WB while the rates were 90% and 97.14% by ELISA, respectively. At the same time, in sheep blood sera samples collected from the Elazig and Siirt provinces of Turkey, 100 (50%) of 200 sera were found to be positive by WB and 46 (23%) were found to be positive by ELISA. The most important problem in ELISA was the high cross-reaction rate of the recombinant antigen used, as in WB. In order to prevent the cross-reactions, it will be useful to compare the genes encoding the enolase protein of parasites from the closely related parasite family, and select the regions where there are no common epitopes, and clone them and test the purified protein.

Leveraging electronic data to expand infection detection beyond traditional settings and definitions (Part II/III)

The rich and complex electronic health record presents promise for expanding infection detection beyond currently covered settings of care. Here, we review the "how to" of leveraging electronic data sources to expand surveillance to settings of care and infections that have not been the traditional purview of the National Healthcare Safety Network (NHSN), including a discussion of creation of objective and reproducible infection surveillance definitions. In pursuit of a 'fully automated' system, we also examine the promises and pitfalls of leveraging unstructured, free-text data to support infection prevention activities and emerging technological advances that will likely affect the practice of automated infection surveillance. Finally, barriers to achieving a completely 'automated' infection detection system and challenges with intra- and interfacility reliability and missing data are discussed.

Nanoparticle-Based Plasmonic Biosensor for the Unamplified Genomic Detection of Carbapenem-Resistant Bacteria

Antimicrobial resistance (AMR) is a global public health issue, and the rise of carbapenem-resistant bacteria needs attention. While progress is being made in the rapid detection of resistant bacteria, affordability and simplicity of detection still need to be addressed. This paper presents a nanoparticle-based plasmonic biosensor for detecting the carbapenemase-producing bacteria, particularly the beta-lactam Klebsiella pneumoniae carbapenemase (bla_KPC) gene. The biosensor used dextrin-coated gold nanoparticles (GNPs) and an oligonucleotide probe specific to bla_KPC to detect the target DNA in the sample within 30 min. The GNP-based plasmonic biosensor was tested in 47 bacterial isolates: 14 KPC-producing target bacteria and 33 non-target bacteria. The stability of GNPs, confirmed by the maintenance of their red appearance, indicated the presence of target DNA due to probe-binding and GNP protection. The absence of target DNA was indicated by the agglomeration of GNPs, corresponding to a color change from red to blue or purple. The plasmonic detection was quantified with absorbance spectra measurements. The biosensor successfully detected and differentiated the target from non-target samples with a detection limit of 2.5 ng/μL, equivalent to ~10³ CFU/mL. The diagnostic sensitivity and specificity were found to be 79% and 97%, respectively. The GNP plasmonic biosensor is simple, rapid, and cost-effective in detecting bla_KPC-positive bacteria.

The Evaluation of Inflammatory Biomarkers in Predicting Progression of Acute Pancreatitis to Pancreatic Necrosis: A Diagnostic Test Accuracy Review

Acute pancreatitis is the acute inflammation of the pancreas; 30% of cases may progress to pancreatic necrosis. The aim of this study was to assess the diagnostic accuracy of inflammatory biomarkers (C-reactive protein (CRP), procalcitonin (PCT), and lactate dehydrogenase (LDH)) in detecting pancreatic necrosis in adults with confirmed acute pancreatitis within 14 days of symptom onset and without organ failure. A systematic search was conducted across the Cumulative Index of Nursing and Allied Health (CINAHL), Cochrane, Embase, PubMed, and Web of Science until May 30, 2022, with the following keywords: acute disease, biomarkers, C-reactive protein, calcitonin, differential, diagnosis, lactate dehydrogenase, pancreatitis, acute necrotizing, necrosis, sensitivity, specificity. Statistical analysis was conducted in RevMan 5.4.1 (Cochrane). Five studies pooling 645 participants were included of which 59.8% were males, with a mean age of 49 years. CRP was the best cutoff at 279 mg/L (χ2 = 47.43, p < 0.001), followed by 200 mg/L (χ2 = 36.54, p < 0.001). LDH was cut off at 290 units/L (χ2 = 51.6, p < 0.001), whereas PCT did not display the most reliable results at 0.05 ng/mL. Inflammatory biomarkers are scalable diagnostic tools that may confer clinical value by decreasing the mortality of acute pancreatitis sequelae.

Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients

Brain-Computer Interface (BCI) systems for motor rehabilitation after stroke have proven their efficacy to enhance upper limb motor recovery by reinforcing motor related brain activity. Hybrid BCIs (h-BCIs) exploit both central and peripheral activation and are frequently used in assistive BCIs to improve classification performances. However, in a rehabilitative context, brain and muscular features should be extracted to promote a favorable motor outcome, reinforcing not only the volitional control in the central motor system, but also the effective projection of motor commands to target muscles, i.e., central-to-peripheral communication. For this reason, we considered cortico-muscular coupling (CMC) as a feature for a h-BCI devoted to post-stroke upper limb motor rehabilitation. In this study, we performed a pseudo-online analysis on 13 healthy participants (CTRL) and 12 stroke patients (EXP) during executed (CTRL, EXP unaffected arm) and attempted (EXP affected arm) hand grasping and extension to optimize the translation of CMC computation and CMC-based movement detection from offline to online. Results showed that updating the CMC computation every 125 ms (shift of the sliding window) and accumulating two predictions before a final classification decision were the best trade-off between accuracy and speed in movement classification, independently from the movement type. The pseudo-online analysis on stroke participants revealed that both attempted and executed grasping/extension can be classified through a CMC-based movement detection with high performances in terms of classification speed (mean delay between movement detection and EMG onset around 580 ms) and accuracy (hit rate around 85%). The results obtained by means of this analysis will ground the design of a novel non-invasive h-BCI in which the control feature is derived from a combined EEG and EMG connectivity pattern estimated during upper limb movement attempts.

Core Needle Biopsy in Suspicious Malignant Thyroid Nodules with Repeated Nondiagnostic Fine Needle Aspiration

Key message

The clinician could have CNB in mind for thyroid nodules when FNA results were nondiagnostic. Our study would suggest CNB a safe and efficient method for investigating thyroid nodules.

Abstract

Usefulness of preoperative tissue sampling and pathology diagnoses in thyroid tumors were accepted worldwide. We investigate the role of Core needle biopsy (CNB) in the thyroid nodules lesions when FNA results are nondiagnostic. We conducted a cross-sectional study to evaluate twenty-six CNBs results of suspicious malignant thyroid nodules with nondiagnostic repeated fine needle aspiration. 25 from 26 CNBs were diagnostic. Twenty-one needle biopsy reports were papillary thyroid carcinoma, three CNB samples diagnosed medullary thyroid carcinoma and one of them had anaplastic results. All diagnostic needle biopsies results were compatible with final pathology. Our study would suggest CNB a safe and efficient method for investigating thyroid nodules while repeated FNA yielded nondiagnostic results.

Development of a Predictive Model for Common Bile Duct Stones in Patients With Clinical Suspicion of Choledocholithiasis: A Cohort Study

BACKGROUND

Current choledocholithiasis guidelines heavily focus on patients with low or no risk, they may be inappropriate for populations with high rates of choledocholithiasis. We aimed to develop a predictive scoring model for choledocholithiasis in patients with relevant clinical manifestations.

METHODS

A multivariable predictive model development study based on a retrospective cohort of patients with clinical suspicion of choledocholithiasis was used in this study. The setting was a 700-bed public tertiary hospital. Participants were patients who had completed three reference tests (endoscopic retrograde cholangiography, magnetic resonance cholangiopancreatography, and intraoperative cholangiography) from January 2019 to June 2021. The model was developed using logistic regression analysis. Predictor selection was conducted using a backward stepwise approach. Three risk groups were considered. Model performance was evaluated by area under the receiver operating characteristic curve, calibration, classification measures, and decision curve analyses.

RESULTS

Six hundred twenty-one patients were included; the choledocholithiasis prevalence was 59.9%. The predictors were age > 55 years, pancreatitis, cholangitis, cirrhosis, alkaline phosphatase level of 125 - 250 or > 250 U/L, total bilirubin level > 4 mg/dL, common bile duct size > 6 mm, and common bile duct stone detection. Pancreatitis and cirrhosis each had a negative score. The sum of scores was -4.5 to 28.5. Patients were categorized into three risk groups: low-intermediate (score ≤ 5), intermediate (score 5.5 - 14.5), and high (score ≥ 15). Positive likelihood ratios were 0.16 and 3.47 in the low-intermediate and high-risk groups, respectively. The model had an area under the receiver operating characteristic curve of 0.80 (95% confidence interval: 0.76, 0.83) and was well-calibrated; it exhibited better statistical suitability to the high-prevalence population, compared to current guidelines.

CONCLUSIONS

Our scoring model had good predictive ability for choledocholithiasis in patients with relevant clinical manifestations. Consideration of other factors is necessary for clinical application, particularly regarding the availability of expert physicians and specialized equipment.

Performance analysis of machine learning algorithms and screening formulae for β-thalassemia trait screening of Indian antenatal women

BACKGROUND

Currently, more than forty discrimination formulae based on red blood cell (RBC) parameters and some supervised machine learning algorithms (MLAs) have been recommended for β-thalassemia trait (BTT) screening. The present study was aimed to evaluate and compare the performance of 26 such formulae and 13 MLAs on antenatal woman data with a recently developed formula SCS_BTT, which is available for evaluation in over seventy countries as an Android app, called SUSOKA[16].

METHODS

A diagnostic database of 2942 antenatal females were collected from PGIMER, Chandigarh, India and was used for this analysis. The data set consists of hypochromic microcytic anemia, BTT, Hemoglobin E trait, double heterozygote for Hemoglobin S and BTT, heterozygote for Hemoglobin D Punjab and normal subjects. Performance of the formulae and the MLAs were assessed by Sensitivity, Specificity, Youden's Index, and AUC-ROC measures. A final recommendation was made from the ranking obtained through two Multiple Criteria Decision-Making (MCDM) techniques, namely, Simultaneous Evaluation of Criteria and Alternatives (SECA) and TOPSIS.

RESULTS

It was observed that Extreme Learning Machine (ELM) and Gradient Boosting Classifier (GBC) showed maximum Youden's index and AUC-ROC measures compared to all discriminating formulae. Sensitivity remains maximum for SCS_BTT. K-means clustering and the ranking from MCDM methods show that SCS_BTT, Shine & Lal and Ravanbakhsh-F4 formula ensures higher performance among all formulae. The discriminant power of some MLAs and formulae was found considerably lower than that reported in original studies.

CONCLUSION

Comparative information on MLAs can aid researchers in developing new discriminating formulae that simultaneously ensure higher sensitivity and specificity. More multi-centric verification of the formulae on heterogeneous data is indispensable. SCS_BTT and Shine & Lal formula, and ELM and GBC are recommended for screening BTT based on MCDM. SCS_BTT can be used with certainty as a tangible cost-saving screening tool for mass screening for antenatal women in India and other countries.

Adjuvanted versus non-adjuvanted standard-dose influenza vaccines in preventing all-cause hospitalizations in the elderly: a cohort study with nested case-control analyses over 18 influenza seasons

BACKGROUND

The higher effectiveness of adjuvanted trivalent influenza vaccine (aTIV) versus non-adjuvanted (na) formulations in preventing all-cause hospitalization has been demonstrated for a single influenza season and in institutionalized elderly only. This study evaluated the relative vaccine effectiveness for aTIV vs. non-adjuvanted trivalent (naTIV) and/or quadrivalent (naQIV) influenza vaccines in preventing all-cause hospitalizations across 18 influenza seasons in primary care.

RESEARCH DESIGN AND METHODS

Using Health Search Database, a nested case-control analysis was conducted in a cohort of older adults being vaccinated with aTIV or naTIV/naQIV. Conditional logistic regression was adopted to estimate the odds ratio (OR) of all-cause hospitalizations occurred during the epidemic period.

RESULTS

Of 58,252 patients vaccinated with aTIV and naTIV/naQIV for the first time, 2,504 cases of all-cause hospitalization (3.46 per 1,000 person-weeks) during the 18 influenza seasons were identified. Compared with naTIV/naQIV, aTIV was associated with a 12% reduced the odds of all-cause hospitalizations (OR 0.88; 95% CI: 0.80-0.98).

CONCLUSIONS

In an 18-season cohort of older adults, aTIV reduced the risk of all-cause hospitalizations when compared with naTIV/naQIV. Our findings confirm additional benefits for adjuvanted influenza vaccines in older adults.

Automatic recognition of parasitic products in stool examination using object detection approach

Background

Object detection is a new artificial intelligence approach to morphological recognition and labeling parasitic pathogens. Due to the lack of equipment and trained personnel, artificial intelligence innovation for searching various parasitic products in stool examination will enable patients in remote areas of undeveloped countries to access diagnostic services. Because object detection is a developing approach that has been tested for its effectiveness in detecting intestinal parasitic objects such as protozoan cysts and helminthic eggs, it is suitable for use in rural areas where many factors supporting laboratory testing are still lacking. Based on the literatures, the YOLOv4-Tiny produces faster results and uses less memory with the support of low-end GPU devices. In comparison to the YOLOv3 and YOLOv3-Tiny models, this study aimed to propose an automated object detection approach, specifically the YOLOv4-Tiny model, for automatic recognition of intestinal parasitic products in stools.

Methods

To identify protozoan cysts and helminthic eggs in human feces, the three YOLO approaches; YOLOv4-Tiny, YOLOv3, and YOLOv3-Tiny, were trained to recognize 34 intestinal parasitic classes using training of image dataset. Feces were processed using a modified direct smear method adapted from the simple direct smear and the modified Kato-Katz methods. The image dataset was collected from intestinal parasitic objects discovered during stool examination and the three YOLO models were trained to recognize the image datasets.

Results

The non-maximum suppression technique and the threshold level were used to analyze the test dataset, yielding results of 96.25% precision and 95.08% sensitivity for YOLOv4-Tiny. Additionally, the YOLOv4-Tiny model had the best AUPRC performance of the three YOLO models, with a score of 0.963.

Conclusion

This study, to our knowledge, was the first to detect protozoan cysts and helminthic eggs in the 34 classes of intestinal parasitic objects in human stools.

The discriminant validity of single-question assessments of subjective cognitive complaints in an Asian older adult population

Objective

To compare the discriminant validity of three different single-question assessments of subjective cognitive complaints (SCC) for dementia in a community-based older adult population in Singapore.

Methods

Eligible older adults aged ≥60 were recruited into phase I for identifying those who require further assessment using the Abbreviated Mental Test (AMT) and progressive forgetfulness question (PFQ). Participants who failed either tests entered phase II and were administered various single-question assessments of SCC, such as the 8th question on the patient Ascertain Dementia 8 (AD8-8_pt), informant AD8 (AD8-8_info), and the 10th item on the Geriatric Depression Scale (GDS-10), followed by the Montreal Cognitive Assessment (MoCA) and a formal neuropsychological battery to identify the participant’s cognitive status by a research diagnosis and DSM-IV criteria. Differences in characteristics among diagnostic groups were compared. All discriminatory indices (sensitivity, specificity, positive, and negative predictive values, overall accuracy) for these single-question assessments and their combinations with the MoCA were calculated and reported to confirm their discriminant validity in identifying the existence of subjective complaints and objective impairment.

Results

A total of 3,780 participants were assessed at phase I, of which 957 entered and completed phase II. Of whom, 911 were dementia-free and 46 had dementia. The MoCA (13/14) displayed good sensitivity (95.6%), specificity (81.5%), and overall accuracy (82.1%) for dementia detection. The GDS-10 and AD8-8_pt showed poor discriminant validity, while the AD8-8_info had the highest specificity (83.2%) and the greatest overall accuracy (82.5%) for dementia. Compensatory combination of the AD8-8_info with MoCA, the sensitivity and positive predictive values were optimized (100%), while the conjunctive combination of two tools achieved excellent specificity (96.3%) and overall accuracy (94.8%) in discriminating dementia patients.

Conclusion and implications

Combining a reliable single-question SCC assessment with an objective tool can efficiently discriminate dementia patients from healthy older adults in the community.

Assessment of the Wisconsin Criteria at a Level I Trauma Center

ABSTRACT

The Wisconsin Criteria was developed for physicians evaluating facial trauma to determine the likelihood of facial fractures. Subsequent studies have not consistently validated these criteria. This study seeks to validate the Wisconsin Criteria and determine its utility in predicting operative facial fractures.Retrospective chart review of the trauma database registry at a Level I Trauma Center was conducted from September 2011 to May 2019. Adult patients who had a complete facial examination by otolaryngology or plastic surgery as well as a head computed tomography scan completed, were included. Fisher exact test was utilized for statistical analysis (P < 0.05) and positive predictive value, and negative predictive value (NPV) were calculated with a 95% confidence interval.After screening, 546 patients met eligibility, 448 had at least 1 finding of the Wisconsin Criteria, and 472 patients had facial fractures. The sensitivity of the Wisconsin Criteria for determining the presence of a facial fracture was 86.23%, the specificity was 44.59%, and the NPV was 33.67% (P < .0001). Malocclusion was the criterion most specific in determining if a facial fracture was present (98.65%), and Glasgow Coma Score < 14 was the least specific (67.57%).The Wisconsin Criteria did aid in the identification of facial fractures in trauma patients with a comparable sensitivity, higher specificity, and much lower NPV than originally described. Further investigation should be done to validate the criteria in other large trauma centers.

The Ability of Clinical Decision Rules to Detect Peripheral Arterial Disease: A Narrative Review

Peripheral arterial disease (PAD) is a common cause of lower extremity wound. Consequently, PAD leads to a cause of leg amputation nowadays, especially in diabetic patients. In general practice (GP), confrontation with PAD prevention is a challenge. In general, ankle-brachial index (ABI) measurement can be used as a PAD diagnostic tool, but this takes some time. The tool is not generally available and this need to train healthcare workers to perform. Multiple independent predictors developed the diagnostic prediction model known as clinical decision rules (CDRs) to identify patients with high-risk PAD. This might therefore limit the number of patients (only high-risk patients) to refer for ABI evaluation. This narrative review summarized existing CDRs for PAD.

Evaluation of the American Society of Gastrointestinal Endoscopy 2019 and the European Society of Gastrointestinal Endoscopy guidelines' performances for choledocholithiasis prediction in clinically suspected patients: A retrospective cohort study

Background and Aim

The American Society of Gastrointestinal Endoscopy (ASGE) and the European Society of Gastrointestinal Endoscopy (ESGE) have published guidelines for choledocholithiasis. However, the guidelines were formulated using data from a large number of patients with no to low risk of common bile duct (CBD) stones. This study aimed to assess the guidelines' predictive performance in a population with a high frequency of stones.

Methods

Data for three choledocholithiasis standard reference tests were retrospectively reviewed from January 2019 to June 2021. Clinical parameters were used to categorize patients into risk groups according to the guidelines, and then the guidelines' predictive abilities were calculated.

Results

Among 1185 patients, 521 were included. The stone prevalence was 61.0% (n = 318). Twelve (2.3%), 146 (28.0%), and 363 (69.7%) patients were classified into low‐, intermediate‐, and high‐risk groups according to the ASGE guidelines, and 30 (5.8%), 149 (28.6%), and 342 (65.6%) according to the ESGE guidelines. Focusing on the high‐risk group, the ASGE guidelines had a positive predictive value of 73.6 and a positive likelihood ratio of 1.78. The ESGE guidelines had a positive predictive value of 73.7 and positive likelihood ratio of 1.79. Both guidelines had equivalent areas under the receiver operating characteristic curve of 0.69 (95% confidence interval [CI]: 0.65–0.73) and 0.68 (95% CI: 0.64–0.72), respectively.

Conclusion

In the high‐risk group, the guidelines increased the chance of detecting choledocholithiasis by approximately 10% (61.0% prevalence to 73.6 and 73.7% positive predictive value). However, statistically, the guidelines had marginal discriminative performance in a population with high stone prevalence.

Predicting access to healthful food retailers with machine learning

Many U.S. households lack access to healthful food and rely on inexpensive, processed food with low nutritional value. Surveying access to healthful food is costly and finding the factors that affect access remains convoluted owing to the multidimensional nature of socioeconomic variables. We utilize machine learning with census tract data to predict the modified Retail Food Environment Index (mRFEI), which refers to the percentage of healthful food retailers in a tract and agnostically extract the features of no access-corresponding to a "food desert" and low access-corresponding to a "food swamp." Our model detects food deserts and food swamps with a prediction accuracy of 72% out of the sample. We find that food deserts and food swamps are intrinsically different and require separate policy attention. Food deserts are lightly populated rural tracts with low ethnic diversity, whereas swamps are predominantly small, densely populated, urban tracts, with more non-white residents who lack vehicle access. Overall access to healthful food retailers is mainly explained by population density, presence of black population, property value, and income. We also show that our model can be used to obtain sensible predictions of access to healthful food retailers for any U.S. census tract.

The role of core needle biopsy in parotid glands lesions with inconclusive fine needle aspiration

OBJECTIVES

Usefulness of preoperative tissue sampling and pathology diagnoses in parotid gland tumors were accepted worldwide. We investigate the role of CNB in the parotid gland lesions when FNA results are inconclusive.

METHODS

We conducted a cross sectional study to evaluate CNB results from fifty-two patients with history of parotid gland lesion and inconclusive previous fine needle aspiration.

RESULTS

In this study, 45 out of 52 CNB results determined definite histological subtype diagnosis. Four CNB reports were inconclusive and three CNB results were compatible with malignancies, but did not define definite diagnosis. Calculated sensitivity for diagnosis of malignancy was 96% and specificity was 85%. Negative predictive value, positive predictive value, and accuracy for CNB in detecting malignancy, were calculated 94%, 90%, and 92%, respectively.

CONCLUSIONS

When FNA is not conclusive, CNB may be used as a precise method to evaluate the parotid gland lesions.

How do the M4 and M6 models perform in an Australian pregnancy of unknown location population?

BACKGROUND

The diagnosis of a pregnancy of unknown location (PUL) is made when there is an elevated serum β human chorionic gonadotropin (βhCG) and no pregnancy on transabdominal and transvaginal ultrasound. Most of these pregnancies end as intra-uterine pregnancies or unsuccessful pregnancies and can be safely managed expectantly. However, up to 20% of these women will have an ectopic pregnancy. Several mathematical models, including the M4 and M6 protocols, have been developed using biochemical markers to triage PUL presentations. This rationalises numbers of tests and visits made without compromising safety and allowing timely intervention.

AIMS

We aimed to externally validate the M4 and M6 models in an Australian tertiary early pregnancy assessment service (EPAS).

MATERIALS AND METHODS

We performed a retrospective single-centre cohort study across five years. Our study population included all women attending our EPAS with a PUL who had at least two serum βhCG levels and one progesterone level measured. The M4 and M6 models were retrospectively applied.

RESULTS

Of the 360 women in the study population, there were 26 confirmed ectopic pregnancies (7.2%) and six persisting PULs (2%). The M4 model had a sensitivity and specificity of 72%. The M6P model had a sensitivity of 91% and specificity of 63%. The M6P misclassified two ectopic pregnancies into the low-risk group, compared with seven in the M4 model.

CONCLUSIONS

The M6P model has the highest sensitivity of the three models and a negative predictive value of 99%. These numbers are comparable to the original United Kingdom population. Further prospective validation is planned.

Classification of myocardial infarction based on hybrid feature extraction and artificial intelligence tools by adopting tunable-Q wavelet transform (TQWT), variational mode decomposition (VMD) and neural networks

Cardiovascular diseases (CVD) is the leading cause of human mortality and morbidity around the world, in which myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. Currently, electrocardiogram (ECG) is widely used by the clinicians to diagnose MI patients due to its inexpensiveness and non-invasive nature. Pathological alterations provoked by MI cause slow conduction by increasing axial resistance on coupling between cells. This issue may cause abnormal patterns in the dynamics of the tip of the cardiac vector in the ECG signals. However, manual interpretation of the pathological alternations induced by MI is a time-consuming, tedious and subjective task. To overcome such disadvantages, computer-aided diagnosis techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on hybrid feature extraction and artificial intelligence tools. Tunable quality factor (Q-factor) wavelet transform (TQWT), variational mode decomposition (VMD) and phase space reconstruction (PSR) are utilized to extract representative features to form cardiac vectors with synthesis of the standard 12-lead and Frank XYZ leads. They are combined with neural networks to model, identify and detect abnormal patterns in the dynamics of cardiac system caused by MI. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. Second, this vector is decomposed into a set of frequency subbands with a number of decomposition levels by using the TQWT method. Third, VMD is employed to decompose the subband of the 4-dimensional cardiac vector into different intrinsic modes, in which the first intrinsic mode contains the majority of the cardiac vector's energy and is considered to be the predominant intrinsic mode. It is selected to construct the reference variable for analysis. Fourth, phase space of the reference variable is reconstructed, in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional (3D) PSR together with Euclidean distance (ED) has been utilized to derive features, which demonstrate significant difference in cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Fifth, cardiac system dynamics can be modeled and identified using neural networks, which employ the ED of 3D PSR of the reference variable as the input features. The difference of cardiac system dynamics between healthy control and MI cardiac vector is computed and used for the detection of MI based on a bank of estimators. Finally, data sets, which include conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls from PTB diagnostic ECG database, are used for evaluation. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 97.98%. Currently, ST segment evaluation is one of the major and traditional ways for the MI detection. However, there exist weak or even undetectable ST segments in many ECG signals. Since the proposed method does not rely on the information of ST waves, it can serve as a complementary MI detection algorithm in the intensive care unit (ICU) of hospitals to assist the clinicians in confirming their diagnosis. Overall, our results verify that the proposed features may satisfactorily reflect cardiac system dynamics, and are complementary to the existing ECG features for automatic cardiac function analysis.

Validation of a Novel Electronic Device for Medication Adherence Monitoring of Ambulatory Patients

Several methods exist for measuring medication adherence. The Time4Med^TM device (Adherence Innovations, Hong Kong) is a small, electronic card to affix on medication packaging that records date and time of intakes when a button is pushed. We aimed to validate the device with an emphasis on polypharmacy. Twenty volunteers used Time4Med^TM devices with a virtual thrice daily intake over 14 days. Diary-recorded date and time were compared to electronically-stored events. Functionality, reliability and recovery for different stress conditions were calculated. User‘s acceptability was assessed with the System Usability Scale (SUS). Eleven elderly outpatients (mean age 80.2 ± 8.1 years) taking >3 medications daily used the device over 4 weeks. Volunteers logged 847 events. Functionality (100%), sensitivity (94.9%), specificity (99.4%) and recovery (100%) were high. Dropping the smart card and storing it in a refrigerator caused either the recording of false events or no recording at all. The mean SUS score was 82.6 (SD 14.8), demonstrating excellent acceptability. Satisfaction was very high for volunteers and patients, except for pushing the button. Time4Med^TM devices are highly accurate in recording, retaining and delivering electronic data of multiple medication intake. They are well accepted by elderly patients. They can be recommended in clinical studies and for practitioners who desire to elucidate adherence patterns of ambulatory patients.

A concise evidence-based physical examination for diagnosis of acromioclavicular joint pathology: a systematic review

OBJECTIVES

The clinical examination of the shoulder joint is an undervalued diagnostic tool for evaluating acromioclavicular (AC) joint pathology. Applying evidence-based clinical tests enables providers to make an accurate diagnosis and minimize costly imaging procedures and potential delays in care. The purpose of this study was to create a decision tree analysis enabling simple and accurate diagnosis of AC joint pathology.

METHODS

A systematic review of the Medline, Ovid and Cochrane Review databases was performed to identify level one and two diagnostic studies evaluating clinical tests for AC joint pathology. Individual test characteristics were combined in series and in parallel to improve sensitivities and specificities. A secondary analysis utilized subjective pre-test probabilities to create a clinical decision tree algorithm with post-test probabilities.

RESULTS

The optimal special test combination to screen and confirm AC joint pathology combined Paxinos sign and O'Brien's Test, with a specificity of 95.8% when performed in series; whereas, Paxinos sign and Hawkins-Kennedy Test demonstrated a sensitivity of 93.7% when performed in parallel. Paxinos sign and O'Brien's Test demonstrated the greatest positive likelihood ratio (2.71); whereas, Paxinos sign and Hawkins-Kennedy Test reported the lowest negative likelihood ratio (0.35).

CONCLUSION

No combination of special tests performed in series or in parallel creates more than a small impact on post-test probabilities to screen or confirm AC joint pathology. Paxinos sign and O'Brien's Test is the only special test combination that has a small and sometimes important impact when used both in series and in parallel. Physical examination testing is not beneficial for diagnosis of AC joint pathology when pretest probability is unequivocal. In these instances, it is of benefit to proceed with procedural tests to evaluate AC joint pathology. Ultrasound-guided corticosteroid injections are diagnostic and therapeutic. An ultrasound-guided AC joint corticosteroid injection may be an appropriate new standard for treatment and surgical decision-making.

LEVEL OF EVIDENCE

II - Systematic Review.

A practical, evidence-based, comprehensive (PEC) physical examination for diagnosing pathology of the long head of the biceps

BACKGROUND

Clinical examination of the shoulder joint has gained attention as clinicians aim to use an evidence-based examination of the biceps tendon, with the desire for a proper diagnosis while minimizing costly imaging procedures. The purpose of this study is to create a decision tree analysis that enables the development of a clinical algorithm for diagnosing long head of biceps (LHB) pathology.

METHODS

A literature review of Level I and II diagnostic studies was conducted to extract characteristics of clinical tests for LHB pathology through a systematic review of PubMed, Medline, Ovid, and Cochrane Review databases. Tests were combined in series and parallel to determine sensitivities and specificities, and positive and negative likelihood ratios were determined for each combination using a subjective pretest probability. The "gold standard" for diagnosis in all included studies was arthroscopy or arthrotomy.

RESULTS

The optimal testing modality was use of the uppercut test combined with the tenderness to palpation of the biceps tendon test. This combination achieved a sensitivity of 88.4% when performed in parallel and a specificity of 93.8% when performed in series. These tests used in combination optimize post-test probability accuracy greater than any single individual test.

CONCLUSION

Performing the uppercut test and biceps groove tenderness to palpation test together has the highest sensitivity and specificity of known physical examinations maneuvers to aid in the diagnosis of LHB pathology compared with diagnostic arthroscopy (practical, evidence-based, comprehensive examination). A decision tree analysis aides in the practical, evidence-based, comprehensive examination diagnostic accuracy post-testing based on the ordinal scale pretest probability.

Clinical utility of the Vanderbilt ADHD diagnostic parent rating scale comorbidity screening scales

OBJECTIVE

To evaluate the clinical utility of the cutoff recommendations for the Vanderbilt ADHD Diagnostic Parent Rating Scale (VADPRS) comorbidity screening scales provided by the American Academy of Pediatrics/National Initiative for Children's Healthcare Quality and to examine alternative cutoff strategies for identifying and ruling out disorders commonly comorbid with attention-deficit/hyperactivity disorder.

METHODS

A sample of 215 children (142 with attention-deficit/hyperactivity disorder), ages 7 to 11 years, participated in the study. Parents completed the VADPRS and were administered a diagnostic interview to establish diagnoses of oppositional defiant disorder (ODD), conduct disorder (CD), anxiety, and depression. The clinical utility of the VADPRS comorbidity screening scales were examined.

RESULTS

The recommended American Academy of Pediatrics/National Initiative for Children's Healthcare Quality cutoff strategies did not have adequate clinical utility for identifying or ruling out comorbidities, with the exception of the VADPRS ODD cutoff strategy, which reached adequate levels for ruling out a diagnosis of ODD. An alternative cutoff approach using total sum scores was superior to the recommended cutoff strategies across all diagnoses in terms of ruling out a diagnosis, and this was particularly evident for anxiety/depression. Several individual items on the ODD and CD scales also had acceptable clinical utility for ruling in diagnoses.

CONCLUSIONS

The VADPRS comorbidity screening scales may be helpful in determining which children likely do not meet diagnostic criteria for ODD, CD, anxiety, or depression. This study suggests that using a total sum score provides the greatest clinical utility for each of these comorbidities and demonstrates the need for further research examining the use of dimensional assessment strategies in diagnostic decision making.

The use of Mood Disorder Questionnaire, Hypomania Checklist-32 and clinical predictors for screening previously unrecognised bipolar disorder in a general psychiatric setting

Bipolar disorder is often unrecognised and misdiagnosed in the general psychiatric setting. This study compared the psychometric properties of the Mood Disorder Questionnaire (MDQ) and the Hypomania Checklist-32 (HCL-32), examined the clinical predictors of bipolar disorder and determined the best approach for screening previously unrecognised bipolar disorder in a general psychiatric clinic. A random sample of 340 non-psychotic outpatients with no previous diagnosis of bipolar disorder completed the MDQ and HCL-32 during their scheduled clinic visits. Mood and alcohol/substance use disorders were reassessed using a telephone-based Structured Clinical Interview for DSM-IV. We found that the HCL-32 had better psychometric performance and discriminatory capacity than the MDQ. The HCL-32's internal consistency and 4-week test-retest reliability were higher. The area under the curve was also greater than that of the MDQ at various clustering and impairment criteria. The optimal cut-off of the MDQ was co-occurrence of four symptoms with omission of the impairment criterion; for the HCL-32, it was 11 affirmative responses. Multivariable logistic regression found that bipolar family history was associated with an increased risk of bipolar disorder (odds ratio=4.93). The study showed that simultaneous use of the HCL-32 and bipolar family history was the best approach for detecting previously unrecognised bipolar disorder.

Development and evaluation of an office ergonomic risk checklist: ROSA--rapid office strain assessment

The Rapid Office Strain Assessment (ROSA) was designed to quickly quantify risks associated with computer work and to establish an action level for change based on reports of worker discomfort. Computer use risk factors were identified in previous research and standards on office design for the chair, monitor, telephone, keyboard and mouse. The risk factors were diagrammed and coded as increasing scores from 1 to 3. ROSA final scores ranged in magnitude from 1 to 10, with each successive score representing an increased presence of risk factors. Total body discomfort and ROSA final scores for 72 office workstations were significantly correlated (R = 0.384). ROSA final scores exhibited high inter- and intra-observer reliability (ICCs of 0.88 and 0.91, respectively). Mean discomfort increased with increasing ROSA scores, with a significant difference occurring between scores of 3 and 5 (out of 10). A ROSA final score of 5 might therefore be useful as an action level indicating when immediate change is necessary. ROSA proved to be an effective and reliable method for identifying computer use risk factors related to discomfort.

Significance of ankle brachial index and collaterals for prediction of critical limb ischemia in infrainguinal peripheral arterial occlusive disease

PURPOSE

Chronic limb ischemia (CLI) is a clinical diagnosis, but should be approved by technical tests like the ankle-brachial index (ABI). Although the ABI is well established, less is known about the influence of collateralization on clinical stage.

MATERIAL/METHODS

Magnetic resonance angiographies (MRA) of 129 lower extremities were searched for morphological changes and for the number of collateral vessels according to Sorlie. Ankle pressures were recorded as higher (APmax) and lower (APmin) systolic blood pressures of the two ankle arteries with consecutive calculation of ABImax and ABImin.

RESULTS

In comparisons of ROC curves, APmax (AUC=0.749) did significantly better as a prognostic marker than APmin (AUC=0.642) (p=0.005) and ABImax (AUC=0.744) did significantly better than ABImin (AUC=0.650) (p=0.019). APmax showed a positive likelihood ratio (+LR) of 5.79 and a negative likelihood ratio (-LR) of 0.47 (cutoff ≤55 mmHg). For the number of collateral vessels a +LR 2.27 and a -LR of 0.09 and in patients with an APmax ≤55 mmHg a +LR of 5.50 and a -LR of 0.00 were calculated (cutoff ≤1 collateral vessel).

CONCLUSION

Whereas APmax is more eligible for verification of CLI, collateral count is better in exclusion of CLI. Both seem to be independent factors for validating the clinical diagnosis of CLI.

HIERARCHICAL ANALYSIS AND CLASSIFICATION OF ASYMPTOMATIC AND KNEE OSTEOARTHRITIS GAIT PATTERNS USING A WAVELET REPRESENTATION OF KINETIC DATA AND THE NEAREST NEIGHBOR CLASSIFIER

The purpose of this study is twofold: (1) to develop a classification method to distinguish between asymptomatic (AS) and knee osteoarthritis (OA) gait patterns using ground reaction force (GRF) measurements, and (2) to investigate OA severity within OA gait patterns. Features were first extracted from the GRF vectors to be used for classification. We investigated a two-level hierarchical classification and analysis method using the nearest neighbor rule. At the first level, the GRF data were classified into two classes: AS and OA. At the second level, the GRF data of OA patients were classified according to the pathology severity. The OA patients were grouped into two OA severity categories according to the Kellgren and Lawrence (KL) scale: KL 1 and KL 2 for one category, and KL 3 and KL 4 for the other. Experiments were conducted using data of 42 cases, 16 AS and 26 pathological. The method discriminated between AS and OA subjects with an accuracy of 38 of 42 cases, and assessed the severity correctly with an accuracy of 20 of 26 cases. These results demonstrated the validity of both, the feature and the classifier, for automatic classification of AS and knee OA gait patterns and for analysis of OA severity.

Clinical versus histological diagnosis of diabetic nephropathy--is renal biopsy required in type 2 diabetic patients with renal disease?

OBJECIVES

The clinical and histological diagnosis (gold standard) of diabetic nephropathy (dNP) and vascular nephropathy (vNP) were compared in type 2 diabetic patients with end-stage renal disease (ESRD).

AIM

The aim of the study was to investigate indication for renal biopsy in type 2 diabetic patients with renal disease.

DESIGN

Retrospective study in diabetic patients with clinical and histological diagnosis of renal disease.

PATIENTS AND METHODS

Eighty-four patients with type 2 diabetes and ESRD were investigated. Histological findings of the kidneys were available in all patients, 14 had undergone a renal biopsy before their first dialysis while a post-mortem kidney investigation was performed in 70 subjects. According to the histological findings, 66 patients had dNP and 18 subjects had vNP. The histological diagnosis was compared with the clinical diagnosis, and the sensitivity as well as the specificity of the clinical diagnosis of dNP and vNP were calculated.

RESULTS

The clinical diagnosis was not identical with the histological diagnosis in 10 cases. In the dNP group the diagnosis was 4 false positive and 3 false negative as in the vNP group 1 false positive and 2 false negative. The sensitivity of clinical diagnosis was 95% for dNP and 89% for vNP. Specificity was 78% for dNP and 97% for vNP.

CONCLUSION

The sensitivity of the clinical diagnosis is very high for dNP as well as vNP. A renal biopsy is not required in the majority of type 2 diabetic patients with ESRD, especially in patients who exhibit all criteria for clinical diagnosis.

Linking ambulance, emergency department and hospital admissions data: understanding the emergency journey

OBJECTIVE

To assess the accuracy of data linkage across the spectrum of emergency care in the absence of a unique patient identifier, and to use the linked data to examine service delivery outcomes in an emergency department (ED) setting.

DESIGN

Automated data linkage and manual data linkage were compared to determine their relative accuracy. Data were extracted from three separate health information systems: ambulance, ED and hospital inpatients, then linked to provide information about the emergency journey of each patient. The linking was done manually through physical review of records and automatically using a data linking tool (Health Data Integration) developed by the CSIRO (Commonwealth Scientific and Industrial Research Organisation). Match rate and quality of the linking were compared.

SETTING

10,835 patient presentations to a large, regional teaching hospital ED over a 2-month period (August - September 2007).

RESULTS

Comparison of the manual and automated linkage outcomes for each pair of linked datasets demonstrated a sensitivity of between 95% and 99%; a specificity of between 75% and 99%; and a positive predictive value of between 88% and 95%.

CONCLUSIONS

Our results indicate that automated linking provides a sound basis for health service analysis, even in the absence of a unique patient identifier. The use of an automated linking tool yields accurate data suitable for planning and service delivery purposes and enables the data to be linked regularly to examine service delivery outcomes.

Assessing insomnia in adolescents: comparison of Insomnia Severity Index, Athens Insomnia Scale and Sleep Quality Index

OBJECTIVES

To compare the psychometric properties of the Chinese versions of Insomnia Severity Index (ISI), Athens Insomnia Scale (AIS) and Sleep Quality Index (SQI) for assessment and screening of insomnia in adolescents.

METHODS

This is a school-based survey of 1516 adolescents aged 12-19 years. Sleep-wake habit questionnaire, ISI, AIS, SQI, Epworth Sleepiness Scale (ESS) and 12-item General Health Questionnaire (GHQ-12) were administered. Insomnia Interview Schedule was used to assess the severity of insomnia symptoms and DSM-IV-TR diagnosis of insomnia.

RESULTS

The Cronbach's alpha of ISI, AIS and SQI were 0.83, 0.81 and 0.65, respectively, and the 2-week test-retest reliability were 0.79, 0.80 and 0.72. All three scales had a 2-factor structure, and their scores were significantly correlated with sleep-wake variables, ESS and GHQ-12 scores, smoking and drinking habits, and academic performance. The areas under curve of ISI, AIS and SQI for detecting clinical insomnia were 0.85, 0.80 and 0.85, respectively. The optimal cut-offs for ISI, AIS and SQI were a total score of nine (sensitivity/specificity: 0.87/0.75), seven (sensitivity/specificity: 0.78/0.74) and five (sensitivity/specificity: 0.83/0.79), respectively.

CONCLUSION

The Chinese versions of ISI, AIS and SQI are reliable and valid instruments. The ISI and AIS appear to have better psychometric properties than the SQI.