Clinical Variables Research Articles

Lipoprotein (a) concentration as a risk factor for ischaemic stroke and its subtypes.

To investigate the relationship between serum lipoprotein (a) [Lp(a)] concentration and the risk of ischaemic stroke (IS) and its subtypes. Lp(a) plays a role in atherogenic, pro-thrombotic, and antifibrinolytic processes. Elevated plasma Lp(a) is a strong independent risk factor for the development and progression of atherosclerotic disease. The association between lipoproteins and IS is more complex than that reported for cardiovascular diseases, with inconsistent and contradictory results from epidemiological studies. 231 patients with acute IS (defined as cases) and 163 age- and sex-matched control subjects were included in this prospective case-control study. Demographic and clinical variables (i.e. age, sex, smoking, presence of chronic diseases and concomitant medication) and laboratory data (i.e. concentrations of total cholesterol, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, triglycerides, Lp(a), apolipoprotein A1, apolipoprotein B) were recorded. The mean age and the percentage of men did not significantly differ between groups. Compared to controls, there was a significantly higher percentage of cases reported with concomitant diseases: diabetes mellitus, myocardial infarction, ischaemic heart disease, peripheral arterial disease, and atrial fibrillation. The study showed a significantly higher serum Lp(a) concentration in cases than in control subjects (81.81 nmol/L [c.32.7 mg/dL] vs. 59.75 nmol/L [c.23.9 mg/dL]; p = 0.036) and found an association between Lp(a) levels stratified by quartiles and the risk for ischaemic stroke (Q1 [Lp(a) < 13 nmol/L] vs. Q4 [Lp(a) > 117 nmol/L]: OR 2.23; 95% CI 1.23-4.03; p = 0.008). A subgroup analysis based on the TOAST classification of IS also showed a significant association between Lp(a) value of more than 75 nmol/L (30 mg/dL) and the risk of large-artery atherosclerosis stroke compared to the controls (OR 2.4; 95% CI 1.39-3.93; p = 0.001), as well as a statistically non-significant association with other subtypes of IS. The influence of Lp(a) remained significant even after adjusting for established risk factors for IS (OR 1.99; 95% CI 1.05-3.76; p = 0.04; respectively for the large-artery atherosclerotic subtype: OR 2.54; 95% CI 1.39-4.67; p = 0.003). We found that Lp(a) is an independent risk factor for ischaemic stroke, and for the large-artery atherosclerotic subtype of ischaemic stroke.

Prognostic significance of MRI features in patients with solitary large hepatocellular carcinoma following surgical resection.

To assess the prognostic impact of preoperative MRI features on outcomes for single large hepatocellular carcinoma (HCC) (≥ 8 cm) after surgical resection. This retrospective study included 151 patients (mean age: 59.2 years; 126 men) with a single large HCC who underwent gadoxetic acid-enhanced MRI and surgical resection between 2008 and 2020. Clinical variables, including tumor markers and MRI features (tumor size, tumor margin, and the proportion of hypovascular component on hepatic arterial phase (AP) (≥ 50% vs. < 50% tumor volume) were evaluated. Cox proportional hazards model analyzed overall survival (OS), recurrence-free survival (RFS), and associated factors. Among 151 HCCs, 37.8% and 62.2% HCCs were classified as ≥ 50% and < 50% AP hypovascular groups, respectively. The 5- and 10-year OS and RFS rates in all patients were 62.0%, 52.6% and 41.4%, 38.5%, respectively. Multivariable analysis revealed that ≥ 50% AP hypovascular group (hazard ratio [HR] 1.7, p = 0.048), tumor size (HR 1.1, p = 0.006), and alpha-fetoprotein ≥ 400 ng/mL (HR 2.6, p = 0.001) correlated with poorer OS. ≥ 50% AP hypovascular group (HR 1.9, p = 0.003), tumor size (HR 1.1, p = 0.023), and non-smooth tumor margin (HR 2.1, p = 0.009) were linked to poorer RFS. One-year RFS rates were lower in the ≥ 50% AP hypovascular group than in the < 50% AP hypovascular group (47.4% vs 66.9%, p = 0.019). MRI with ≥ 50% AP hypovascular component and larger tumor size were significant factors associated with poorer OS and RFS after resection of single large HCC (≥ 8 cm). These patients require careful multidisciplinary management to determine optimal treatment strategies. Preoperative MRI showing a ≥ 50% arterial phase hypovascular component and larger tumor size can predict worse outcomes after resection of single large hepatocellular carcinomas (≥ 8 cm), underscoring the need for tailored, multidisciplinary treatment strategies. MRI features offer insights into the postoperative prognosis for large hepatocellular carcinoma. Hypovascular component on arterial phase ≥ 50% and tumor size predicted poorer overall survival and recurrence-free survival. These findings can assist in prioritizing aggressive and multidisciplinary approaches for patients at risk for poor outcomes.

Characteristics of forensic psychiatric patients with a neurocognitive disorder.

Emotional and behavioural disturbances accompanying neurocognitive disorders may sometimes lead to a criminal offence. Our knowledge of this specific forensic subpopulation is lagging behind the knowledge on, and attention for, 'classic' psychiatric disorders in forensic populations. To gain knowledge on the prevalence and characteristics of individuals with neurocognitive disorders in the forensic population. This retrospective database study uses an anonymised data-set of the National Database of penitentiary psychiatric centres (PPC) (N = 8391), which collects data on all patients admitted to one of the four PPCs (mental health clinics within the prison system) in The Netherlands since May 2013. Inclusion criterion for this study was the presence of a Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM-IV-TR) or Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) diagnostic code belonging to the category of neurocognitive disorders. A DSM-IV-TR or DSM-5 diagnostic code of a neurocognitive disorder was classified in 254 out of 8391 unique individuals, resulting in a prevalence of 3.0% in the total PPC population. The most prevalent diagnosis was unspecified neurocognitive disorder (59.1%). The neurocognitive disorder group significantly differed from a random control group from the database (n = 762) on demographic, clinical and criminological variables. The prevalence of neurocognitive disorders in this real-world clinical sample is remarkably lower than in two earlier studies in similar populations. Also remarkable is the relatively high prevalence of an unspecified neurocognitive disorder. These findings lead us to hypothesise that neurocognitive disorders may be underdiagnosed in this population. Forensic psychiatric settings should evaluate whether they have sufficient expertise available in neuropsychological assessment.

Endoscopic submucosal dissection for rectal neuroendocrine tumours: A multicentric retrospective study.

Endoscopic Submucosal Dissection (ESD) has been reported as a feasible and effective treatment for Rectal Neuroendocrine Tumours (R-NETs). However, most of the experience on the topic comes from retrospective tertiary centre from Eastern Asia. Data on ESD for R-NETs in Western centres are lacking. This is a retrospective study, including patients who underwent endoscopic resection of R-NETS by ESD between 2015 and 2020 in Western Centres. Important clinical variables such as demographic, size of R-NETs, histological type, presence of lymphovascular invasion or distant metastasis, completeness of the endoscopic resection, recurrence, and procedure related complications were recorded. 40 ESD procedure on R-NETs from 39 patients from 8 centres were included. Mean R-NETs size was 10.3 mm (SD 4.01). Endoscopic en-bloc resection was achieved in 39/40 ESD (97.5 %), R0 margin resection was obtained in 87.5 % (35/40) of the procedures, one patient was referred to surgery for lymphovascular invasion, two procedures (5 %) reported significant episodes of bleeding, whereas a perforation occurred in one case (1/40, 2.5 %) managed endoscopically. Recurrence occurred in 1 patient (2.5 %). ESD is an effective and safe treatment for R-NETs in western centres.

Clinical and health care utilization variables can predict 90-day hospital re-admission in adults with Crohn's disease for point of care risk evaluation.

Hospital re-admission for persons with Crohn's disease (CD) is a significant contributor to morbidity and healthcare costs. We derived prediction models of risk of 90-day re-hospitalization among persons with CD that could be applied at hospital discharge to target outpatient interventions mitigating this risk. We performed a retrospective study in persons with CD admitted between 2009 and 2016 for an acute CD-related indication. Demographic, clinical, and health services predictor variables were ascertained through chart review and linkage to administrative health databases. We derived and internally validated a multivariable logistic regression model of 90-day CD-related re-hospitalization. We selected the optimal probability cut-point to maximize Youden's index. There were 524 CD hospitalizations and 57 (10.9%) CD re-hospitalizations within 90 days of discharge. Our final model included hospitalization within the prior year (adjusted odds ratio [aOR] 3.27, 95% confidence interval [CI] 1.76-6.08), gastroenterologist consultation within the prior year (aOR 0.185, 95% CI 0.0950-0.360), intra-abdominal surgery during index hospitalization (aOR 0.216, 95% CI 0.0500-0.934), and new diagnosis of CD during index hospitalization (aOR 0.327, 95% CI 0.0950-1.13). The model demonstrated good discrimination (optimism-corrected c-statistic value 0.726) and excellent calibration (Hosmer-Lemeshow goodness-of-fit p-value 0.990). The optimal model probability cut point allowed for a sensitivity of 71.9% and specificity of 70.9% for identifying 90-day re-hospitalization, at a false positivity rate of 29.1% and false negativity rate of 28.1%. Demographic, clinical, and health services variables can help discriminate persons with CD at risk of early re-hospitalization, which could permit targeted post-discharge intervention.

Association of OPRM1 rs1799971, HTR1B rs6296 and COMT rs4680 polymorphisms with clinical phenotype among women with fibromyalgia.

To investigate the association between three selected pain polymorphisms and clinical, functional, sensory-related, psychophysical, psychological or cognitive variables in a sample of women with fibromyalgia (FMS). One hundred twenty-three (n = 123) women with FMS completed demographic (age, height, weight), clinical (years with pain, intensity of pain at rest and during daily living activities), functional (quality of life, physical function), sensory-related (sensitization-associated and neuropathic-associated symptoms), psychophysical (pressure pain thresholds), psychological (sleep quality, depressive and anxiety level) and cognitive (pain catastrophizing, kinesiophobia) variables. Those three genotypes of the OPRM1 rs1799971, HTR1B rs6296 and COMT rs4680 single nucleotide polymorphisms were obtained by polymerase chain reactions from no-stimulated whole saliva collection. No significant differences in demographic, clinical, functional, sensory-related, psychophysical, psychological and cognitive variables according to OPRM1 rs1799971, HTR1B rs6296 or COMT rs4680 genotype were identified in our sample of women with FMS. A multilevel analysis did not either reveal any significant gene-to-gene interaction between OPRM1 rs1799971 x HTR1B rs6296, OPRM1 rs1799971 x COMT rs4680 and HTR1B rs6296 x COMT rs4680 for any of the investigated outcomes. This study revealed that three single nucleotide polymorphisms, OPRM1 rs1799971, HTR1B rs6296 or COMT rs4680, mostly associated with chronic pain were not involved in phenotyping features of FMS. Potential gene-to-gene interaction and their association with clinical phenotype in women with FMS should be further investigated in future studies including large sample sizes.

Generation and external validation of a histological transformation risk model for patients with follicular lymphoma.

Follicular lymphoma (FL) is the most frequent indolent lymphoma. 10-15% of patients suffer histological transformation (HT) to a more aggressive lymphoma, usually diffuse large B cell lymphoma (DLBCL). This study aimed to validate and improve a genetic risk model to predict HT at diagnosis. We collected mutational data from diagnosis biopsies of 64 FL patients. We combined them with the data from a previously published cohort (total n = 104, 62 from non-transformed, and 42 from patients who did transform to DLBCL). This combined cohort was used to develop a nomogram to estimate the risk of HT. Prognostic mutated genes and clinical variables were assessed using Cox regression analysis to generate a risk model. The model was internally validated by bootstrapping and externally validated in an independent cohort. Its performance was evaluated using a concordance index and a calibration curve. The clinicogenetic nomogram included the mutational status of three genes (HIST1HE1, KMT2D, and TNFSR14) and high-risk FLIPI and predicted HT with a concordance index of 0.746. Patients were classified as being at low or high risk of transformation. The probability HT function at 24 months was 0.90 in the low-risk group vs. 0.51 in the high-risk group and, at 60 months, 0.69 vs. 0.15, respectively. In the external validation cohort, the probability HT function in the low-risk group was 0.86 vs. 0.54 in the high-risk group at 24 months, and 0.71 vs. 0.32 at 60 months. The concordance index in the external cohort was 0.552. In conclusion, we propose a clinicogenetic risk model to predict FL HT to DLBLC, combining genetic alterations in HIST1H1E, KMT2D, and TNFRSF14 genes and clinical features (FLIPI) at diagnosis. This model could improve the management of FL patients and allow treatment strategies that would prevent or delay transformation.

Immunohistochemical profile of triple negative breast cancers: SOX10 and AR dual negative tumors have worse outcomes.

Triple negative breast cancer (TNBC) refers to an estrogen receptor negative, progesterone receptor negative, and HER2-negative breast cancer. Although accepted as a clinically valid category, TNBCs are heterogeneous at the histologic, immunohistochemical, and molecular levels. Gene expression profiling studies have molecularly classified TNBCs into multiple groups, but the prognostic significance is unclear except for a relatively good prognosis for the luminal androgen receptor (LAR) subtype. Immunohistochemistry (IHC) has been used as a surrogate for basal and luminal subtypes within TNBC, but prognostication of TNBC using IHC is not routinely performed. We aimed to study immunophenotypic correlations in a well-annotated cohort of consecutive TNBCs, excluding post-neoadjuvant chemotherapy cases. Tissue microarrays were constructed from a total of 245 TNBC cases. IHC stains were performed and consisted of luminal (AR, INPP4B), basal (SOX10, Nestin, CK5, EGFR), and diagnostic (GCDFP15, mammaglobin, GATA3, TRPS1) markers. Survival analysis was performed to assess the significance of clinical pathologic variables including age, histology, grade, lymphovascular invasion [LVI], Nottingham prognostic index [NPI] category, AJCC stage, stromal tumor-infiltrating lymphocytes at 10% increment, CD8+ T-cell count, Ki-67 index, PD-L1 status, and chemotherapy along with the results of IHC markers. Apocrine tumors show prominent reactivity for luminal markers and GCDFP15 while no special type carcinomas are often positive for basal markers. TRPS1 is a sensitive marker of breast carcinoma but shows low or no expression in apocrine tumors. High AJCC stage, lack of chemotherapy, and dual SOX10/AR negativity are associated with worse outcomes on univariable as well as multivariable analysis. LVI and higher NPI category were associated with worse outcomes on univariable but not multivariable analysis. The staining for IHC markers varies based on tumor histology which may be considered in determining breast origin. Notably, we report that SOX10/AR dual negative status in TNBC is associated with a worse prognosis along with AJCC stage, and chemotherapy status.

Incidence and predictors of intracranial hemorrhage after intravenous thrombolysis with tenecteplase.

Despite its increasing use, there are limited data on the risk of intracranial hemorrhage (ICH) after intravenous thrombolysis with tenecteplase in the setting of acute ischemic stroke. Our aim was to investigate the incidence and predictors of ICH after tenecteplase administration. We reviewed data from the prospective ongoing multicenter TETRIS (Tenecteplase Treatment in Ischemic Stroke) registry. Patients with available day-1 imaging were included in this study. Clinical, imaging and biological variables were collected. Follow-up imaging performed 24 h after IVT was locally reviewed by senior neuroradiologists and neurologists. The incidence of parenchymal hematoma (PH) and any ICH were investigated. Potential predictors of PH and any ICH were assessed in multivariable logistic regressions. Subgroup analyses focusing on patients intended for endovascular treatment were performed. PH and any ICH occurred in 126/1321 (incidence rate: 9.5%, 95% CI 8.1-11.2) and 521/1321 (39.4%, 95% CI 36.8-42.1) patients, respectively. Symptomatic ICH was observed in 77/1321 (5.8%; 95% CI 4.7-7.2). PH occurrence was significantly associated with poorer functional outcomes (p < 0.0001) and death (p < 0.0001) after 3 months. Older age (aOR = 1.03; 95% CI 1.01-1.05), male gender (aOR = 2.07; 95% CI 1.28-3.36), a history of hypertension (aOR = 2.08; 95% CI 1.19-3.62), a higher baseline NIHSS (aOR = 1.07; 95% CI 1.03-1.10) and higher admission blood glucose level (aOR = 1.12; 95% CI 1.05-1.19) were independently associated with PH occurrence. Similar associations were observed in the subgroup of patients intended for endovascular treatment. We quantified the incidence of ICH after IVT with tenecteplase in a real-life prospective registry and determined independent predictors of ICH. These findings allow to identify patients at high risk of ICH.

The risk of free flap revision and subsequent medical costs in patients with hypopharyngeal cancer.

Few studies have examined the preoperative risks and healthcare costs related to free flap revision in hypopharyngeal cancer (HPC) patients. A 20-year retrospective case-control study was conducted using the Chang Gung Research Database, focusing on HPC patients who underwent tumor excision and free flap reconstruction from January 1, 2001, to December 31, 2019. The impacts of clinical variables on the need for re-exploration due to free flap complications were assessed using logistic regression. The direct and indirect effects of these complications on medical costs were evaluated by causal mediation analysis. Among 348 patients studied, 43 (12.4%) developed complications requiring re-exploration. Lower preoperative albumin levels significantly increased the risk of complications (OR 2.45, 95% CI 1.12-5.35), especially in older and previously irradiated patients. Causal mediation analysis revealed that these complications explained 11.4% of the effect on increased hospitalization costs, after controlling for confounders. Lower preoperative albumin levels in HPC patients are associated with a higher risk of microvascular free flap complications and elevated healthcare costs, underscoring the need for enhanced nutritional support before surgery in this population.

Serrated polyposis syndrome: defining the epidemiology and predicting the risk of dysplasia.

Serrated polyposis syndrome is the most common polyposis syndrome that has neoplastic potential. However, the natural history, genetic basis, and risk of dysplasia and neoplasia of serrated polyposis syndrome are incompletely understood. The objective of this study is to define the epidemiology of serrated polyposis syndrome. Using this data, we aim to evaluate candidate variables for predicting the risk of dysplasia and neoplasia in sessile serrated lesions found in serrated polyposis syndrome patients. Finally, we aim to use this data to create and evaluate clinical prediction models for accuracy in predicting dysplastic sessile serrated lesions in serrated polyposis syndrome patients. This was a regional Australian single-centre retrospective cohort study. Data was prospectively collected data from the clinical record database of a regional Australian gastroenterology practice. All patients undergoing colonoscopy at Port Macquarie Gastroenterology between January 2015 and September 2021 were screened for this study. Collected data included patient demographic, endoscopic, and histopathological findings. Clinical and endoscopic multivariate logistic regression models were created to predict dysplastic sessile serrated lesions. Model performance was examined using the area under the receiver operating curve. In total 8401 patients underwent a colonoscopy procedure during the study period. Serrated polyposis syndrome was diagnosed in 247, representing a prevalence of 2.94% (mean age 67.15 years, 62.75% female). Logistic regression identified; older age at serrated polyposis syndrome diagnosis, a personal history of colorectal cancer, size of the largest sessile serrated lesions removed, and total sessile serrated lesions count as predictors of dysplastic sessile serrated lesions. The clinical and endoscopic model had an area under the receiver operating curve of 0.75. Serrated polyposis syndrome is more common than previously described. The clinical and endoscopic variables identified in logistic regression have acceptable accuracy in predicting the risk of dysplasia, however other populations need to be studied to achieve generalisability and improve model performance.

Preoperative N-Terminal Pro-B-Type Natriuretic Peptide and High-Sensitivity Cardiac Troponin T and Outcomes After Major Noncardiac Surgery: A Prospective Cohort Study.

Patients undergoing noncardiac surgery have varying risk of cardiovascular complications. This study evaluated preoperative N-terminal pro-B-type natriuretic peptide and high-sensitivity cardiac troponin T to enhance cardiovascular events prediction for major noncardiac surgery. This prospective cohort study included adult patients with cardiovascular disease or risk factors undergoing elective major noncardiac surgery at four hospitals in China. Blood samples were collected within 30 days before surgery for N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T measurements. The primary outcome was a composite of any cardiovascular events within 30 days after surgery. Logistic regression models were used to assess associations, and the predictive performance was evaluated primarily using area under the receiver-operating-characteristic curve (AUC) and fraction of new predictive information. Between June 2019 and September 2021, 2833 patients were included, with 435 (15.4%) experiencing the primary outcome. In the logistic regression model that included clinical variables and both biomarkers, the odds ratio for the primary outcome was 1.68 (95% CI 1.37-2.07) when comparing the 75th percentile to the 25th percentile of N-terminal pro-B-type natriuretic peptide distribution, and 1.91 (95% CI 1.50-2.43) for high-sensitivity troponin T. Each biomarker enhanced model discrimination beyond clinical predictors, with a change in AUC of 0.028 for N-terminal pro-B-type natriuretic peptide and 0.029 for high-sensitivity cardiac troponin T, and a fraction of new information of 0.164 and 0.149, respectively. The model combining both biomarkers demonstrated the best discrimination, with a change in AUC of 0.042 and a fraction of new information of 0.219. Preoperative N-terminal pro-B-type natriuretic peptide and high-sensitivity troponin T both improved the prediction for cardiovascular events after noncardiac surgery in addition to clinical evaluation, with their combination providing maximal predictive information.

Clustering Identifies Subtypes With Different Phenotypic Characteristics in Women With Polycystic Ovary Syndrome.

Hierarchical clustering (HC) identifies subtypes of polycystic ovary syndrome (PCOS). This work aimed to identify clinically significant subtypes in a PCOS cohort diagnosed with the Rotterdam criteria and to further characterize the distinct subtypes. Clustering was performed using the variables body mass index (BMI), luteinizing hormone (LH), follicle-stimulating hormone, dehydroepiandrosterone sulfate, sex hormone-binding globulin (SHBG), testosterone, insulin, and glucose. Subtype characterization was performed by analyzing the variables estradiol, androstenedione, dehydroepiandrosterone, cortisol, anti-Müllerian hormone (AMH), total follicle count (TFC), lipid profile, and blood pressure. Study participants were girls and women who attended our university hospital for reproductive endocrinology screening between February 1993 and February 2021. In total, 2502 female participants of European ancestry, aged 13 to 45 years with PCOS (according to the Rotterdam criteria), were included. A subset of these (n = 1067) fulfilled the National Institutes of Health criteria (ovulatory dysfunction and hyperandrogenism). Main outcome measures included the identification of distinct PCOS subtypes using cluster analysis. Additional clinical variables associated with these subtypes were assessed. Metabolic, reproductive, and background PCOS subtypes were identified. In addition to high LH and SHBG levels, the reproductive subtype had the highest TFC and levels of AMH (all P < .001). In addition to high BMI and insulin levels, the metabolic subtype had higher low-density lipoprotein levels and higher systolic and diastolic blood pressure (all P < .001). The background subtype had lower androstenedione levels and features of the other 2 subtypes. Reproductive and metabolic traits not used for subtyping differed significantly in the subtypes. These findings suggest that the subtypes capture distinct PCOS causal pathways.

Enhanced patient-based real-time quality control using the graph-based anomaly detection.

Patient-based real-time quality control (PBRTQC) is an alternative tool for laboratories that has gained increasing attention. Despite the progress made by using various algorithms, the problems of data volume imbalance between in-control and out-of-control results, as well as the issue of variation remain challenges. We propose a novel integrated framework using anomaly detection and graph neural network, combining clinical variables and statistical algorithms, to improve the error detection performance of patient-based quality control. The testing results of three representative analytes (sodium, potassium, and calcium) and eight independent variables of patients (test date, time, gender, age, department, patient type, and reference interval limits) were collected. Graph-based anomaly detection network was modeled and used to generate control limits. Proportional and random errors were simulated for performance evaluation. Five mainstream PBRTQC statistical algorithms were chosen for comparison. The framework of a patient-based graph anomaly detection network for real-time quality control (PGADQC) was established and proven feasible for error detection. Compared with classic PBRTQC, the PGADQC showed a more balanced performance for both positive and negative biases. For different analytes, the average number of patient samples until error detection (ANPed) of PGADQC decreased variably, and reductions could reach up to approximately 95 % at a small bias of 0.02 taking calcium as an example. The PGADQC is an effective framework for patient-based quality control, integrating statistical and artificial intelligence algorithms. It improves error detection in a data-driven fashion and provides a new approach for PBRTQC from the data science perspective.

Patient-reported, clinical and radiological factors associated with the result after non-surgical management of acute AC joint dislocation Rockwood type III and V.

The treatment of Rockwood type III and V acromioclavicular (AC) joint dislocations is controversial, and an individualized treatment algorithm is yet to be developed. The objective of this study was to investigate the association of demographical, clinical, patient-reported and radiological variables with the Western Ontario Shoulder Instability Index (WOSI) score and risk of surgery. Inclusion criteria for this prospective cohort study were patients aged 18-60 with an acute AC joint dislocation with >25% increase in the coracoclavicular distance on bilateral Zanca radiographs. Patients were treated non-surgically with 3 months of home-based training and the option of delayed surgical intervention. The outcomes were the WOSI score and surgery yes/no. Demographical, clinical, patient-reported (WOSI and Shoulder Pain and Disability Index [SPADI]) and radiological variables were collected at baseline and 6 weeks after the injury and investigated for association with the outcomes at 3 months, 6 months and 1 year. Ninety-five patients with Rockwood type III/V AC joint dislocation were included. Pre-injury participation in overhead/collision sports was a risk factor for surgery with an odds ratio of 5 (p = 0.03). Reduced range of motion (ROM) at baseline was associated with reduced WOSI scores and increased risk of surgery. At 6 weeks, reduced ROM, increased SPADI and increased pain during cross-over were associated with the outcomes. Radiological measurements were not correlated with the result. At the 6 weeks follow-up, patients eventually requiring surgery could be detected with a sensitivity of 100% and a specificity of 94% based on a SPADI score of >30 and a ROM ≤ 140° in shoulder flexion or abduction. ROM was the only variable consistently associated with both WOSI and risk of surgery. Six weeks after the injury, it was possible to detect patients in need of surgery based on ROM and SPADI with a sensitivity of 100% and a specificity of 94%. Level II.

Characterizing diseases using genetic and clinical variables: A data analytics approach

Characterizing diseases using genetic and clinical variables: A data analytics approach

Searching for the Best Machine Learning Algorithm for the Detection of Left Ventricular Hypertrophy from the ECG: A Review

Background: Left ventricular hypertrophy (LVH) is a powerful predictor of future cardiovascular events. Objectives: The objectives of this study were to conduct a systematic review of machine learning (ML) algorithms for the identification of LVH and compare them with respect to the classical features of test sensitivity, specificity, accuracy, ROC and the traditional ECG criteria for LVH. Methods: A search string was constructed with the operators “left ventricular hypertrophy, electrocardiogram” AND machine learning; then, Medline and PubMed were systematically searched. Results: There were 14 studies that examined the detection of LVH utilizing the ECG and utilized at least one ML approach. ML approaches encompassed support vector machines, logistic regression, Random Forest, GLMNet, Gradient Boosting Machine, XGBoost, AdaBoost, ensemble neural networks, convolutional neural networks, deep neural networks and a back-propagation neural network. Sensitivity ranged from 0.29 to 0.966 and specificity ranged from 0.53 to 0.99. A comparison with the classical ECG criteria for LVH was performed in nine studies. ML algorithms were universally more sensitive than the Cornell voltage, Cornell product, Sokolow-Lyons or Romhilt-Estes criteria. However, none of the ML algorithms had meaningfully better specificity, and four were worse. Many of the ML algorithms included a large number of clinical (age, sex, height, weight), laboratory and detailed ECG waveform data (P, QRS and T wave), making them difficult to utilize in a clinical screening situation. Conclusions: There are over a dozen different ML algorithms for the detection of LVH on a 12-lead ECG that use various ECG signal analyses and/or the inclusion of clinical and laboratory variables. Most improved in terms of sensitivity, but most also failed to outperform specificity compared to the classic ECG criteria. ML algorithms should be compared or tested on the same (standard) database.

Rituximab retention rate in systemic sclerosis: a long term real-life multicenter study.

to report real-life data on rituximab retention-rate as indicator of safety and efficacy in a multicentric national cohort of systemic sclerosis patients. SSc patients treated with rituximab and followed for at least 36 months were included, clinically characterized, and longitudinally monitored. A competing risk analysis with sub-Hazard Ratio(sHR) definition was performed to explore the clinical variables linked to specific cause of rituximab discontinuation. One-hundred-fifty-two SSc-patients (mean age 47.3 ± 12.3 years; females 79.6%; diffuse disease 77.6%; anti-topoisomerase-I positivity 63.2%) were evaluated over a median(IQR) time of 3.3(1.7-5.0) years. The primary indication for rituximab were interstitial lung disease (ILD)(38.8%), worsening skin fibrosis(36.8%), and arthritis(13.8%); 138 patients(90.8%) received more than one rituximab course. The 5-years rituximab retention rate was 59.9%(44.6-64.7%). Clinical response was the most common reason for rituximab discontinuation[5.7(3.7-8.4) per 100 patient-year] and was associated with a shorter disease duration[sHR 0.8(0.7-0.9)], anti-topoisomerase-I negativity[sHR 0.4(0.2-0.9)], previous digital ulcers[sHR 2.6(1.1-6.2] and no history of arthritis[sHR 0.3 (0.1-0.8)]. Treatment failure was the second cause of rituximab discontinuation[3.7(2.2-6.0) per 100 patient-year] and was associated with anti-centromere antibody positivity[sHR 2.8(1.1-7.4)] and anti-topoisomerase-I negativity[sHR 0.2(0.1-0.6)]. Adverse events(AEs) were the less common cause of discontinuation[3.1(1.7-5.2) per 100 patient-year], associated with limited cutaneous subset[sHR 3.4(1.2-9.7)] and previous mycophenolate mofetil treatment[sHR 4.5(1.2-16.3)]. rituximab is a safe and effective treatment in SSc: clinical response emerged as the primary reason for rituximab discontinuation, and AEs had a limited impact on treatment persistence. The identification of specific disease features associated with a response to rituximab will be useful in the management of SSc-patients.

Role of Gut Microbiome in Neoadjuvant Chemotherapy Response in Urothelial Carcinoma: A Multi-Institutional Prospective Cohort Evaluation.

Neoadjuvant chemotherapy (NAC) is linked with clinical advantages in urothelial carcinoma for patients with muscle-invasive bladder cancer (MIBC). Despite comprehensive research into the influence of tumor mutation expression profiles and clinicopathological factors on chemotherapy response, the role of the gut microbiome (GM) in bladder cancer(BC) chemotherapy response remains poorly understood. This study examines the variance in the gut microbiome(GM) of BC patients compared to healthy adults, and investigates GM compositional differences between patients who respond to chemotherapy versus those who exhibit residual disease. Our study reveals distinct clustering, effectively separating the BC and healthy cohorts. However, no significant differences were observed between chemotherapy responders and non-responders within community subgroups. Machine Learning models based on responder status outperformed clinical variables in predicting complete response (AUC 0.88 vs AUC 0.50), although no single microbial species emerged as a fully reliable biomarker. The evaluation of short-chain fatty acid (SCFA) concentration in blood and stool revealed no correlation with responder status. Still, SCFA analysis showed a higher abundance of Akkermansia (rs = 0.51, p = 0.017) and Clostridia (rs = 0.52, p = 0.018), which correlated with increased levels of detectable fecal isobutyric acid. Higher levels of fecal Lactobacillus (rs = 0.49, p=0.02) and Enterobacteriaceae (rs = 0.52, p < 0.03) correlated with increased fecal propionic acid. In conclusion, our study constitutes the first large-scale, multi-center assessment of GM composition, suggesting the potential for a complex microbial signature to predict patients more likely to respond to NAC based on multiple taxa.

Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data, and Symptomatic Assessments.

In this study, we propose a novel framework that utilizes deep learning and attention mechanisms to predict the radiographic progression of patellofemoral osteoarthritis (PFOA) over a period of 7 years. This study included subjects (1,832 subjects, 3,276 knees) from the baseline of the Multicenter Osteoarthritis Study (MOST). Patellofemoral joint regions of interest were identified using an automated landmark detection tool (BoneFinder) on lateral knee X-rays. An end-to-end deep learning method was developed for predicting PFOA progression based on imaging data in a five-fold cross-validation setting. To evaluate the performance of the models, a set of baselines based on known risk factors were developed and analyzed using gradient boosting machine (GBM). Risk factors included age, sex, body mass index, and Western Ontario and McMaster Universities Arthritis Index score, and the radiographic osteoarthritis stage of the tibiofemoral joint (Kellgren and Lawrence [KL] score). Finally, to increase predictive power, we trained an ensemble model using both imaging and clinical data. Among the individual models, the performance of our deep convolutional neural network attention model achieved the best performance with an area under the receiver operating characteristic curve (AUC) of 0.856 and average precision (AP) of 0.431, slightly outperforming the deep learning approach without attention (AUC = 0.832, AP = 0.4) and the best performing reference GBM model (AUC = 0.767, AP = 0.334). The inclusion of imaging data and clinical variables in an ensemble model allowed statistically more powerful prediction of PFOA progression (AUC = 0.865, AP = 0.447), although the clinical significance of this minor performance gain remains unknown. The spatial attention module improved the predictive performance of the backbone model, and the visual interpretation of attention maps focused on the joint space and the regions where osteophytes typically occur. This study demonstrated the potential of machine learning models to predict the progression of PFOA using imaging and clinical variables. These models could be used to identify patients who are at high risk of progression and prioritize them for new treatments. However, even though the accuracy of the models were excellent in this study using the MOST dataset, they should be still validated using external patient cohorts in the future.