Clinical Classification System Research Articles

Characteristics and Utilization of Hospitalizations Among Children With Medical Complexity.

Hospitalizations for children with medical complexity (CMC) have increased substantially over the past 2 decades and constitute a disproportionate percentage of hospitalization rates and costs among children. We sought to describe the etiology and utilization for hospitalizations of CMC using the Pediatric Clinical Classification System (PECCS). Using the 2019 Kids' Inpatient Database, we classified hospitalizations for CMC using the PECCS, which groups diagnoses into mutually exclusive, pediatric-specific categories. For the medical, surgical, and medical/surgical PECCS clinical groups, we reported diagnosis groups accounting for ≥1% of hospital encounters for that group. We described admission frequency, cost, payer, length of stay, and mortality rates within each diagnosis grouping using survey-weighted statistics. We identified 2 315 743 nonlivebirth hospitalizations, of which 712 139 (30.8%) were for CMC. Most (94.4%) hospitalizations occurred at a teaching hospital. Medical diagnosis comprised most hospitalizations (69.2%), whereas hospitalizations for surgical and medical/surgical conditions had a higher median cost. The most common diagnosis groups overall were encounters for chemotherapy, diabetic ketoacidosis, and respiratory failure, whereas the costliest were for necrotizing enterocolitis, transposition of the great vessels, and hypoplastic left heart syndrome. We evaluated the most common diagnoses and their associated resource use for hospitalized CMC using the PECCS, providing a more granular view on the etiology, utilization, cost, and outcomes of hospitalizations for CMC. These topics represent high-impact areas for further research and quality efforts for CMC.

Evaluation of somatosensory bedside testing and neurodynamics of the trigeminal nerve in craniofacial pain: A matched case–control study

ABSTRACT Objective The objective of this study was to investigate whether trigeminal somatosensory function and mechanosensitivity differ between groups with craniofacial neuropathic pain (CNP), non-neuropathic craniofacial pain (NNP), and healthy controls (HC). Methods Thirty-three participants were categorized into these groups, matched for age and sex. The study evaluated pain intensity, the Leeds Assessment of Neuropathic Symptoms and Signs (LANSS), and various trigeminal somatosensory tests, including vibration, pressure pain, thermal detection, cold pain, and neurodynamic tests of the trigeminal nerve. Results Cold pain thresholds differed significantly among the three groups CNP, NNP, and HC (p = .047). No difference was found in vibration detection, pressure pain threshold, and thermal detection. Trigeminal nerve mechanosensitivity was significantly different among the three groups (p = .03), particularly between groups CNP and HC (p = .01). Conclusion Differences in aspects of trigeminal somatosensory function, including cold pain and trigeminal mechanosensitivity, exist between subjects with chronic craniofacial pain and HC. This implies that a clinical classification system for neuromusculoskeletal rehabilitation could be valuable in evaluating patients.

Biobanking rare gynaecological tumours – how harmonised is data collection?

IntroductionGYNOCARE, the European Network for Gynaecological Rare Cancer Research, set out to evaluate the current status of biobanks with access to rare gynaecological tumours, with a view to harmonising sample and data collection and associated consent, to facilitate collaborative cross-border research, enabling clinical trials and translational research. MethodsTwo digital surveys were formulated, one covering clinical and scientific parameters and one exploring ethical and regulatory issues around informed consent. ResultsData were analysed for 20 common responses, from 7 European countries. Tissue was the main sample type biobanked with 63% also banking blood. Documentation of clinical data, treatment regimens and classification systems varied. Eighty percent collected pathological information. Most biobanks were linked to medical records but only one fifth with national registries.The Information Sheet covered governance, benefits/risks, sharing (mainly for non-profit research), return of results and data protection safeguards. Only 37% informed patients about sample and data storage, although about half stored samples for an indefinite time. Pseudonymisation and Data Protection Officer approval were the prime data safeguards. Less than half explained the difference between anonymisation and pseudonymisation. Broad consent was the norm (84%) and 95% granted the right to withdraw consent. Three countries have Biobank legislation. ConclusionThese surveys provide a snapshot of the current state of biobanks and highlight divergences in the consent process and data management. More work is needed to understand what parameters are being gathered across more EU countries and thus harmonise the sample and data collection processes to facilitate cross-border research.

Retrospective evaluation of patients admitted to the emergency department due to drowning

Introduction: Drowning is a preventable process that can result in respiratory failure and death, and often occurs accidentally. In this study, it was aimed to evaluate the demographic characteristics and clinical course of the patients admitted to our emergency department in a district neighbouring a lake due to drowning. Methods: Demographic characteristics, time of presentation, swimming ability, accident mechanism, predisposing factors, Glasgow coma scores (GCS), treatment, discharge, hospitalisation decisions, and mortality status of all patients admitted to our emergency department between January 2018 and January 2023 were recorded retrospectively from patient files and the digital automation system. Results: Twenty (66.7%) patients were male and 10 (33.3%) were female; the patients wereaged between 6 months and 59 years, with a mean age of 14.06 years. An analysis of the site of incident revealed that 26 cases (86.6%) drowned in the lake and 4 cases (13.3%) drowned in a water canal. When we analysed the predisposing factors, it was found that one of the cases drowned after an accidental fall while walking on the rocks and one of the cases drowned after having chest pain and syncope. Among the cases with a GCS of 3, 1 of them died in the intensive care unit after 12 hours, 3 of them recovered with tracheostomy sequelae, and the remaining 14 cases died in the emergency department. Mortality was not observed in any of the 12 cases with a GCS of 14 or above. Fourteen cases died in the emergency department, 11 were referred to the intensive care unit, and 5 cases were discharged after emergency department follow-up. When we classified the patients according to Szpilman's clinical classification system, 8 cases were classified as Grade1, 4 cases as Grade 2, 16 cases as Grade 5, and 2 cases as Grade 6. Conclusion: Raising awareness of families with children and increasing the necessary safety measures in water canals in summer months and in lakes is believed to reduce drowning-induced mortality. In addition to preventing drowning cases, providing the public with first aid training for drowning and healthcare professionals with appropriate assessment, intervention, and treatment algorithms may further reduce mortality.

Treatment of Cesarean Scar Ectopic Pregnancy Using a Double Balloon System

Cesarean scar pregnancies are increasing in incidence, with ill-defined management options. We applied a newly published cesarean scar ectopic pregnancy clinical classification system to our previously published, now expanded, case series of cervical double-balloon catheter management to inform this option as a potential surgical treatment strategy. We found that cervical double-balloon catheter management of cesarean scar pregnancies is an efficacious and safe option when utilizing the newly published clinical classification system. Of our 34 recorded cases, only 5.9% (2/34) of patients required additional procedures after initial cervical double-balloon catheter. Cervical double-balloon catheter should be considered as potential management in future classification systems.

P276. Outcomes of lumbar fusion patients stratified by the Clinical and Radiographic Degenerative Spondylolisthesis (CARDS) classification system

P276. Outcomes of lumbar fusion patients stratified by the Clinical and Radiographic Degenerative Spondylolisthesis (CARDS) classification system

Establishing a Classification System for Predicting Flow-Limiting Dissection After Balloon Angioplasty Using Explainable Machine-Learning Models: A Multicenter Retrospective Cohort Study.

Percutaneous transluminal angioplasty (PTA) is the primary method for treatment in peripheral arterial disease. However, some patients experience flow-limiting dissection (FLD) after PTA. We utilized machine learning and SHapley Additive exPlanations to identify and optimize a classification system to predict FLD after PTA. This was a multi-center, retrospective, cohort study. The cohort comprised 407 patients who underwent treatment of the femoropopliteal (FP) arteries in 3 institutions between January 2021 and June 2023. Preoperative computed tomography angiography images were evaluated to identify FP artery grading, chronic total occlusion (CTO), and vessel calcification (peripheral artery calcium scoring system [PACSS]). After PTA, FLD was identified by angiography. We trained and validated 6 machine-learning models to estimate FLD occurrence after PTA, and the best model was selected. Then, the sum of the Shapley values for each of CTO, FP, and PACSS was calculated for each patient to produce the CTO-FP-PACSS value. The CTO-FP-PACSS classification system was used to classify the patients into classes 1 to 4. Univariate and multivariate analyses were performed to validate the effectiveness of the CTO-FP-PACSS classification system for predicting FLD. Overall, 407 patients were analyzed, comprising 189 patients with FLD and 218 patients without FLD. Differences in sex (71% males vs 54% males, p<0.001), CTO (72% vs 43%, p<0.001), FP (3.26±0.94 vs 2.66±1.06, p<0.001), and PACSS (2.39±1.40 vs 1.74±1.35, p<0.001) were observed between patients with and without FLD, respectively. The random forest model demonstrated the best performance (validation set area under the curve: 0.82). SHapley Additive exPlanations revealed CTO, PACSS, and FP as the 3 most influential FLD predictors, and the univariate and multivariate analyses confirmed CTO-FP-PACSS classification as an independent FLD predictor (multivariate hazard ratio 4.13; p<0.001). The CTO-FP-PACSS classification system accurately predicted FLD after PTA. This user-friendly system may guide surgical decision-making, helping choose between PTA and additional devices to reduce FLD in FP artery treatment. We utilised machine-learning techniques in conjunction with SHapley Additive exPlanations to develop a clinical classification system that predicts the probability of flow-limiting dissection (FLD) after plain old balloon angioplasty. This classification system categorises lesions into Classes 1-4 based on three factors: chronic total occlusion, femoropopliteal grading, and peripheral artery calcium scoring. Each class demonstrated a different probability of developing FLD. This classification system may be valuable for surgeons in their clinical practice, as well as serving as a source of inspiration for other researchers.

Predicting hospital length of stay using machine learning on a large open health dataset

BackgroundGovernments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient’s length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper.MethodsWe investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns.ResultsThe study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns.ConclusionOur study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery.

Utilisation of Atlanta Classification for Local Complications and Severity Detection of Acute Pancreatitis – A Narrative Review

Abstract Acute pancreatitis (AP) is one of the most common acute abdominal pathologies, having varying grades of severity and presentation ranging from mild self-limiting pain in the abdomen and vomiting to severe category which consists of multiple organ failure, sepsis, fever, and severe abdominal pain and vomiting making the patient hospital-bound. Over the years, many radiological and clinical classification systems have emerged to categorize the patient severity and associated local complications to assess their prognosis and help the clinicians decide the treatment plan. Patients suffering from mild pancreatitis are treated with conservative management, whereas those with moderate-to-severe pancreatitis are dealt with minimally invasive or surgical procedures hence making it necessary to classify the patients according to their severity which further helps the clinicians and surgeons decide on treatment plans. The revised Atlanta’s (2012) classification has made a milestone change in the way we classify AP and its complications by including the presence of organ failure and local fluid collections as an essential component of severity grading. In this review article, we will be reviewing past literature and discussing the efficacy of the revised Atlanta classification (2012) in evaluating the type of pancreatitis, the presence or absence of organ failure, local complications, and its role in assessing the prognosis of the patient.

Aggressive Periodontitis: A case report with exome sequencing and functional characterization of identified variant

Aggressive periodontitis (AgP) is characterized by rapid and progressive destruction of the periodontal tissues and eventual tooth loss. Genetic risk contributions have been explored and rare mutations in NOD2 were reported. Sequencing analysis of the coding regions of NOD2 was conducted using individuals of African ancestry, to identify the pathogenic protein-altering variants in individuals with AgP using: 1) In Silico tools and clinical variant classification system; 2) IF staining to understand the expression pattern, and 3) In vitro cell based assay to understand the variant protein function. We discovered a novel nonsense NOD2 variant (c.835G&gt;T; p.Glu279Ter) ; a truncating protein variant that is clinically pathogenic, and the gene-product lacks domains regulating inflammatory responses. Murine studies suggest that this gene contributes to tooth development. In vitro analysis suggests a dominant-negative effect of the variant. NOD2 gene is expressed in the immune cells and periodontal tissues and regulates the inflammatory response within this tissue. This truncating variant found in one African family, which results in a dominant-negative effect, could explain the exacerbated immune response to pathogens in the pathogenesis of this periodontal disease.

Speed- and Endurance-Based Classifications of Community Ambulation Post-Stroke Revisited: The Importance of Location in Walking Performance Measurement

Background Gait speed or 6-minute walk test are frequently used to project community ambulation abilities post-stroke by categorizing individuals as household ambulators, limited, or unlimited community ambulators. However, whether improved clinically-assessed gait outcomes truly translate into enhanced real-world community ambulation remains uncertain. Objective This cross-sectional study aimed to examine differences in home and community ambulation between established categories of speed- and endurance-based classification systems of community ambulation post-stroke and compare these with healthy controls. Methods Sixty stroke survivors and 18 healthy controls participated. Stroke survivors were categorized into low-speed, medium-speed, or high-speed groups based on speed-based classifications and into low-endurance, medium-endurance, or high-endurance groups based on the endurance-based classification. Home and community steps/day were quantified using Global Positioning System and accelerometer devices over 7 days. Results The low-speed groups exhibited fewer home and community steps/day than their medium- and high-speed counterparts (P < .05). The low-endurance group took fewer community steps/day than the high-endurance group (P < .05). Despite vast differences in clinical measures of gait speed and endurance, the medium-speed/endurance groups did not differ in their home and community steps/day from the high-speed/endurance groups, respectively. Stroke survivors took 48% fewer home steps/day and 77% fewer community steps/day than healthy controls. Conclusions Clinical classification systems may only distinguish home ambulators from community ambulators, but not between levels of community ambulation, especially beyond certain thresholds of gait speed and endurance. Clinicians should use caution when predicting community ambulation status through clinical measures, due to the limited translation of these classification systems into the real world.

BACK-to-MOVE: Machine learning and computer vision model automating clinical classification of non-specific low back pain for personalised management.

Low back pain (LBP) is a major global disability contributor with profound health and socio-economic implications. The predominant form is non-specific LBP (NSLBP), lacking treatable pathology. Active physical interventions tailored to individual needs and capabilities are crucial for its management. However, the intricate nature of NSLBP and complexity of clinical classification systems necessitating extensive clinical training, hinder customised treatment access. Recent advancements in machine learning and computer vision demonstrate promise in characterising NSLBP altered movement patters through wearable sensors and optical motion capture. This study aimed to develop and evaluate a machine learning model (i.e., 'BACK-to-MOVE') for NSLBP classification trained with expert clinical classification, spinal motion data from a standard video alongside patient-reported outcome measures (PROMs). Synchronised video and three-dimensional (3D) motion data was collected during forward spinal flexion from 83 NSLBP patients. Two physiotherapists independently classified them as motor control impairment (MCI) or movement impairment (MI), with conflicts resolved by a third expert. The Convolutional Neural Networks (CNNs) architecture, HigherHRNet, was chosen for effective pose estimation from video data. The model was validated against 3D motion data (subset of 62) and trained on the freely available MS-COCO dataset for feature extraction. The Back-to-Move classifier underwent fine-tuning through feed-forward neural networks using labelled examples from the training dataset. Evaluation utilised 5-fold cross-validation to assess accuracy, specificity, sensitivity, and F1 measure. Pose estimation's Mean Square Error of 0.35 degrees against 3D motion data demonstrated strong criterion validity. Back-to-Move proficiently differentiated MI and MCI classes, yielding 93.98% accuracy, 96.49% sensitivity (MI detection), 88.46% specificity (MCI detection), and an F1 measure of .957. Incorporating PROMs curtailed classifier performance (accuracy: 68.67%, sensitivity: 91.23%, specificity: 18.52%, F1: .800). This study is the first to demonstrate automated clinical classification of NSLBP using computer vision and machine learning with standard video data, achieving accuracy comparable to expert consensus. Automated classification of NSLBP based on altered movement patters video-recorded during routine clinical examination could expedite personalised NSLBP rehabilitation management, circumventing existing healthcare constraints. This advancement holds significant promise for patients and healthcare services alike.

Establishment and preliminary evaluation of CT-based classification for distal radius fracture

Establish a new classification system of distal radius fracture based on computed tomographic (CT), and evaluate its reliability and reproducibility preliminarily, and provide a new theoretical reference for clinicians to use the clinical classification system. The imaging data and clinical data of 204 inpatients with distal radius fracture during 6 years from January 1, 2014 to January 1, 2019 in orthopaedic department were analyzed retrospectively and classified based on CT. Three observers evaluated the image data of 48 randomly selected cases based on CT at different time nodes of T1 and T2. Cohen's kappa was used to calculate the consistency. At the last follow-up, patients' Disabilities of the Arm, Shoulder and Hand (DASH), Patient Rated Wrist Evaluation (PRWE), and VAS scores were collected. Among 204 cases, there were 12 cases of type 1, including 6 cases of type 1-D, 4 cases of type 1-V and 2 cases of type 1-R. There were 6 cases of type 2, including 2 cases of type 2-DV, 2 cases of type 2-DR and 2 cases of type 2-VR. There were 186 cases of type 3, including 32 cases of type 3–0, 127 cases of type 3–1 and 27 cases of type 3–2. There was no significant difference in DASH, PRWE and VAS scores among all types (P > 0.05). The results of interobserver reproducibility were kappa = 0.985, ICC = 0.984 in the first evaluation, kappa = 0.986, ICC = 0.986 in the second evaluation. The results of intraobserver reproducibility were O1 = 0.991, O2 = 0.991, O3 = 0.989 respectively. The new classification system of distal radius fracture based on CT has theoretical and practical significance for incision selection, fracture reduction and internal fixation. 123 classification system is clear, comprehensive, easy to understand and remember. Moreover, it has higher interobserver reliability and intraobserver reproducibility than other systems reported at present.

Clinical notes classification system for automated identification of diabetic patients: Hybrid approach integrating rules, information extraction and machine learning

Efficient medical information management is essential in today’s healthcare, significantly to automate diagnoses of chronic diseases. This study focuses on the automated identification of diabetic patients through a clinical note classification system. This innovative approach combines rules, information extraction, and machine learning algorithms to promise greater accuracy and adaptability. Initially, the four algorithms evaluated showed similar performance, with Gradient Boosting standing out with an accuracy of 0.999. They were tested on our clinical and oncology notes, where SVM excelled in correctly labeling non-oncology notes with a 0.99. Gradient Boosting had the best average with 0.966. The combination of rules, information extraction, and Random Forest provided the best average performance, significantly improving the classification of clinical notes and reducing the margin of error in identifying diabetic patients. The principal contribution of this research lies in the pioneering integration of rule-based methods, information extraction techniques, and machine learning algorithms for enhanced accuracy in diabetic patient identification. For future work, we consider implementing these algorithms in natural clinical settings to evaluate their practical performance. Additionally, additional approaches will be explored to improve the accuracy and applicability of clinical note-grading systems in healthcare.

Abstract 7093: Prediction of overall survival in stage II and III colon cancer through machine learning of rapidly-acquired proteomics

Abstract The use of adjuvant therapy in stage II-III colon cancer (CC) has been controversial for decades. No markers or models now is satisfactory enough to be adopted widely by the medical community. Here, we analyzed the proteomics of paraffin-embedded tissue (FFPE) biopsy samples from 230 CC patients (stages II-III) with up to 10 years of survival by using pressure cycling technology (PCT) and data-independent acquisition (DIA) mass spectrometry. Using machine learning, we established a novel and practical protein-based clinical classification system for CC prognosis which was further verified in an independent validation cohort. Using the nine protein-based clinical model, we improved the area under the curve (AUC) value to 0.926 in the training cohort and 0.872 in the validation cohort. Our promising model will be a potential approach to prognostication to aid in rational follow-up schedule-making and risk-adaptive individualized therapies. Citation Format: Kailun Xu, Xiaoyang Yin, Tiannan Guo, Shu Zheng, Yingkuan Shao. Prediction of overall survival in stage II and III colon cancer through machine learning of rapidly-acquired proteomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7093.

Updated Clinical Classification and Hemodynamic Definitions of Pulmonary Hypertension and Its Clinical Implications.

Pulmonary hypertension (PH) refers to a pathologic elevation of the mean pulmonary artery pressure (mPAP) and is associated with increased morbidity and mortality in a wide range of medical conditions. These conditions are classified according to similarities in pathophysiology and management in addition to their invasive hemodynamic profiles. The 2022 ESC/ERS guidelines for the diagnosis and treatment of pulmonary hypertension present the newest clinical classification system and includes significant updates to the hemodynamic definitions. Pulmonary hypertension is now hemodynamically defined as an mPAP > 20 mmHg, reduced from the previous threshold of ≥25 mmHg, due to important insights from both normative and prognostic data. Pulmonary vascular resistance has been extended into the definition of pre-capillary pulmonary hypertension, with an updated threshold of >2 Wood Units (WU), to help differentiate pulmonary vascular disease from other causes of increased mPAP. Exercise pulmonary hypertension has been reintroduced into the hemodynamic definitions and is defined by an mPAP/cardiac output slope of >3 mmHg/L/min between rest and exercise. While these new hemodynamic thresholds will have a significant impact on the diagnosis of pulmonary hypertension, no evidence-based treatments are available for patients with mPAP between 21-24 mmHg and/or PVR between 2-3 WU or with exercise PH. This review highlights the evidence underlying these major changes and their implications on the diagnosis and management of patients with pulmonary hypertension.

Outcomes in Lumbar Fusion Patients Stratified by the Clinical and Radiographic Degenerative Spondylolisthesis (CARDS) Classification System.

Background and objective The Meyerding classification system remains the most common classification system for spondylolisthesis based on the percentages of vertebral translation. However, the majority of patients with degenerative disease fall into Grade 1, limiting its utility in this subset of patients. The Clinical and Radiographic Degenerative Spondylolisthesis (CARDS) classification system provides a simple radiographic framework for classifying degenerative lumbar spondylolisthesis (DLS) patients by incorporating disc height, kyphosis, and anterior translation. The purpose of this study was to evaluate how clinical characteristics, treatments, and outcomes vary across different CARDSgroups in patients undergoing one- or two-level lumbar fusion for DLS. Methods The patients were classified into one of the following four CARDS groups - Type A: advanced disc space collapse with no evidence of kyphosis; Type B: partially preserved disc space with less than 5.0 mm of translation; Type C: partially preserved disc space with greater than 5.0 mm of translation; and Type D: kyphotic alignment. Univariate analyses were performed to compare demographics, symptoms, clinical outcomes, and Patient-Reported Outcomes Measurement Information System (PROMIS) physical (PH) and mental health (MH) scores across groups. Results Ninety-one patients were included in the study. Based on the CARDS classification, there were three (3%) Type A patients, 25 (28%) Type B, 58 (64%) Type C, and five (5%) Type D. No significant differences in baseline demographics, symptom duration, or PROMIS scores were observed across groups. Interbody utilization varied, ranging from 19% in CARDS C (n=11) to 60% in CARDS B (n=15) and D (n=3) patients (p=0.005). Thirty-day clinical outcomes were similar across groups. At an average follow-up of 8.9 months, improvements in PROMIS PH and MH scores and rates of clinically significant improvement were similar across groups. Conclusions Based on our findings, patients undergoing lumbar fusion for DLS present with similar demographic and clinical characteristicsand experience similar clinical and patient-reported outcomes when stratified using the CARDS classification system. Posterolateral fusion (PLF) can be effective for various radiographic presentations of DLS. Further research is warranted to assess the utility of CARDS in preoperative planning.

The use of standardized nursing languages in electronic medical records: an exploratory study on opportunities, limitations, and strategies

Standardized nursing languages (SNLs) have found increasing application in electronic medical records in recent years. In Italy their use is still uneven and accompanied by a silent debate between positions 'against' and 'for' their use. To render visible the debate regarding SNLs in Italy, and the strategies to consider when digitized records are based on a SNL. Data has been collected through audio-recorded semi-structured interviews, selecting three Italian nursing professors, four managers representing Italian healthcare settings that used a SNT and a representative of the Central committee of the National federation of orders of nursing professions. The thematic approach was used to analyze the data. Participants reported having introduced digitized records based on nursing diagnoses, integrated with the Nursing Interventions Classification System and Nursing Outcome Classification, Clinical Care Classification System, Nursing Sensitive Outcomes or mixed models. Divergent aspects emerge regarding: (1) using nursing languages vs a common language to other healthcare professions; (2) planning care vs enhancing clinical reasoning; (3) measuring nursing care vs accepting the variability of the practice, and (4) making documentation efficient vs dedicating more time. Some convergences have emerged and a set of indications for introducing electronic records when based on standardized languages. The introduction of electronic documentation requires the use of homogeneous languages. The debate on the potential and limits of SNL is still open and requires reflection among researchers, trainers, clinicians, and coordinators/managers of nursing care regarding the choices to be made which may have long-term effects on many nurses.

Correlation Between Clinical and Histopathological Stagings of Oral Submucous Fibrosis: A Clinicopathological Cognizance of 238 Cases From South India.

Background This paper aims to descriptively present the clinico-demographic and pathological profile of 238 cases of oral submucous fibrosis (OSMF) with emphasis on the correlation between clinical and histopathological classification systems and the incidence of malignant transformation. Methods A total of 7098 oral biopsies were retrospectively retrieved over a period of 13 years, out of which 238 cases of OSMF were included in the present study. Data were analyzed for age, gender, habits, clinical symptoms, functional staging, histological staging, type and nature of epithelium, signet-ring cell changes, presence/absence of dysplasia or transformation squamous cell carcinoma, and treatment. Results Clinically and histologically, most cases were moderately advanced. Men outnumbered women. The prevalence of dysplasia was found to be 23.94% and the malignant transformation rate was estimated to be 13.8%. There was a significant correlation between clinical staging with age and histological grading. No correlation was found between histological staging and the age of the patients. Conclusions OSMF is a habit-related progressive chronic oral condition with a high malignant transformation rate. Multimodal treatment with auxiliary physiotherapy helps in the improvement of the clinical symptoms.

Adult ADHD: it is old and new at the same time- what is it?

Even though the number of studies aiming to improve comprehension of ADHD pathology has increased in recent years, there still is an urgent need for more effective studies, particularly in understanding adult ADHD, both at preclinical and clinical levels, due to the increasing evidence that adult ADHD is highly distinct and a different entity from childhood ADHD. This review paper outlines the symptoms, diagnostics, and neurobiological mechanisms of ADHD, with emphasis on how adult ADHD could be different from childhood-onset. Data show a difference in the environmental, genetic, epigenetic, and brain structural changes, when combined, could greatly impact the behavioral presentations and the severity of ADHD in adults. Furthermore, a crucial aspect in the quest to fully understand this disorder could be through longitudinal analysis. In this way, we will determine if and how the pathology and pharmacology of ADHD change with age. This goal could revolutionize our understanding of the disorder and address the weaknesses in the current clinical classification systems, improving the characterization and validity of ADHD diagnosis, specifically those in adults.