Limited Medical Resources Research Articles

Insisting on help, facing challenges, and maintaining balance: The lived experiences of caregivers raising a child with developmental delay in the remote islands of Kinmen

PurposeTo explore the lived experiences of the caregivers of children with developmental delay (DD) on the remote island of Kinmen, Taiwan. Design and methodsThis descriptive phenomenological study included 14 caregivers of 16 children with DD recruited from Kinmen, Taiwan. Data were collected from September 2020 to February 2021 with face-to-face semi-structured interviews, which were transcribed verbatim and analyzed using thematic analysis. ResultsTwelve caregivers were mothers; there was one father and one grandmother. Their mean age was 37.93 ± 5.17 years. The mean age of the children was 3.90 ± 1.49 years; 11 were male (69%). As we began our study, the COVID-19 pandemic occurred, which influenced two of the three themes: (1) focusing on their child's delayed development, which involved seeking a diagnosis and blaming themselves; (2) facing barriers to caring a child with DD on a remote island, which included ostracization of their child and limited off-island medical resources, especially during COVID-19; and (3) maintaining a healthy life balance, which involved protecting their child from physical or spiritual harm, including exposure to COVID-19, and reflecting on their strengths. ConclusionsOur findings highlight the challenges of caring for a child with DD in the Kinmen islands of Taiwan, and the impact of the COVID-19 pandemic. Practical implicationsHealthcare professionals are encouraged to provide caregivers of children with DD living in remote, island communities an opportunity to share their caregiving experiences and provide strategies for obtaining medical care, which could help reduce caregiver burden.

The impact of social media advertisements and treatments on the dynamics of infectious diseases with optimal control strategies

The dissemination of public health information through television and social media posts is essential for informing the public about the transmission of contagious diseases, which is crucial in preventing the spread of various infectious diseases. In this paper, we propose a non-linear mathematical model to assess the effect of advertisements through social media in creating awareness and limiting treatment on spreading infectious diseases. These initiatives may alter population behaviour and divide the susceptible population into subgroups. In addition, to comprehend these dynamics better, we use half-saturation constant rates for media coverage and treatment. The model’s well-posedness and feasibility are evaluated. The possible biological equilibrium points are calculated. Local and global stability are carried out. The objective of our study is to produce the model’s bifurcation. Transcritical, Saddle–node, Hopf bifurcation of codimension 1 and Cusp, Generalized-Hopf (Bautin), and Bogdanov–Takens (BT) bifurcation of codimension 2 are studied for this purpose. Due to the limited medical resources and supply efficiency, the model exhibits backward bifurcation, resulting in bistability. Moreover, the occurrence condition for stability and direction of Hopf bifurcation is discussed. This model study demonstrates that the system is significantly influenced by the pace with which awareness programmes are implemented and that raising this value above a threshold may result in continuous oscillation. Sensitivity analysis employs the normalized forward sensitivity index of the basic reproduction number to provide a comprehensive understanding of the effect of various parameters on accelerating and limiting disease spread. Further, the minimum possible cost is determined by formulating an optimal control system based on sensitivity analysis and applying Pontryagin’s maximum principle. Methods of cost-effectiveness, such as ACER and ICER, are used to determine the most cost-effective control intervention strategy among all the strategies. Numerical simulations have been done to support all theoretical findings.

Study of the impact of information and limited medical resources on Zika prevalence: an optimal control approach

Study of the impact of information and limited medical resources on Zika prevalence: an optimal control approach

Combination strategy for prognostication in patients undergoing post-resuscitation care after cardiac arrest

This study investigated the prognostic performance of combination strategies using a multimodal approach in patients treated after cardiac arrest. Prospectively collected registry data were used for this retrospective analysis. Poor outcome was defined as a cerebral performance category of 3–5 at 6 months. Predictors of poor outcome were absence of ocular reflexes (PR/CR) without confounding factors, a highly malignant pattern on the most recent electroencephalography, defined as suppressed background with or without periodic discharges and burst-suppression, high neuron-specific enolase (NSE) after 48 h, and diffuse injury on imaging studies (computed tomography or diffusion-weighted imaging [DWI]) at 72–96 h. The prognostic performances for poor outcomes were analyzed for sensitivity and specificity. A total of 130 patients were included in the analysis. Of these, 68 (52.3%) patients had poor outcomes. The best prognostic performance was observed with the combination of absent PR/CR, high NSE, and diffuse injury on DWI [91.2%, 95% confidence interval (CI) 80.7–97.1], whereas the combination strategy of all available predictors did not improve prognostic performance (87.8%, 95% CI 73.8–95.9). Combining three of the predictors may improve prognostic performance and be more efficient than adding all tests indiscriminately, given limited medical resources.

The current status and implications of the medical insurance fee schedule in Japan

Background: Korea has the most rapidly aging population in the world. Medical costs for the elderly are quickly increasing, which raises concerns about the sustainability of health insurance finances. Accordingly, the need to allocate limited medical resources efficiently has increased, with improving the fee schedule seen as an effective way to achieve this. Japan has experienced a super-aging society for at least 20 years prior to Korea and has been successful in improving its fee schedule. Korea’s fee schedule, however, needs to overcome the challenge of being relatively limited in type and simplicity.Current Concepts: The new patient consultation fee in Japan is the same regardless of the type of medical institution and is approximately 40% higher than found in Korea. In the case of established patient consultations, the fee for medical institutions with fewer than 200 beds are substantially higher than for large medical institutions, thereby suppressing re-examination at large hospitals. Japan’s additional points consist of 71 types of items, which is more diverse than Korea’s 28 types, and the additional points for children under six years of age, holidays, and late nights are set significantly higher than that found in Korea.Discussion and Conclusion: Japan designed its fee schedule to structurally reduce the consumption of medical resources and to effectively compensate for the behavior of medical providers. This policy is a standard which Korea’s fee schedule should aim for. In the future, it will be necessary to prepare measures to respond to the super-aging environment through detailed research concerning Japan’s fee schedule.

Performance Assessment of a Rehabilitation Transportation Reservation Matching Service with Market Design Mechanisms

Government agencies provide huge amount of subsidies to support the rehabilitation transportation service over the past decade in eastern Taiwan; however, low demand request fulfillment rate, limited medical and transportation resources, long travel distances, and an extremely high percentage of dead mileages are still the main challenges faced by rehabilitation transportation service providers. This study applies the market design theory to match the rehabilitation buses with the requests of patients, so as to improve resource utilization efficiency in rural areas. The developed market design mechanisms aim to allocate resources to those who need them most in a matching manner, by using the deferred acceptance algorithm and the top trading cycle approach. The model is initialized with the requests of those who choose the rehabilitation bus based on their desired boarding time slots. On the other hand, the service providers of the rehabilitation bus would determine patients’ schedule based on their disability level, willingness to share the ride, number of fulfilled appointments during this month, and the travel distance of this trip as the order of preference. Since the current vehicle dispatching rule is to reserve seats of a rehabilitation bus on the “first-come-first-served” basis, and it cannot fully satisfy patients need. In accordance with the historical data, 63 of 72 demand requests could successfully reserve the seats. In the “first-come, first-served” mode, 48 requests obtained the first-ranking shift (i.e., their desired time slots), and the sum of their disability level score is 155. In the market design matching mode, 57 requests obtained the first-ranking shift, and the sum of their disability level score is 170, which demonstrates that the proposed market design matching mechanism outperforms than the conventional rules.

A Registry-based Audit of a Current Situation of Clinical Research in COVID-19 Using Artificial Intelligence Techniques: A Systematic Review

Background: The term artificial intelligence refers to the use of computers and technology to replicate intelligent behaviour and critical thinking similar to that of a human being. In COVID-19, artificial intelligence has been widely applied in diagnostics, public health, clinical decision-making, social control, treatments, vaccine development, monitoring, integration with big data, operation of additional vital clinical services, and patient management. Hence, we conceptualized this study to evaluate the usage of artificial intelligence as a tool at the time of the COVID-19 pandemic. Methods: From December, 2019, to May, 2022, all clinical trials using AI approaches listed on clinicaltrials.gov and ctri.gov.in were examined and analysed. Results: Out of 8072 studies on COVID-19 listed on ClinicalTrials.gov and 674 studies on the CTRI website, 53 studies were related to AI. Ten (18.9%) of the 53 studies were interventional, while the remaining 43 (81.1%) were observational. Conclusion: With limited medical resources and growing healthcare strain, the introduction of AI approaches will increase human efficiency and capacity to combat the COVID-19 pandemic. In this review, artificial intelligence was proven to be more accurate than human specialists in COVID-19 diagnosis and medication discovery.

Patient delay in chronic kidney disease: A qualitative study.

This study aimed to investigate the reasons for patient delay in chronic kidney disease (CKD) and provide a scientific basis for implementing effective interventions. With the adoption of the phenomenological method in qualitative research, semi-structured, face-to-face interviews were conducted with 14 cases, and the Colaizzi seven-step analysis method was used to analyze the interview data and refine the themes. A total of 4 themes were obtained, namely, a cognitive explanation of illness, negative psychological emotions, socioeconomic levels, and limited medical resources. The current status of patient delay in chronic kidney disease is serious, and there are various reasons for it. Health management departments and healthcare providers at all levels should pay attention to this situation and provide targeted supportive interventions and health education to help patients establish the correct awareness of medical consultation and effectively improve their quality of survival.

StratMed: Relevance stratification between biomedical entities for sparsity on medication recommendation

With the growing imbalance between limited medical resources and escalating demands, AI-based clinical tasks have become paramount. As a sub-domain, medication recommendation aims to amalgamate longitudinal patient history with medical knowledge, assisting physicians in prescribing safer and more accurate medication combinations. Existing works ignore the inherent long-tailed distribution of medical data, have uneven learning strengths for hot and sparse data, and fail to balance safety and accuracy. To address the above limitations, we propose StratMed, which introduces a stratification strategy that overcomes the long-tailed problem and achieves fuller learning of sparse data. It also utilizes a dual-property network to address the issue of mutual constraints on the safety and accuracy of medication combinations, synergistically enhancing these two properties. Specifically, we construct a pre-training method using deep learning networks to obtain medication and disease representations. After that, we design a pyramid-like stratification method based on relevance to strengthen the expressiveness of sparse data. Based on this relevance, we design two graph structures to express medication safety and precision at the same level to obtain patient representations. Finally, the patient’s historical clinical information is fitted to generate medication combinations for the current health condition. We employed the MIMIC-III dataset to evaluate our model against state-of-the-art methods in three aspects comprehensively. Compared to the sub-optimal baseline model, our model reduces safety risk by 15.08%, improves accuracy by 0.36%, and reduces training time consumption by 81.66%. Our source code is publicly available at: https://github.com/lixiang-222/StratMed

COVision: convolutional neural network for the differentiation of COVID−19 from common pulmonary conditions using CT scans

With the growing amount of COVID-19 cases, especially in developing countries with limited medical resources, it is essential to accurately and efficiently diagnose COVID-19. Due to characteristic ground-glass opacities (GGOs) and other types of lesions being present in both COVID-19 and other acute lung diseases, misdiagnosis occurs often — 26.6% of the time in manual interpretations of CT scans. Current deep-learning models can identify COVID-19 but cannot distinguish it from other common lung diseases like bacterial pneumonia. Concretely, COVision is a deep-learning model that can differentiate COVID-19 from other common lung diseases, with high specificity using CT scans and other clinical factors. COVision was designed to minimize overfitting and complexity by decreasing the number of hidden layers and trainable parameters while still achieving superior performance. Our model consists of two parts: the CNN which analyzes CT scans and the CFNN (clinical factors neural network) which analyzes clinical factors such as age, gender, etc. Using federated averaging, we ensembled our CNN with the CFNN to create a comprehensive diagnostic tool. After training, our CNN achieved an accuracy of 95.8% and our CFNN achieved an accuracy of 88.75% on a validation set. We found a statistical significance that COVision performs better than three independent radiologists with at least 10 years of experience, especially in differentiating COVID-19 from pneumonia. We analyzed our CNN’s activation maps through Grad-CAMs and found that lesions in COVID-19 presented peripherally, closer to the pleura, whereas pneumonia lesions presented centrally.

Treating the dead; how far ought medicine go to obtain transplantable organs?

Under what circumstances, is it ethical to perform tumor surgery on a brain-dead individual? The neurosurgeons at Brigham and Women's Hospital were recently faced with such a question when asked to operate on a 28-year-old man who was pronounced brain-dead secondary to a severe brain-stem injury. His advanced directives clearly documented a desire for organ donation. During his transplant work-up, cranial imaging suggested a possible cerebellar mass of unknown etiology that was concerning for metastatic disease. Despite negative full body imaging, the neurosurgical team was asked to perform an open biopsy of the intracranial lesion to rule out occult systemic cancer. This case invites many nuanced questions related to the decisions surgeons and the broader medical community must make in the face of pursuing viable organs for the many in need. What is the moral standing and personhood eligibility of brain-dead individuals? What is the scope of medical interventions and procedures that surgeons are ethically bound to carry out? How ought the desire for increased medical intervention to try to save organs be balanced with practical limitations given limited medical resources?

An Accessible Pre-Rehabilitation Bundle for Patients Undergoing Elective Heart Valve Surgery with Limited Resources: The TIME Randomized Clinical Trial.

Despite gradually increasing evidence for pre-rehabilitation for heart valve surgery, it remains underused, especially in developing countries with limited resources. The study aimed to investigate the feasibility and effects of an innovative three-day pre-rehabilitation bundle for patients undergoing elective heart valve surgery. This was a single-center, assessor-blind, randomized clinical trial. A total of 165 patients were randomly assigned to either usual care (control group, n = 83) or usual care with an additional 3-day pre-rehabilitation bundle (Three-day of Inspiratory muscle training, aerobic Muscle training, and Education (TIME) group, n = 82). The main outcome of the study was the incidence of postoperative pulmonary complications (PPCs). Secondary outcomes included the feasibility of the intervention, duration of the non-invasive ventilator, length of stay, and PPCs-related medical costs on discharge. Of 165 patients 53.94% were male, the mean age was 63.41 years, and PPCs were present in 26 of 82 patients in the TIME group and 44 of 83 in the control group (odds ratio (OR), 0.60; 95% CI, 0.41-0.87, p = 0.006). The feasibility of the pre-rehabilitation bundle was good, and no adverse events were observed. Treatment satisfaction and motivation scored on 10-point scales, were 9.1 0.8 and 8.6 1.4, respectively. The TIME group also had fewer additional PPCs-related medical costs compared to the control group (6.96 vs. 9.57 thousand CNY (1.01 vs. 1.39 thousand USD), p 0.001). The three-day accessible pre-rehabilitation bundle reduces the incidence of PPCs, length of stay, and PPCs-related medical costs in patients undergoing elective valve surgery. It may provide an accessible model for the expansion of pre-rehabilitation in countries and regions with limited medical resources. This trial was based on the Consolidated Standards of Reporting Trials (CONSORT) guidelines. This trial was registered in the Chinese Clinical Trial Registry (identifier ChiCTR2000039671).

Abstract 13850: Cost-Effectiveness Analysis of Universal Screening for Atrial Fibrillation Using Electrocardiographic Artificial Intelligence in a Rural Community in Taiwan

Introduction: Atrial fibrillation (AF) substantially contributes to strokes, and screening for AF is generally recommended. However, implementing universal screening for AF is challenging in rural areas with limited medical resources. This study assessed the cost-effectiveness of using artificial intelligence-enabled 12-lead electrocardiography (AI-ECG) for AF screening in a rural community. Methods: The cost-effectiveness analysis focused on screening individuals aged 65 or older and utilized a lifelong decision analytic Markov model. The AI-ECG for AF was trained at a medical center in Taiwan, achieving a sensitivity of 0.944 (standard error [SE]: 0.015) and specificity of 0.941 (SE: 0.008). Costs and efficacy of anticoagulant treatments, utility of health status, and clinical variables such as stroke and major bleeding risks were estimated based on literature and epidemiological data from Taiwan. Results were presented as US dollars per quality-adjusted life year (QALY). The base-case analysis compared technicians using AI-ECG for AF detection and referral, and physicians' evaluation with standard 12-lead ECGs to no screening. Uncertainty in the model was addressed through probabilistic sensitivity analysis. Results: Both AI-ECG screening and screening by a physician were found to be more costly yet more effective when compared to no screening. While both strategies exhibited similar effectiveness, the cost was lower with AI-ECG than by a physician ($453 versus $490). Based on 10,000 Monte Carlo simulations, if the willingness to pay (WTP) for a QALY exceeds $9,514, screening by a physician is a cost-effective strategy. However, if the WTP per QALY falls between $2,289 and $9,514, AI-ECG screening is more cost-effective than physician-based screening. Conclusion: This economic evaluation demonstrates the cost-effectiveness of AI-ECG screening for AF, making it a practical alternative for AF screening in areas with limited medical resources.

Morbidity During the Early Interwar Period (1923-27): A Historical-Epidemiological Study of 15,146 Cases Treated at the "Agios Dimitrios" Hospital in Thessaloniki, Greece.

Background and objective The period spanning 1923-1927was a turbulent period in Greek history following the catastrophic defeat of the Greek armyin September 1922, known as the "Asia Minor Catastrophe". The massive settlement of refugees in Thessaloniki, Greece, entailed massive economic, public health, and social challenges.The present historical-epidemiological study aims to evaluate the diseases of individuals hospitalized at the "Agios Dimitrios" Hospital inThessaloniki during the aforementionedperiod. Materials and methods This study involved 15,146 consecutive patients (January 1923-March 1927)treated at the hospital. Data were collected from the General Hospital of Thessaloniki "Agios Dimitrios" and were manually entered into a pre-coded database. Descriptive statistics were calculated. In addition, the case fatality rates (CFR) were calculated; the respective 95% confidence intervals (CI) were estimated. Results Themost frequent causesfor admission to the hospital were as follows: normal delivery/delivery without disclosed sequalae (n=1915, 12.7%), followed by tuberculosis (n=1514, 10.0%), malaria (n=1438, 9.5%), injuries/falls/fractures (n=1394, 9.2%), pneumonia/pleuritis (n=1010, 6.7%), appendicitis (n=623, 4.1%), dysentery/enterocolitis/typhoid (n=489, 3.2%), salpingitis/salpingo-oophoritis (n=358, 2.4%), soft tissue abscesses (n=309, 2.0%), hernias (n=295, 2.0%), rabies (n=239, 1.6%), metrorrhagia/menorrhagia (n=233, 1.5%), ocular cataract (n=225, 1.5%), postpartum infections/endometritis (n=181, 1.2%), uterine discomfort/uterine pain (n=162, 1.1%), nephritis/uremia (n=157, 1.0%), miscarriage (n=155, 1.0%), skin infections/inflammations excluding abscesses (n=152, 1.0%), otitis/mastoiditis/labyrinthitis (n=96, 0.6%), and peptic ulcer (n=93, 0.6%). Tuberculosis was particularly associated with high CFR (49.5%, 95% CI: 47.2-52.3%), followed by nephritis/uremia (CFR: 37.6%), dysentery/infectious enterocolitis/typhoid (CFR: 24.3%), peptic ulcer (CFR: 22.6%), pneumonia/pleuritis (CFR: 16.1%), postpartum infections/endometritis(CFR: 15.5%). Conclusions Infections predominated in the disease spectrum of the hospitalized population. The documented fatality rates were high; poor outcomes may reflect the socioeconomic adversities and limited medical means and resources available at that time.

Dynamic analysis of an Ebola epidemic model incorporating limited medical resources and immunity loss

Dynamic analysis of an Ebola epidemic model incorporating limited medical resources and immunity loss

Deep Learning Performance of Ultra-Widefield Fundus Imaging for Screening Retinal Lesions in Rural Locales

Retinal diseases are the leading cause of irreversible blindness worldwide, and timely detection contributes to prevention of permanent vision loss, especially for patients in rural areas with limited medical resources. Deep learning systems (DLSs) based on fundus images with a 45° field of view have been extensively applied in population screening, while the feasibility of using ultra-widefield (UWF) fundus image-based DLSs to detect retinal lesions in patients in rural areas warrants exploration. To explore the performance of a DLS for multiple retinal lesion screening using UWF fundus images from patients in rural areas. In this diagnostic study, a previously developed DLS based on UWF fundus images was used to screen for 5 retinal lesions (retinal exudates or drusen, glaucomatous optic neuropathy, retinal hemorrhage, lattice degeneration or retinal breaks, and retinal detachment) in 24 villages of Yangxi County, China, between November 17, 2020, and March 30, 2021. The captured images were analyzed by the DLS and ophthalmologists. The performance of the DLS in rural screening was compared with that of the internal validation in the previous model development stage. The image quality, lesion proportion, and complexity of lesion composition were compared between the model development stage and the rural screening stage. A total of 6222 eyes in 3149 participants (1685 women [53.5%]; mean [SD] age, 70.9 [9.1] years) were screened. The DLS achieved a mean (SD) area under the receiver operating characteristic curve (AUC) of 0.918 (0.021) (95% CI, 0.892-0.944) for detecting 5 retinal lesions in the entire data set when applied for patients in rural areas, which was lower than that reported at the model development stage (AUC, 0.998 [0.002] [95% CI, 0.995-1.000]; P < .001). Compared with the fundus images in the model development stage, the fundus images in this rural screening study had an increased frequency of poor quality (13.8% [860 of 6222] vs 0%), increased variation in lesion proportions (0.1% [6 of 6222]-36.5% [2271 of 6222] vs 14.0% [2793 of 19 891]-21.3% [3433 of 16 138]), and an increased complexity of lesion composition. This diagnostic study suggests that the DLS exhibited excellent performance using UWF fundus images as a screening tool for 5 retinal lesions in patients in a rural setting. However, poor image quality, diverse lesion proportions, and a complex set of lesions may have reduced the performance of the DLS; these factors in targeted screening scenarios should be taken into consideration in the model development stage to ensure good performance.

Ethical Issues Regarding Neurosurgical Management ofPenetrating Brain Injury inthe French Armed Forces.

Combat penetrating brain injury (PBI) differs significantly from PBI in civilian environments. Differences include technical factors such as the weapons involved, strained resource environments, and limited medical materials and human resources available. Ethical issues regarding the management of PBI in military settings may occur. This case study examines the case of a 20-year-old member of the French Armed Forces that suffered a penetrating brain injury in a combat situation. The four-quadrant method along with the four principles of medical ethics (respect for autonomy, beneficence, nonmaleficence, and justice) was used to analyze this case and to apply ethics to the practice of military medicine. Nowadays, we possess the medical and surgical resources as well as the aeromedical evacuation capability to save the life of a soldier with a penetrating craniocerebral wound. Nonetheless, the functional outcome of this type of wound places military doctors in an ethical dilemma. The line of conduct and clinical protocol established by the French Medical Health Service is to manage all PBIs when the patient's life can be saved and to provide all available financial and social support for the rehabilitation of patients and their family.

Rapid Segmentation and Diagnosis of Breast Tumor Ultrasound Images at the Sonographer Level Using Deep Learning.

Breast cancer is one of the most common malignant tumors in women. A noninvasive ultrasound examination can identify mammary-gland-related diseases and is well tolerated by dense breast, making it a preferred method for breast cancer screening and of significant clinical value. However, the diagnosis of breast nodules or masses via ultrasound is performed by a doctor in real time, which is time-consuming and subjective. Junior doctors are prone to missed diagnoses, especially in remote areas or grass-roots hospitals, due to limited medical resources and other factors, which bring great risks to a patient's health. Therefore, there is an urgent need to develop fast and accurate ultrasound image analysis algorithms to assist diagnoses. We propose a breast ultrasound image-based assisted-diagnosis method based on convolutional neural networks, which can effectively improve the diagnostic speed and the early screening rate of breast cancer. Our method consists of two stages: tumor recognition and tumor classification. (1) Attention-based semantic segmentation is used to identify the location and size of the tumor; (2) the identified nodules are cropped to construct a training dataset. Then, a convolutional neural network for the diagnosis of benign and malignant breast nodules is trained on this dataset. We collected 2057 images from 1131 patients as the training and validation dataset, and 100 images of the patients with accurate pathological criteria were used as the test dataset. The experimental results based on this dataset show that the MIoU of tumor location recognition is 0.89 and the average accuracy of benign and malignant diagnoses is 97%. The diagnosis performance of the developed diagnostic system is basically consistent with that of senior doctors and is superior to that of junior doctors. In addition, we can provide the doctor with a preliminary diagnosis so that it can be diagnosed quickly. Our proposed method can effectively improve diagnostic speed and the early screening rate of breast cancer. The system provides a valuable aid for the ultrasonic diagnosis of breast cancer.

In-hospital adverse outcomes and risk factors among chronic kidney disease patients infected with the omicron variant of SARS-CoV-2: a single-center retrospective study

IntroductionThe SARS-CoV-2 Omicron variant has decreased virulence and pathogenicity, yet the number of Omicron infections worldwide is unprecedentedly high, with rather high mortality and severe disease rate. Chronic kidney disease (CKD) patients are particularly vulnerable to the SARS-CoV-2 Omicron variant and have unique clinical outcomes.MethodsWe retrospectively collected data from 2140 hospitalized patients with SARS-CoV-2 Omicron variant infection from March 29, 2022, to May 17, 2022. Demographic characteristics, ancillary examination results, and clinical treatments were described. Occurrence of critical COVID-19 or death and time of positive-to-negative conversion was defined as primary outcomes. The presence of COVID-19 pneumonia and the usage of respiratory or circulatory support was defined as secondary outcomes. Univariate or multivariate logistic regression analyses were performed to identify risk factors for primary outcomes.Results15.74% of CKD patients infected with the SARS-CoV-2 Omicron variant ended up with critical COVID-19 or death. Pre-existing CKD was a risk factor for critical COVID-19 or death and prolonged time of positive-to-negative conversion of SARS-CoV-2. Nirmatrelvir-ritonavir facilitated viral clearance among COVID-19 patients with non-severe CKD.ConclusionWe found patients with CKD and COVID-19 due to Omicron experienced worse clinical outcomes and prolonged time of positive-to-negative conversion of SARS-CoV-2 compared to patients without CKD, which helps rationalize limited medical resources and offers guidance for appropriate clinical treatments.

Computer-Aided Autism Spectrum Disorder Diagnosis With Behavior Signal Processing

Behavioral observation plays an essential role in the diagnosis of Autism Spectrum Disorder (ASD) by analyzing children’s atypical patterns in social activities (e.g., impaired social interaction, restricted interests, and repetitive behavior). To date, this process still heavily relies on the questionnaire survey, clinical observation, or retrospective video analysis, leading to high demand for professionals with massive labor costs. This paper proposes a standardized platform for stimulating, gathering, analyzing, modeling, and interpreting human behavioral data in the application of computer-aided ASD diagnosis. By a structured assessment process, the proposed system can automatically evaluate children’s multiple social interaction skills using the captured audio-visual data and provide the final diagnostic suggestions. We collect a multimodal behavioral database of 95 participants (71 children with ASD and 24 age-matched typical controls) in a real clinic environment, the Third Affiliated Hospital of Sun Yat-sen University, China. On the clinical database, our proposed computer-aided ASD diagnosis system obtains an accuracy of 88.42% for identifying ASD children with an average age of 24 months, representing a performance comparable to top-level human experts. As a unified and replicable solution, it has good potential to be promoted to less developed areas with limited high-quality medical resources.