A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images.

OBJECTIVES: Assessment of plaque stenosis severity allows better management of carotid source of stroke. Our objective is to create a deep learning (DL) model to segment carotid intima-media thickness and plaque and further automatically calculate plaque stenosis severity on common carotid artery (CCA) transverse section ultrasound images. METHODS: Three hundred and ninety images from 376 individuals were used to train (235/390, 60%), validate (39/390, 10%), and test (116/390, 30%) on a newly proposed CANet model. We also evaluated the model on an external test set of 115 individuals with 122 images acquired from another hospital. Comparative studies were conducted between our CANet model with four state-of-the-art DL models and two experienced sonographers to re-evaluate the present model's performance. RESULTS: On the internal test set, our CANet model outperformed the four comparative models with Dice values of 95.22% versus 90.15%, 87.48%, 90.22%, and 91.56% on lumen-intima (LI) borders and 96.27% versus 91.40%, 88.94%, 91.19%, and 92.88% on media-adventitia (MA) borders. On the external test set, our model still produced excellent results with a Dice value of 92.41%. Good consistency of stenosis severity calculation was observed between CANet model and experienced sonographers, with Intraclass Correlation Coefficient (ICC) of 0.927 and 0.702, Pearson's Correlation Coefficient of 0.928 and 0.704 on internal and external test set, respectively. CONCLUSIONS: Our CANet model achieved excellent performance in the segmentation of carotid IMT and plaques as well as automated calculation of stenosis severity.

Image-based remote evaluation of PASI scores with psoriasis by the YOLO-v4 algorithm.

As a chronic relapsing disease, psoriasis is characterized by widespread skin lesions. The Psoriasis Area and Severity Index (PASI) is the most frequently utilized tool for evaluating the severity of psoriasis in clinical practice. Nevertheless, long-term monitoring and precise evaluation pose difficulties for dermatologists and patients, which is time-consuming, subjective and prone to evaluation bias. To develop a deep learning system with high accuracy and speed to assist PASI evaluation, we collected 2657 high-quality images from 1486 psoriasis patients, and images were segmented and annotated. Then, we utilized the YOLO-v4 algorithm to establish the model via four modules, we also conducted a human-computer comparison through quadratic weighted Kappa (QWK) coefficients and intra-class correlation coefficients (ICC). The YOLO-v4 algorithm was selected for model training and optimization compared with the YOLOv3, RetinaNet, EfficientDet and Faster_rcnn. The model evaluation results of mean average precision (mAP) for various lesion features were as follows: erythema, mAP = 0.903; scale, mAP = 0.908; and induration, mAP = 0.882. In addition, the results of human-computer comparison also showed a median consistency for the skin lesion severity and an excellent consistency for the area and PASI score. Finally, an intelligent PASI app was established for remote disease assessment and course management, with a pleasurable agreement with dermatologists. Taken together, we proposed an intelligent PASI app based on the image YOLO-v4 algorithm that can assist dermatologists in long-term and objective PASI scoring, shedding light on similar clinical assessments that can be assisted by computers in a time-saving and objective manner.

Segmental differences in daytime changes of leg volume.

BACKGROUND: The aim of our study was to comparatively assess volume changes related to daily occupation of the whole leg (WLv), of the lower leg (LLv) and of the upper leg (ULv) in subject with no venous and lymphatic disorders. METHOD: WLv, LLv, and Ulv were evaluated by water displacement volumetry (WDV) in the morning and in the evening in 20 healthy subjects. RESULTS: In the legs with occupational edema (OE), WLv increased by 7.07%, LLv by 5.25%, and ULv by 9.80%. In legs without clear OE, WLv increased by 2.41%, LLv by 1.35, and ULv by 3.38%. CONCLUSIONS: Surprisingly, the increase of ULv was greater than that of LLv. An evening increase in the leg volume also occurred in legs with no clear OE. In our series, a clinically evident OE was related to an increase of the WLv, LLv, and ULv greater than 5.83%, 8.68%, and 1.88%, respectively.

"It's hard to say anything definitive about what severity really is": lay conceptualisations of severity in a healthcare context.

BACKGROUND: Demand for healthcare outweighs available resources, making priority setting a critical issue. 'Severity' is a priority-setting criterion in many healthcare systems, including in Norway, Sweden, the Netherlands, and the United Kingdom. However, there is a lack of consensus on what severity means in a healthcare context, both in the academic literature and in policy. Further, while public preference elicitation studies demonstrate support for severity as a relevant concern in priority setting, there is a paucity of research on what severity is taken to mean for the public. The purpose of this study is to explore how severity is conceptualised by members of the general public. METHODS: Semi-structured group interviews were conducted from February to July 2021 with members of the Norwegian adult public (n = 59). These were transcribed verbatim and subjected to thematic analysis, incorporating inductive and deductive elements. RESULTS: Through the analysis we arrived at three interrelated main themes. Severity as subjective experience included perceptions of severity as inherently subjective and personal. Emphasis was on the individual's unique insight into their illness, and there was a concern that the assessment of severity should be fair for the individual. The second theme, Severity as objective fact, included perceptions of severity as something determined by objective criteria, so that a severe condition is equally severe for any person. Here, there was a concern for determining severity fairly within and across patient groups. The third theme, Severity as situation dependent, included perceptions of severity centered on second-order effects of illness. These included effects on the individual, such as their ability to work and enjoy their hobbies, effects on those surrounding the patient, such as next of kin, and effects at a societal level, such as production loss. We also identified a concern for determining severity fairly at a societal level. CONCLUSIONS: Our findings suggest that severity is a polyvalent notion with different meanings attached to it. There seems to be a dissonance between lay conceptualisations of severity and policy operationalisations of the term, which may lead to miscommunications between members of the public and policymakers.

Severity of COVID-19 infection in patients with COVID-19 combined with diabetes.

PURPOSE: This study aimed to analyse the correlation between blood glucose control and the severity of COVID-19 infection in patients with diabetes. METHODS: Clinical and imaging data of a total of 146 patients with diabetes combined with COVID-19 who visited our hospital between December 2022 and January 2023 were retrospectively collected. The patients were divided into the 'good blood glucose control' group and the 'poor blood glucose control' group based on an assessment of their blood glucose control. The clinical data, computed tomography (CT) appearance and score and the severity of COVID-19 infection of the two groups were compared, with the severity of COVID-19 infection being the dependent variable to analyse other influencing factors. RESULTS: The group with poor blood glucose control showed a higher lobar involvement degree and total CT severity score (CTSS) than the group with good blood glucose control (13.30 ± 5.25 vs. 10.38 ± 4.84, p < 0.05). The two groups exhibited no statistically significant differences in blood lymphocyte, leukocyte, C-reaction protein, pleural effusion, consolidation, ground glass opacity or crazy-paving signs. Logistic regression analysis showed that the total CTSS significantly influences the clinical severity of patients (odds ratio 1.585, p < 0.05), whereas fasting plasma glucose and blood glucose control are not independent factors influencing clinical severity (both p > 0.05). The area under the curve (AUC) of CTSS prediction of critical COVID-19 was 0.895 with sensitivity of 79.3% and specificity of 88.1% when the threshold value is 12. CONCLUSION: Blood glucose control is significantly correlated with the CTSS; the higher the blood glucose is, the more severe the lung manifestation. The CTSS can also be used to evaluate and predict the clinical severity of COVID-19.

Evolution of illness severity in hospital admissions due to COVID-19, Québec, Canada, January to April 2022.

Background: The coronavirus disease 2019 (COVID-19) severity is influenced by multiple factors, such as age, underlying medical conditions, individual immunity, infecting variant, and clinical practice. The highly transmissible Omicron variants resulted in decreased COVID-19 screening capacity, which limited disease severity surveillance. Objective: To report on the temporal evolution of disease severity among patients admitted to Québec hospitals due to COVID-19 between January 2, 2022, and April 23, 2022, which corresponded to the peak period of hospitalizations due to Omicron. Methods: Retrospective population-based cohort study of all hospital admissions due to COVID-19 in Québec, between January 2, 2022, and April 23, 2022. Study period was divided into four-week periods, corresponding roughly to January, February, March and April. Regression using Cox and Poisson generalized estimating equations (GEEs) was used to quantify temporal variations in length of stay and risk of complications (intensive care admission or in-hospital death) through time, using the Omicron peak (January 2022) as reference. Measures were adjusted for age, sex, vaccination status, presence of chronic diseases, and clustering by hospital. Results: During the study period, 9,178 of all 18,272 (50.2%) patients hospitalized with a COVID-19 diagnosis were admitted due to COVID-19. Of these, 1,026 (11.2%) were admitted to intensive care and 1,523 (16.6%) died. Compared to January, the risk of intensive care admission was 25% and 31% lower in March and April respectively, while in-hospital fatality continuously decreased by 45% lower in April. The average length of stay was temporarily lower in March (9%). Conclusion: Severity of admissions due to COVID-19 decreased in the first months of 2022, when predominant circulating variants were considered to be of similar severity. Monitoring hospital admissions due to COVID-19 can contribute to disease severity surveillance.

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis.

Background: Predicting the severity of acute pancreatitis (AP) early poses a challenge in clinical practice. While there are well-established clinical scoring tools, their actual predictive performance remains uncertain. Various studies have explored the application of machine-learning methods for early AP prediction. However, a more comprehensive evidence-based assessment is needed to determine their predictive accuracy. Hence, this systematic review and meta-analysis aimed to evaluate the predictive accuracy of machine learning in assessing the severity of AP. Methods: PubMed, EMBASE, Cochrane Library, and Web of Science were systematically searched until December 5, 2023. The risk of bias in eligible studies was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses, based on different machine learning types, were performed. Additionally, the predictive accuracy of mainstream scoring tools was summarized. Results: This systematic review ultimately included 33 original studies. The pooled c-index in both the training and validation sets was 0.87 (95 % CI: 0.84-0.89) and 0.88 (95 % CI: 0.86-0.90), respectively. The sensitivity in the training set was 0.81 (95 % CI: 0.77-0.84), and in the validation set, it was 0.79 (95 % CI: 0.71-0.85). The specificity in the training set was 0.84 (95 % CI: 0.78-0.89), and in the validation set, it was 0.90 (95 % CI: 0.86-0.93). The primary model incorporated was logistic regression; however, its predictive accuracy was found to be inferior to that of neural networks, random forests, and xgboost. The pooled c-index of the APACHE II, BISAP, and Ranson were 0.74 (95 % CI: 0.68-0.80), 0.77 (95 % CI: 0.70-0.85), and 0.74 (95 % CI: 0.68-0.79), respectively. Conclusions: Machine learning demonstrates excellent accuracy in predicting the severity of AP, providing a reference for updating or developing a straightforward clinical prediction tool.

Determinants of severity levels of anemia among pregnant women in Sub-Saharan Africa: multilevel analysis.

Background: Anemia is a severe public health problem affecting 54% of pregnant women in SSA Yet, only a limited number of studies have provided a partial assessment of the pooled prevalence and related determinants of the severity levels of anemia in pregnant women in SSA. Therefore, this study provides the most recent estimates of anemia severity levels and related determinants. Methods: The most recent Demographic Health Survey (DHS) dataset of 21 Sub-Saharan African countries which were collected between 2015 and 2022 were used. A total of 14,098 pregnant women were included. Multilevel ordinal logistic regression was used. Results: The pooled prevalence of anemia was 51.26%. Pregnant women who were in the old age groups, and who have attended secondary and higher education were less likely to be at higher levels of anemia. Those women who have given birth to >1 children in the last 5 years, pregnant women in second and third trimester and living in poorest households had greater odds of being at higher levels of anemia. Conclusion: In Sub-Saharan Africa, anemia is a severe public health concern for pregnant mothers. When developing and implementing strategies for the prevention and control of anemia, it is imperative to take into account the individual and community circumstances. Programs for the prevention and control of anemia should incorporate the economic and educational empowerment of women.

Impact of Anti-angiogenic Drugs on Severity of COVID-19 in Patients with Non-Small Cell Lung Cancer.

Introduction: The 2019 coronavirus disease (COVID-19) pandemic has reshaped oncology practice, but the impact of anti-angiogenic drugs on the severity of COVID-19 in patients with non-small cell lung cancer (NSCLC) remains unclear. Patients and Methods: We carried out a retrospective study involving 166 consecutive patients with NSCLC who were positive for COVID-19, aiming to determine the effects of anti-angiogenic drugs on disease severity, as defined by severe/critical symptoms, intensive care unit (ICU) admission/intubation, and mortality outcomes. Risk factors were identified using univariate and multivariate logistic regression models. Results: Of the participants, 73 had been administered anti-angiogenic drugs (termed the anti-angiogenic therapy (AT) group), while 93 had not (non-AT group). Comparative analyses showed no significant disparity in the rates of severe/critical symptoms (21.9% vs 35.5%, P = 0.057), ICU admission/intubation (6.8% vs 7.5%, P = 0.867), or death (11.0% vs 9.7%, P = 0.787) between these two groups. However, elevated risk factors for worse outcomes included age ≥ 60 (odds ratio (OR): 2.52, 95% confidence interval (CI): 1.07-5.92), Eastern Cooperative Oncology Group performance status of 2 or higher (OR: 21.29, 95% CI: 4.98-91.01), chronic obstructive pulmonary disease (OR: 7.25, 95% CI: 1.65-31.81), hypertension (OR: 2.98, 95% CI: 1.20-7.39), and use of immunoglobulin (OR: 5.26, 95% CI: 1.06-26.25). Conclusion: Our data suggests that the use of anti-angiogenic drugs may not exacerbate COVID-19 severity in NSCLC patients, indicating their potential safe application even during the pandemic period.

The Prevalence and Risk Factors of Metabolic Syndrome in Patients with Hemodialysis.

Introduction: Metabolic syndrome is a common disorder that puts patients at high risk for cardiovascular disease (CVD) and mortality. To our knowledge, there is no published study in Pub Med which evaluated both lifestyle and metabolic syndrome in hemodialysis patients. This study aimed to estimate the prevalence of metabolic syndrome and investigate the potential risk factors in hemodialysis patients. Methods: This was a cross-sectional study conducted on 204 patients enrolled conveniently. National Cholesterol Education Program Adult Treatment Panel III criteria considered for Metabolic Syndrome. Demographics, lifestyle, and disease characteristics were gathered. The relationship between metabolic syndrome and its severity with independent variables was investigated through multivariable multivariate logistic and linear regressions. Results: The mean (SD) age was 55 (14) years and 42% were women. 42.6% had metabolic syndrome. Low high-density lipoprotein (HDL), high fasting blood sugar, high blood pressure (BP), increased waist circumference (WC), and high triglyceride were observed in decreasing order of frequency in 54.4%, 44.1%, 38.7%, 33.3% 28.9% of patients, respectively. The logistic regression model revealed significant associations between metabolic syndrome and physical activity (OR=0.85, 95% CI : 0.74-0.97), mood (OR=1.04, 95% CI : 1.002-1.078), age (OR=1.023, 95% CI : 1.001-1.046), and missed work (OR=0.86, 95% CI : 0.76-0.97). The linear regression model revealed significant associations between metabolic syndrome severity score and physical activity (B=-0.12, 95% CI : -0.21-0.02) and sleep quality (B=0.017, 95% CI : 0.001-0.033). Conclusion: Poorer sleep quality, lower physical activity, lower mood status, and older age were associated with higher odds of metabolic syndrome/metabolic syndrome severity score in hemodialysis patients.

Functional near-infrared spectroscopy-based diagnosis support system for distinguishing between mild and severe depression using machine learning approaches.

Significance: Early diagnosis of depression is crucial for effective treatment. Our study utilizes functional near-infrared spectroscopy (fNIRS) and machine learning to accurately classify mild and severe depression, providing an objective auxiliary diagnostic tool for mental health workers. Aim: Develop prediction models to distinguish between severe and mild depression using fNIRS data. Approach: We collected the fNIRS data from 140 subjects and applied a complete ensemble empirical mode decomposition with an adaptive noise-wavelet threshold combined denoising method (CEEMDAN-WPT) to remove noise during the verbal fluency task. The temporal features (TF) and correlation features (CF) from 18 prefrontal lobe channels of subjects were extracted as predictors. Using recursive feature elimination with cross-validation, we identified optimal TF or CF and examined their role in distinguishing between severe and mild depression. Machine learning algorithms were used for classification. Results: The combination of TF and CF as inputs for the prediction model yielded higher classification accuracy than using either TF or CF alone. Among the prediction models, the SVM-based model demonstrates excellent performance in nested cross-validation, achieving an accuracy rate of 92.8%. Conclusions: The proposed model can effectively distinguish mild depression from severe depression.

Racial and Ethnic Disparities in Health-Related Outcomes in Crohn's Disease: Results From the National Health and Wellness Survey.

Background: Crohn's disease (CD) is a chronic inflammatory condition affecting the entire gastrointestinal tract that is associated with significant humanistic, clinical, and economic burdens. Few studies have assessed the association between CD severity and patient-reported outcomes (PROs), healthcare resource utilization (HCRU), and medical costs; even fewer have examined differences in disease outcomes among patients of various racial/ethnic groups. Methods: In this cross-sectional study, sociodemographic data, PROs, and economic outcomes for participants with self-reported CD were collected from the National Health and Wellness Survey (2018-2020). Multivariable analyses were used to assess the association of CD severity and race/ethnicity with health-related quality of life (HRQoL), work productivity and activity impairment (WPAI), HCRU, and medical costs. Results: Analyses included 1077 participants with CD (818 non-Hispanic White, 109 non-Hispanic Black, and 150 Hispanic). Participants with self-reported moderate/severe CD reported significantly worse HRQoL and WPAI, greater HCRU, and higher medical costs than those with self-reported mild CD. Non-Hispanic Black participants reported better HRQoL and fewer healthcare provider visits than non-Hispanic White participants. There were no significant differences in PROs between non-Hispanic White and Hispanic groups. Interactions between race/ethnicity and CD severity emerged for some, but not all groups: Specifically, non-Hispanic Black participants with moderate/severe CD reported greater absenteeism and more gastroenterologist visits than non-Hispanic Black participants with mild CD. Conclusions: Participants with moderate/severe CD reported worse PROs, greater HCRU, and higher medical costs than those with mild CD. Additionally, racial/ethnic differences were found across several HCRU and economic outcomes. Further research is needed to better understand factors contributing to burden among patients with varying CD severity across racial/ethnic groups.

A new sight to acute pancreatitis through paracolic gutter exudation, a multicenter retrospective study.

Objectives: Paracolic gutter exudation (PGE) may influence the severity of acute pancreatitis, but no study has explored it extensively. The objective of this study was to evaluate PGE for assessing the severity of disease. Methods: We performed a retrospective analysis of 488 patients from three tertiary hospitals in Guangxi, China. General clinical information, severity, and clinical courses were recorded. The PGE score were classified as follows: 0 for no exudation, 1 for unilateral exudation, and 2 for bilateral exudation. We used ROC curves to assess the predictive value of the PGE score, and logistic regression analysis to determine risk factors associated with death, ICU admission, and the occurrence of MODS. Results: This study included 352 patients with moderately severe acute pancreatitis (MSAP) and 136 patients with severe acute pancreatitis (SAP). Patients who had PGE experienced higher total hospitalization costs, longer hospital stays, a higher incidence of SAP, higher mortality rates, higher ICU admission rates, a higher incidence of MODS, and higher incidence of infections than those without (P < 0.05). Diagnostic efficacy in predicting severity in patients with MSAP and SAP increased after BISAP, MCTSI, modified Marshall, and SOFA scores combined with PGE score respectively. The PGE score of >1 is an independent risk factor for ICU admission and MODS occurrence. (P < 0.05). Conclusion: The PGE provides reliable and objective information for assessing severity and clinical course of patients with MSAP and SAP.

Risk Prediction of Severe Bronchopulmonary Dysplasia (BPD) Using the Respiratory Severity Score (RSS) in Extremely Preterm Infants: A Retrospective Study From Saudi Arabia.

Background Bronchopulmonary dysplasia (BPD) is a significant complication in extremely preterm infants. Therefore, early diagnosis of BPD is important for planning treatment strategies. In this study, we aimed to assess the predictive efficacy of the Respiratory Severity Score (RSS) in determining severe BPD or death outcomes in very preterm infants. Methodology This retrospective study included preterm infants born with a gestational age of ≤30 weeks. The inclusion criteria comprised individuals who were mechanically ventilated (<1 week) during the first four weeks of life. Any patients who died during the first seven days of life were excluded. RSS values were recorded on days 3, 14, 21, and 28 of life. Multivariate logistic regression was used to identify a correlation between RSS and patient outcomes. Results A total of 154 infants were included in the analysis, of whom 82 (53.24%) developed severe BPD and 38 (24.67%) died. RSS was higher in patients who either died or developed severe BPD compared to those who survived. The multivariate logistic regression analysis revealed that RSSs at postnatal day 14 (odds ratio (OR) = 3.970; 95% confidence interval (CI) = 1.114-14.147; p < 0.05), day 21 (OR = 6.201; 95% CI = 1.937-19.851; p < 0.05), and day 28 (OR = 8.925; 95% CI = 3.331-28.383; p < 0.05) was significantly associated with a higher risk of death or severe BPD. Conclusions The findings of the present study revealed that RSS can help predict the risk of severe BPD in very preterm infants.

Predicting the severity of acute pancreatitis: Current approaches and future directions.

Acute pancreatitis (AP) is a sudden-onset inflammatory disease of the pancreas. The severity of AP is classified into mild, moderate, and severe categories based on the presence and persistence of organ failure. Severe acute pancreatitis (SAP) can be associated with significant morbidity and mortality. It requires early recognition for appropriate timely management. Prognostic scores for predicting SAP incorporating many clinical, laboratory, and radiological parameters have been developed in the past. However, all of these prognostic scores have low positive predictive value for SAP and some of these scores require >24 h for assessment. There is a need to develop biomarkers that can accurately identify patients at risk for SAP early in the course of the presentation. In this review, we aim to provide a summary of the most commonly utilized prognostic scores for AP and discuss future directions.

S. aureus biofilm properties correlate with immune B cell subset frequencies and severity of chronic rhinosinusitis.

Staphylococcus aureus mucosal biofilms are associated with recalcitrant chronic rhinosinusitis (CRS). However, S. aureus colonisation of sinus mucosa is frequent in the absence of mucosal inflammation. This questions the relevance of S. aureus biofilms in CRS etiopathogenesis. This study aimed to investigate whether strain-level variation in in vitro-grown S. aureus biofilm properties relates to CRS disease severity, in vitro toxicity, and immune B cell responses in sinonasal tissue from CRS patients and non-CRS controls. S. aureus clinical isolates, tissue samples, and matched clinical datasets were collected from CRS patients with nasal polyps (CRSwNP), CRS without nasal polyps (CRSsNP), and controls. B cell responses in tissue samples were characterised by FACS. S. aureus biofilms were established in vitro, followed by measuring their properties of metabolic activity, biomass, colony-forming units, and exoprotein production. S. aureus virulence was evaluated using whole-genome sequencing, mass spectrometry and application of S. aureus biofilm exoproteins to air-liquid interface cultures of primary human nasal epithelial cells (HNEC-ALI). In vitro S. aureus biofilm properties were correlated with increased CRS severity scores, infiltration of antibody-secreting cells and loss of regulatory B cells in tissue samples. Biofilm exoproteins from S. aureus with high biofilm metabolic activity had enriched virulence genes and proteins, and negatively affected the barrier function of HNEC-ALI cultures. These findings support the notion of strain-level variation in S. aureus biofilms to be critical in the pathophysiology of CRS.

Carpal tunnel syndrome and sleep, a systematic review and meta-analysis.

BACKGROUND: The most common symptom and reason patients seek treatment for carpal tunnel syndrome is lack of sleep. Our purpose was to determine how much sleep-related symptoms of carpal tunnel syndrome improve after carpal tunnel release using validated patient-reported outcome measures (PROMs) and objective sleep data as primary measures of interest. METHODS: A PRISMA-guided literature search was conducted using Ovid MEDLINE, PubMed, Cochrane, and ClinicalTrials.gov. Only interventional clinical trials that examined primary outcome measures of interest were included. Patient-reported outcome measures underwent meta-analysis to determine how much scores improved following carpal tunnel release. RESULTS: The Pittsburgh Sleep Quality Index improved significantly after carpal tunnel release, by 4.43 points and 6.02 points at 1-3 and 6-12 months postoperatively, respectively, and continued to improve up to 2 years. Improvement on the Insomnia Severity Index after carpal tunnel release was also significant, with improvement up to 1 year postoperatively, by 8.54 points and 9.05 points at 1-3 and 6-12 months, respectively. Insomnia Severity Index scores improved significantly after splinting as well. CONCLUSIONS: The present meta-analysis determined to what extent patients can expect their sleep to improve after operative and non-operative intervention, as measured by various patient-reported outcome measures that assess sleep. The Pittsburgh Sleep Quality Index and Insomnia Severity Index correlated very well between studies and across hundreds of patients with carpal tunnel syndrome. Data are lacking to define the minimal clinically important difference and assess whether patients achieve a minimal clinically important difference for sleep questionnaires; more information on this topic is needed. LEVEL OF EVIDENCE: III.

Charlson comorbidity index and the severity of community-acquired pneumonia caused by SARS-CoV-2: A retrospective analysis.

BACKGROUND: The Charlson comorbidity index (CCI) has been considered as a valid and reliable tool for predicting poor clinical outcomes and mortality in patients with coronavirus disease 2019 (COVID-19). However, its relationship with the severity of pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not been thoroughly explored. OBJECTIVES: The aim of the present study was to identify the impact of the comorbidity burden, quantitatively assessed by applying CCI, on the severity of inpatient community-acquired pneumonia (CAP) caused by SARS-CoV-2. MATERIAL AND METHODS: The study was conducted using the medical records of 208 patients with CAP who had an epidemiological history of a plausible SARS-CoV-2 infection, with positive polymerase chain reaction (PCR) confirmation no later than 1 month before being admitted for inpatient treatment. The CCI was calculated using a custom computer program. The statistical analysis of data was carried out using Statistica, v. 7.0. RESULTS: Our study found a significant correlation between the comorbidity burden and the severity of CAP caused by SARS-CoV-2. Specifically, we observed a low CCI score in the majority of patients in the pneumonia risk class II and III groups, and a high CCI score ≥3 in the majority of patients in the pneumonia risk class IV group. Moreover, a direct correlation between CCI and age was established. The comorbidities most commonly associated with CAP caused by SARS-CoV-2 were congestive heart failure, moderate to severe liver diseases and diabetes mellitus (DM) with chronic complications. CONCLUSIONS: The use of CCI to evaluate comorbid pathology in hospitalized patients with CAP caused by SARS-CoV-2 can assist the medical staff in developing timely preventive and therapeutic strategies, leading to improved patient prognosis.

Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach.

BACKGROUND: Though there has been considerable effort to implement machine learning (ML) methods for health care, clinical implementation has lagged. Incorporating explainable machine learning (XML) methods through the development of a decision support tool using a design thinking approach is expected to lead to greater uptake of such tools. OBJECTIVE: This work aimed to explore how constant engagement of clinician end users can address the lack of adoption of ML tools in clinical contexts due to their lack of transparency and address challenges related to presenting explainability in a decision support interface. METHODS: We used a design thinking approach augmented with additional theoretical frameworks to provide more robust approaches to different phases of design. In particular, in the problem definition phase, we incorporated the nonadoption, abandonment, scale-up, spread, and sustainability of technology in health care (NASSS) framework to assess these aspects in a health care network. This process helped focus on the development of a prognostic tool that predicted the likelihood of admission to an intensive care ward based on disease severity in chest x-ray images. In the ideate, prototype, and test phases, we incorporated a metric framework to assess physician trust in artificial intelligence (AI) tools. This allowed us to compare physicians' assessments of the domain representation, action ability, and consistency of the tool. RESULTS: Physicians found the design of the prototype elegant, and domain appropriate representation of data was displayed in the tool. They appreciated the simplified explainability overlay, which only displayed the most predictive patches that cumulatively explained 90% of the final admission risk score. Finally, in terms of consistency, physicians unanimously appreciated the capacity to compare multiple x-ray images in the same view. They also appreciated the ability to toggle the explainability overlay so that both options made it easier for them to assess how consistently the tool was identifying elements of the x-ray image they felt would contribute to overall disease severity. CONCLUSIONS: The adopted approach is situated in an evolving space concerned with incorporating XML or AI technologies into health care software. We addressed the alignment of AI as it relates to clinician trust, describing an approach to wire framing and prototyping, which incorporates the use of a theoretical framework for trust in the design process itself. Moreover, we proposed that alignment of AI is dependent upon integration of end users throughout the larger design process. Our work shows the importance and value of engaging end users prior to tool development. We believe that the described approach is a unique and valuable contribution that outlines a direction for ML experts, user experience designers, and clinician end users on how to collaborate in the creation of trustworthy and usable XML-based clinical decision support tools.

Occurrence of area postrema syndrome during follow-up: phenotype and influence over NMOSD activity in LATAM in real-world settings.

INTRODUCTION: We aimed to assess the frequency, duration, and severity of area postrema syndrome (APS) during follow-up in neuromyelitis optica spectrum disorder (NMOSD) patients, as well as its association with inflammatory activity and prognostic factors of APS severity in a real-world setting. METHODS: We conducted a retrospective study on a cohort of Latin American (LATAM) NMOSD patients who had experienced APS during their follow-up. Patients from Mexico, Peru, Brazil, Colombia, Panama, Chile and Argentina patients who met 2015 NMOSD criteria were included. We evaluated data on symptom type (nausea, vomiting and/or hiccups), frequency, duration, severity (measured by APS severity scale), association with other NMOSD core relapses, and acute treatments (symptomatic and immunotherapy or plasmapheresis). Logistic regression was conducted to evaluate factors associated with APS severity (vs. mild-moderate). RESULTS: Out of 631 NMOSD patients, 116 (18.3%) developed APS during their follow-up. The most common APS phenotype was severe. Inflammatory activity (i.e., relapses) significantly decreased after the onset of APS. Half of the patients experienced isolated APS with a median duration of 10 days, and the most frequently used acute treatment was IV steroids. All three symptoms were present in 44.6% of the patients. APS symptoms resolved following immunotherapy. Logistic regression did not identify independent factors associated with the severity of APS. CONCLUSIONS: Our findings indicate that 18.3% of NMOSD patients developed APS during the follow-up period, with most patients fulfilling criteria for severe APS. The inflammatory activity decreased after the onset of APS compared to the previous year.