Clinical presentation, diagnosis and management of multisystem inflammatory syndrome in children (MIS-C): a systematic review.

BACKGROUND: Knowledge about multisystem inflammatory syndrome in children (MIS-C) is evolving, and evidence-based standardised diagnostic and management protocols are lacking. Our review aims to summarise the clinical and diagnostic features, management strategies and outcomes of MIS-C and evaluate the variances in disease parameters and outcomes between high-income countries (HIC) and middle-income countries (MIC). METHODS: We searched four databases from December 2019 to March 2023. Observational studies with a sample size of 10 or more patients were included. Mean and prevalence ratios for various variables were pooled by random effects model using R. A mixed generalised linear model was employed to account for the heterogeneity, and publication bias was assessed via funnel and Doi plots. The primary outcome was pooled mean mortality among patients with MIS-C. Subgroup analysis was conducted based on the income status of the country of study. RESULTS: A total of 120 studies (20 881 cases) were included in the review. The most common clinical presentations were fever (99%; 95% CI 99.6% to 100%), gastrointestinal symptoms (76.7%; 95% CI 73.1% to 79.9%) and dermatological symptoms (63.3%; 95% CI 58.7% to 67.7%). Laboratory investigations suggested raised inflammatory, coagulation and cardiac markers. The most common management strategies were intravenous immunoglobulins (87.5%; 95% CI 82.9% to 91%) and steroids (74.7%; 95% CI 68.7% to 79.9%). Around 53.1% (95% CI 47.3% to 58.9%) required paediatric intensive care unit admissions, and overall mortality was 3.9% (95% CI 2.7% to 5.6%). Patients in MIC were younger, had a higher frequency of respiratory distress and evidence of cardiac dysfunction, with a longer hospital and intensive care unit stay and had a higher mortality rate than patients in HIC. CONCLUSION: MIS-C is a severe multisystem disease with better mortality outcomes in HIC as compared with MIC. The findings emphasise the need for standardised protocols and further research to optimise patient care and address disparities between HIC and MIC. PROSPERO REGISTRATION NUMBER: CRD42020195823.

Novel data visualization and risk analytic algorithm in a low-middle income country.

To discuss the use of T3™, a data aggregation, visualization, and risk analytic platform in a single centre and its framework for implementation of such a tool in clinical care. We share experience of a tool implemented in a tertiary care Intensive Care Unit (ICU) with limited resources. Superusers were identified and trained. Implementation involved monitoring, evaluation, and user engagement data for continuous emphasis on the use of this tool. Persistent display of T3 data enhanced nursing operational efficiency. Its use was expanded to use in nurses rounds and handover, mortality and morbidity meetings, clinical team teaching through selected teaching cases and analysis of stored data with different research questions. However, lack of infrastructure and technological comprehension, paucity of multidisciplinary teams makes it a challenge in its implementation. Clear framework of implantation and pre-designed studies to determine the clinical usage and effectiveness are important for wide-spread use of such tools.

Factors predicting mortality among patients with COVID-19 associated hospital acquired pneumonia: insights from a tertiary care center.

Hospital acquired pneumonia (HAP) is a severe and dangerous complication in patients admitted with COVID-19, causing significant morbidity and mortality globally. However, the early detection and subsequent management of high-risk cases may prevent disease progression and improve clinical outcomes. This study was undertaken in order to identify predictors of mortality in COVID-19 associated HAP. A retrospective study was performed on all patients who were admitted to a tertiary care center with COVID-19 associated HAP from July 2020 till November 2020. Data was collected on relevant demographic, clinical and laboratory parameters to determine their association with in-hospital mortality; 1574 files were reviewed, out of which 162 were included in the final study. The mean age of subjects was 59.4±13.8 and a majority were male (78.4%). There were 71 (48.3%) mortalities in the study sample. Klebsiella pneumoniae (31.5%) and Pseudomonas aeruginosa (30.2%) were the most common organisms overall. Clinically significant growth of Aspergillus sp. was observed in 41 (29.0%) of patients. On univariate analysis, several factors were found to be associated with mortality, including male gender (p=0.04), D-dimers >1.3 mg/L (p<0.001), ferritin >1000 µg/mL (p<0.001), LDH >500I.U/mL (p<0.001) and procalcitonin >2.0 µg/mL (p<0.001). On multivariate analysis, ferritin >1000ng/mL, initial site of care in Special Care Units or Intensive Care Units, developing respiratory failure and developing acute kidney injury were factors independently associated with mortality in our patient sample. These results indicate that serum ferritin levels may be a potentially useful biomarker in the management of COVID-19 associated HAP.

Shoulder pain and functional disability in type 1 diabetic patients: A cross-sectional survey.

OBJECTIVE: To assess the prevalence of shoulder pain and functional disability (SPFD) in Type-1 diabetic patients, and to explore its association with duration of the disease, age and gender. METHODS: A cross-sectional survey was carried out on previously diagnosed patients with Type-1 diabetes mellitus between April 2019 and March 2020. Data was collected from six hospitals including three tertiary care hospitals of Islamabad and Rawalpindi. Three hundred and twenty-eight patients were recruited through convenience sampling. Shoulder Pain and Disability Index was used to determine SPFD among participants. Point-biserial and Pearson correlation coefficients were calculated to find out the correlation between the variables. Independent t-test was used to determine the difference in the mean scores between the variables. RESULTS: The prevalence of SPFD was found 85.7%. A significant correlation was found of the SPFD with age (r = 0.332, p < 0.001), duration of the diabetes mellitus (r = 0.154, p = 0.005) and gender (rpb = 0.171, p = 0.002). A significant difference was found in SPFD mean scores between female and male patients (female patients = 43.42±22.80, male patients = 35.31±22.91, p = 0.002). CONCLUSION: SPFD seems prevalent among Type-1 diabetic patients. Increasing age, long history of diabetes mellitus and female gender appear the associated risk factors for the shoulder pain and disability.

A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data.

Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.

Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme.

Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.

Frequency of wrist pain and its associated risk factors in students using mobile phones.

OBJECTIVE: To assess the frequency of wrist pain in students due to mobile phone usage, and impact of usage hours and screen size of mobile phones on pain and disability at wrist joint. METHODS: A cross-sectional survey was conducted among students studying in different universities of Islamabad and Rawalpindi belonging to both public and private sectors. The study was conducted between May 2018 and March 2019. Sample size was 360 students which were selected through convenience sampling. Data was collected through self-formulated closed ended questionnaire. Patient Rated Wrist Evaluation questionnaire was used to assess pain and disability at wrist joint. Data entry and analysis were done using SPSS 21. Results were analyzed using descriptive statistics. Spearman's and point-biserial correlation coefficients were applied to determine association between different variables. RESULTS: Point, last month, last 3 months, last 6 months, last year and lifetime frequency were found to be 9%, 18.6%, 29%, 33.3%, 42% and 45.3% respectively. Duration of mobile phone usage was found to be of significant association factor that could lead to wrist pain and disability (p=0.004). Wrist pain was not significantly related to mobile phone screen size (p=0.488). CONCLUSION: It appears that wrist pain is common among mobile phone users and an increase in use of mobile phones increased pain and disability of wrist joint. In addition, it seems that screen size of mobile phone has no significant effect on pain and disability of wrist joint.