Monitoring Of COVID-19 Patients Research Articles

Quadruplex Droplet Digital PCR Assay for Screening and Quantification of SARS-CoV-2.

The ongoing COVID-19 pandemic, caused by the rapid global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) since early 2020, has highlighted the need for sensitive and reliable diagnostic methods. Droplet digital PCR (ddPCR) has demonstrated superior performance over the gold-standard reverse transcription PCR (RT-PCR) in detecting SARS-CoV-2. In this study, we explored the development of a multiplex ddPCR assay that enables sensitive quantification of SARS-CoV-2, which could be utilized for antiviral screening and the monitoring of COVID-19 patients. We designed a quadruplex ddPCR assay targeting four SARS-CoV-2 genes and evaluated its performance in terms of specificity, sensitivity, linearity, reproducibility, and precision using a two-color ddPCR detection system. The results showed that the quadruplex assay had comparable limits of detection and accuracy to the simplex ddPCR assays. Importantly, the quadruplex assay demonstrated significantly improved performance for samples with low viral loads and ambiguous results compared to the standard qRT-PCR approach. The developed multiplex ddPCR represents a valuable alternative and complementary tool for the diagnosis of SARS-CoV-2 and potentially other pathogens in various application scenarios beyond the current COVID-19 pandemic. The improved sensitivity and reliability of this assay could contribute to more effective disease monitoring and antiviral screening during the ongoing public health crisis.

Significance of leukogram-derived indices (NLR, LMR and NER) in the monitoring of COVID-19 patients

Significance of leukogram-derived indices (NLR, LMR and NER) in the monitoring of COVID-19 patients

B-046 Curious Thing, an Artificial Intelligence (AI) Based Conversational Agents in COVID-19 Patient Management

Abstract Background There are no clear guidelines or validated models for artificial intelligence (AI) based approaches in monitoring of COVID-19 patients who were isolated in the community, in order to identify early deterioration of their health symptoms. Methods Developed in partnership with Curious Thing (CT), Sydney-based AI conversational technology, a new care robot technology was introduced in South Western Sydney (SWS) in September 2021 to manage the large numbers of low to medium risk patients with a COVID-19 diagnosis and isolated at home. The CT interface made contact with patients via their mobile phone, following a locally produced script to obtain information recording physical condition, wellness and support. Results Between 2nd September to 14th December 2021, there were 32 001 conversations (every phone call that resulted in a conversation with a patient) conducted with 6323 unique patients (patients included in call lists identified by phone number) engaged. Over half of the completed calls (n = 13 143, 53.7%) identified that individuals needed no further support (freeing up valuable clinician time). 4.24% (n = 1077) identified as needing urgent support (getting to them faster than a standard clinician dialling pattern). The AI assisted phone calls effectively identified the patients requiring further support, saved clinician time by monitoring less ailing patients remotely and enabled them to spend more time on critically ill patients, thus ensured that service and supply resources could be directed to those at greatest need. Conclusion Based on the severity of the COVID-19, different approaches have been adopted by the healthcare professionals for handling the patients in the most effective manner. This paper describes the AI assisted conversational agent which can act as an alternate approach to identify the deteriorating patients requiring further support while they are isolated at home. The findings are intended to inform policy makers and health professionals about the implications of the use of these AI-based technologies for the management of current or any future pandemic.

Tele-monitoring system for intensive care ventilators in isolation rooms

The COVID-19 pandemic and discovery of new mutant strains have a devastating impact worldwide. Patients with severe COVID-19 require various equipment, such as ventilators, infusion pumps, and patient monitors, and a dedicated medical team to operate and monitor the equipment in isolated intensive care units (ICUs). Medical staff must wear personal protective equipment to reduce the risk of infection. This study proposes a tele-monitoring system for isolation ICUs to assist in the monitoring of COVID-19 patients. The tele-monitoring system consists of three parts: medical-device panel image processing, transmission, and tele-monitoring. This system can monitor the ventilator screen with obstacles, receive and store data, and provide real-time monitoring and data analysis. The proposed tele-monitoring system is compared with previous studies, and the image combination algorithm for reconstruction is evaluated using structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The system achieves an SSIM score of 0.948 in the left side and a PSNR of 23.414 dB in the right side with no obstacles. It also reduces blind spots, with an SSIM score of 0.901 and a PSNR score of 18.13 dB. The proposed tele-monitoring system is compatible with both wired and wireless communication, making it accessible in various situations. It uses camera and performs live data monitoring, and the two monitoring systems complement each other. The system also includes a comprehensive database and an analysis tool, allowing medical staff to collect and analyze data on ventilator use, providing them a quick, at-a-glance view of the patient's condition. With the implementation of this system, patient outcomes may be improved and the burden on medical professionals may be reduced during the COVID-19 pandemic-like situations.

Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review.

SARS-CoV-2 is a novel virus that has been affecting the global population by spreading rapidly and causing severe complications, which require prompt and elaborate emergency treatment. Automatic tools to diagnose COVID-19 could potentially be an important and useful aid. Radiologists and clinicians could potentially rely on interpretable AI technologies to address the diagnosis and monitoring of COVID-19 patients. This paper aims to provide a comprehensive analysis of the state-of-the-art deep learning techniques for COVID-19 classification. The previous studies are methodically evaluated, and a summary of the proposed convolutional neural network (CNN)-based classification approaches is presented. The reviewed papers have presented a variety of CNN models and architectures that were developed to provide an accurate and quick automatic tool to diagnose the COVID-19 virus based on presented CT scan or X-ray images. In this systematic review, we focused on the critical components of the deep learning approach, such as network architecture, model complexity, parameter optimization, explainability, and dataset/code availability. The literature search yielded a large number of studies over the past period of the virus spread, and we summarized their past efforts. State-of-the-art CNN architectures, with their strengths and weaknesses, are discussed with respect to diverse technical and clinical evaluation metrics to safely implement current AI studies in medical practice.

Telemedicine in the monitoring of covid-19 patients: general practitioners’ usage, facilitators and barriers in the local health authority roma 1

Telemedicine in the monitoring of covid-19 patients: general practitioners’ usage, facilitators and barriers in the local health authority roma 1

Post-COVID psychiatry functional unit of hospital del mar: Treatment and monitoring of COVID-19 patients who developed neuropsychiatric symptoms

IntroductionThere is growing evidence on the prevalence of neuropsychiatric disorders in patients suffering from COVID-19. On Hospital del Mar, a multidisciplinary post-COVID unit was created in May 2020 for COVID-19 patients.ObjectivesExplain the functioning of the Post-COVID Psychiatry Functional Unit and the sociodemographic and clinical characteristics of treated patients.MethodsThe Post-COVID Unit assessed all patients treated for COVID at Hospital del Mar. Patients referred by their primary care center for persistent post-COVID symptoms were also treated.During a telephone interview, they received the PHQ-4 questionnaire to identify psychiatric symptoms. If the score was => 3, the patient was referred to the Post-COVID Psychiatry Unit.Initial contact was established by a mental health nurse, who collected a clinical history and administered three scales: PHQ-9, GAD-7 and PCL-5. A descriptive sociodemographic and clinical data analysis of patients treated by the Post-COVID Psychiatry Unit is carried out.ResultsThe sample consists of 149 patients who have been treated for positive PHQ-4 from July 2020 to April 2021. The majority were women (71%) and averaged 50.2 years old (SD = 12.3). In terms of the severity of COVID-19 infection, 45.6% needed hospitalization.The mean score of the psychiatric symptom scales was 11.12 points on the PHQ-9 (SD=5.4) (1-4: minimal depression, 5-9: mild, 10-14: moderate, 15.19: moderately severe, 20-27: severe), 9.43 (SD=5.1) points in the GAD7 (cutoff point>=10) and 2.99 (SD=4) in the PCL5 (cutoff point>6).39.5% patients of the sample were visited by psychiatry/psychology, of these 33.6% met psychiatric diagnostic criteria, the most frequent being an Adjustment disorder (15.40%), followed by Major Depressive Disorder (8.7%), Anxiety Disorder (4.7%) and other diagnoses (4.7%).ConclusionsPatients treated in the Post-COVID Unit were mainly middle-aged women, the most common symptomatology being mild depressive symptoms.Disclosure of InterestNone Declared

PDRF-Net: a progressive dense residual fusion network for COVID-19 lung CT image segmentation.

The lungs of patients with COVID-19 exhibit distinctive lesion features in chest CT images. Fast and accurate segmentation of lesion sites from CT images of patients' lungs is significant for the diagnosis and monitoring of COVID-19 patients. To this end, we propose a progressive dense residual fusion network named PDRF-Net for COVID-19 lung CT segmentation. Dense skip connections are introduced to capture multi-level contextual information and compensate for the feature loss problem in network delivery. The efficient aggregated residual module is designed for the encoding-decoding structure, which combines a visual transformer and the residual block to enable the network to extract richer and minute-detail features from CT images. Furthermore, we introduce a bilateral channel pixel weighted module to progressively fuse the feature maps obtained from multiple branches. The proposed PDRF-Net obtains good segmentation results on two COVID-19 datasets. Its segmentation performance is superior to baseline by 11.6% and 11.1%, and outperforming other comparative mainstream methods. Thus, PDRF-Net serves as an easy-to-train, high-performance deep learning model that can realize effective segmentation of the COVID-19 lung CT images.

Effects of the COVID-19 pandemic on the activity of clinical laboratories in Spain, evolution in the 2019-2021 period.

To assess the impact of the COVID-19 pandemic on the activity of clinical laboratories in Spain. A descriptive, observational, retrospective, multicenter study. Between March and December 2020, there was a statistically significant decrease in the number of test requests (-17.7%, p=<0.001) and total tests performed (-18.3%, p<0.001) with respect to the same period in 2019. A decrease was observed in the number of requests from primary care (-37.4%) (p<0.001) and in the number of foecal occult blood (-45.8%); qualitative urine (-30.1%); PSA (-28.5%); TSH (-27.8%); total cholesterol (-27.2%) and HbA1c (-24.7%) tests performed, p<0.001. A significant increase was found in the number of requests from ICUs (76.6%, p<0.001) and number of IL-6 (+22,350.9), D-dimer (+617.2%), troponin (+46.8%) and arterial blood gas (+3.9%) tests carried out, p<0.001. During the first months of 2021, there were significant changes in the number of requests for qualitative urine (-8.7%, p<0.001), PSA (-6.3%, p=0.009), IL-6 (+66,269.2, p<0.001), D-dimer (+603.6%, p<0.001), troponin (+28.7%, p<0.001), arterial blood gas (+26,2%, p=0.014) and ferritin (+16.0%, p=0.002) tests performed. There were changes in the origin and number of test requested to clinical laboratories in Spain. The number of requests for the evaluation and monitoring ofCOVID-19 patients increased, whereas requests for thecontrol of non-COVID patients and for population screening decreased. Long-term analysis reveals that the volume of tests performed for the control of chronic diseases returned to normal over time, whereas the increase observed in the volume of tests performed for the management of COVID-19 patients is maintained.

Factors related to the behavior of seeking health services in community experiencing symptoms of covid-19

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Background: &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;Coronavirus is a group of viruses that can cause disease in animals or humans. Positive confirmed cases of Covid-19 in Cirebon Regency, West Java, during 1-28 August 2020 reached 193 people while the number of deaths due to Covid-19 in Cirebon City, West Java, again increased by 17 people died. The causes of Covid-19 death include patients receiving late treatment due to the less than optimal level of occupancy in the treatment area and monitoring of COVID-19 patients who are self-isolating. In addition, it is also caused by isoman patients with severe symptoms, patient overload, scarcity of drugs and oxygen, the health system is not ready, the health sub-system of community empowerment has failed. there are still many people who still consider being exposed to the corona virus as a disgrace, so they don't want to go to health facilities. People who are sick in many areas, are still seen as tainted, condemned, unkind people. does not want to be tested, does not want to report, because he has a social burden.&lt;/em&gt;&lt;/p&gt;&lt;pre&gt;&lt;strong&gt;&lt;em&gt;Purpose: This&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; study aims to determine the factors related to the behavior of seeking health services in the community if experiencing symptoms of Covid-19 in Cirebon.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/pre&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Methods&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;: This study used a cross sectional design. This research was conducted at Arsy Medika Clinic, Cirebon Regency and Akbid Muhammadiyah Clinic, Cirebon City on 18-25 September 2021 and obtained a sample of 87 respondents.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;pre&gt;&lt;strong&gt;&lt;em&gt;Results:&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; The results showed that most of the respondents were 20-29 years old, the gender of the respondents was female, the respondent's education was high school, the respondent's occupation was not working, married, received information from television, there was a relationship between age and education with health service seeking behavior, and there was no relationship between age and education. there is a relationship between gender, occupation, marital status, information media, and knowledge with health service seeking behavior.&lt;/em&gt;&lt;/pre&gt;&lt;pre&gt;&lt;strong&gt;&lt;em&gt;Conclusion:&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; This study shows that there is a relationship between age and education with health service seeking behavior, and there is no relationship between gender, occupation, marital status, information media, and knowledge with health service seeking behavior.&lt;/em&gt;&lt;/pre&gt;

A System for Monitoring of COVID-19 Patients at Home Based on Internet of Things and Fog Computing

The number of elderly persons, who generally have chronic illnesses and have seen a substantial spread of the novel coronavirus in recent years, has grown in recent years (COVID-19). The majority of older persons suffering from various chronic diseases have died as a result of infection with this virus. Thousands of people have died as a result of this. Coronavirus has also caused several problems in hospitals, and people are not being treated as a result of the large number of patients who require medical care. Medical and paramedic personnel have also been infected, and there is a risk that the virus will spread to the patient and his attendants via medical and paramedical workers. You may avoid this problem by staying at home, where you can monitor your whereabouts and warn in an emergency. The Internet of Things was critical in real-time monitoring of COVID-19-infected patients. Many cloud-based IoT applications are available; nevertheless, high latency, bandwidth, and energy consumption are significant concerns with cloud computing. Fog Computing attempts to address cloud computing issues, such as bandwidth, network latency, and energy consumption. Similarly, one of the primary fog computing applications is data handling generated by healthcare IoT devices. Healthcare IoT devices generate a large amount of data, which must be managed effectively with low latency, no failures, low energy consumption, low cost, and high accuracy.

Application for data collection and monitoring of COVID-19 patients in Sukorame Community Health Center

The significant increase in COVID-19 cases in Indonesia in May-July 2021 overwhelmed health workers. One of the efforts to monitor the spread of COViD-19 disease is collecting data on patients and proper monitoring. For example, the Sukorame Community Health Center, Mojoroto Kediri, does not yet have an application to record and monitor COVID-19 patients. Data collection is currently done manually by writing in books and excel. This study designed and built a data collection and monitoring application for COVID-19 patients to help Puskesmas staff obtain more accurate patient data and monitor the related patient data. This study implements the waterfall method, including system requirements, design, implementation, verification, and maintenance. The results of this study are the applications that can help and facilitate Community Health Center in collecting data on COVID-19 as a form of effort in overcoming and preventing the spread of COVID-19 in the work area of Sukorame Community Health Center, Kediri City. Based on the user satisfaction questionnaire results, 75% of users consisting of staff and heads of community health centers were helped by this application.

The role of Monocyte Distribution Width (MDW) in the prognosis and monitoring of COVID-19 patients

The new parameter derived from the standard deviation of the monocyte distribution width (MDW) has shown a good diagnostic efficacy in COVID-19 patients. In this study, we propose MDW as a prognostic and monitoring parameter in patients with severe forms of COVID-19. Sixty SARS-CoV-2-positive patients admitted to the San Donato Hospital in Arezzo were enrolled. A blood sample taken to measure the complete blood count was used for the determination of MDW using a UniCel DxH 900 instrument (Beckman Coulter). For each patient, a mean of 6 ± 2 measurements of MDW were taken. The difference between the last and first MDW results was reported as the ΔMDW variable. The ΔMDW and age were significantly correlated to the outcome. In non-survivors patients, the difference in the mean of the MDW between the first and other points was not significant, while in survivors, the first point was higher than the other points (p < 0.005), with the exception of the mean of the second point (p-value = NS). The ΔMDW area under the curve (AUC) was 0.84, and with a cut-off lower than 0.00 the sensitivity and specificity were 88% and 81%, respectively. The most important result of this study is the ΔMDW calculated on the basis of the difference between the first and third measurement, after approximately the 5–7th day of hospitalisation. A ΔMDW less than one was indicative of an unfavourable prognosis. The data reported suggest that MDW could be used to support monitoring and surveillance, alongside other tests such as procalcitonin, in critically ill patients in the ICU.

Predictive Value Alteration of Laboratory Routine and Ferritin/Transferrin Ratio in Monitoring of COVID-19 Patients.

Inflammatory processes activated by rapid viral replication of SARS-CoV-2 can play a key role in the pathogenesis of multiple organ damage and be responsible for the COVID-19 patients' dramatic outcomes and common abnormal laboratory findings. The aim of this study was to assess the correlation between various laboratory biomarkers, ferritin/transferrin ratio (FTR) and receiver operating characteristic (ROC) analysis in monitoring COVID-19 patients. This observational study was conducted in three groups: healthy participants, non COVID-19 patients with COVID-19-like clinical signs, and COVID-19 patients (severe and non-severe). Biochemical (CRP, ferritin, transferrin and albumin) and hematological (WBC, lymphocytes) parameters were assessed by automated methods. Moreover, FTR and NLR markers were calculated in the three groups mentioned. Statistical analyses were done using R (version 4.1.0). ROC curve was used to validate the predictive value of parameters. The COVID-19 positive group had significantly higher NEU, CRP, ferritin, FTR values, while it's WBC, absolute counts of lymphocytes and albumin were significantly lower compared to the non-COVID-19 patients (p < 0.001). Serum ferritin and FTR level of the severe group was significantly higher than that of the non-severe group (p = 0.006 and (p = 0.011, respectively). The strongest correlation in all subjects showed between lymphocytopenia and increased NEU (r = -0.99, p < 0.001). The AUC values of WBC (0.95), lymphocytes (0.89), NEU (0.88), and NLR (0.88) were higher than CRP (0.64) or Ferritin (0.81). We recommend using FTR, WBC, and NLR changes as simple, useful, and inexpensive indicators in early detection of COVID-19 patients.

COVID-19 Follow-App. Mobile App-Based Monitoring of COVID-19 Patients after Hospital Discharge: A Single-Center, Open-Label, Randomized Clinical Trial.

Introduction: In the midst of a pandemic, apps can be used to provide close follow-up, ensure that patients are monitored at home, avoid excessive pressure on medical facilities, prevent the movement of people (both patients and health professionals), and reduce the risk of infection. Objective: To adapt and validate the use of a smartphone application for outpatient follow-up of COVID-19 patients after hospital discharge. Methods: We conducted an open-label clinical trial at Hospital Universitari Vall d’Hebron in Barcelona, Spain. Patients were randomly assigned in a 1:1 ratio to be followed by the Farmalarm app or by their primary care center. The primary endpoint was the reduction in the need for in-person return visits. Results: From 31 March to 4 May 2020, 150 patients were enrolled in the study at hospital discharge: 74 patients were randomized to the experimental group, and 76 to the control group. All patients in the control group and all except for six in the experimental group completed the study. During hospitalization, before study inclusion, all but 4 (97.3%) had viral pneumonia, 91 (60.7%) required supplemental oxygen, and 16 (10.7%) required intensive care unit (ICU) admission. COVID-19–related return visits to the emergency department were significantly higher in the control group (7.9% vs. 0%; p = 0.028) in the per-protocol analysis. Telephone consultations with the emergency department were performed by 12 (15.8%) patients in the control group and 0 (0%) in the experimental group (p < 0.001). Satisfaction with outpatient monitoring was rated higher by the experimental group (5 vs. 4 points; p < 0.001). Conclusions: Following COVID-19 hospital discharge, home follow-up via a mobile app was effective in reducing in-person return visits without undermining patient satisfaction or perception of health, compared with standard follow-up.

Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning

BackgroundWe designed an algorithm to assess COVID-19 patients severity and dynamic intubation needs and predict their length of stay using the breathing frequency (BF) and oxygen saturation (SpO2) signals. MethodsWe recorded the BF and SpO2 signals for confirmed COVID-19 patients admitted to the ICU of a teaching hospital during both the first and subsequent outbreaks of the pandemic in France. An unsupervised machine-learning algorithm (the Gaussian mixture model) was applied to the patients' data for clustering. The algorithm's robustness was ensured by comparing its results against actual intubation rates. We predicted intubation rates using the algorithm every hour, thus conducting a severity evaluation. We designed a S24 severity score that represented the patient's severity over the previous 24 h; the validity of MS24, the maximum S24 score, was checked against rates of intubation risk and prolonged ICU stay. ResultsOur sample included 279 patients. . The unsupervised clustering had an accuracy rate of 87.8% for intubation recognition (AUC = 0.94, True Positive Rate 86.5%, true Negative Rate 90.9%). The S24 score of intubated patients was significantly higher than that of non-intubated patients at 48 h before intubation. The MS24 score allowed for the distinguishing between three severity levels with an increased risk of intubation: green (3.4%), orange (37%), and red (77%). A MS24 score over 40 was highly predictive of an ICU stay greater than 5 days at an accuracy rate of 81.0% (AUC = 0.87). ConclusionsOur algorithm uses simple signals and seems to efficiently visualize the patients' respiratory situations, meaning that it has the potential to assist staffs' in decision-making. Additionally, real-time computation is easy to implement.

Radiological manifestations of COVID-19 variants and their impact on patient management

Background: The COVID-19 pandemic has significantly impacted the field of radiology, leading to changes in the utilization and application of various imaging modalities. Initially, chest computed tomography (CT) was widely employed for screening and diagnosing COVID-19. However, the current recommendation is to use CT primarily for high-risk patients, individuals with severe disease, or in regions where polymerase chain reaction (PCR) testing is not widely accessible. Aim of Work: The aim of this research paper is to examine the evolving role of radiology, particularly chest radiography, in the management of COVID-19 patients, as well as to highlight the operational changes and technological advancements that have been implemented in the field of radiology during the pandemic. Methods: This research paper is a comprehensive review of the existing literature on the changing role of radiology in the COVID-19 pandemic. It synthesizes the available information on the utilization of various imaging modalities, such as chest radiography and CT, for the screening, diagnosis, and monitoring of COVID-19 patients. Additionally, it explores the operational changes and technological advancements that have been implemented in the field of radiology to address the challenges posed by the pandemic.

Symptom Analysis Using Fuzzy Logic for Detection and Monitoring of COVID-19 Patients

Recent developments regarding the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) opened new horizons of healthcare opportunities. Moreover, these technological advancements give strength to face upcoming healthcare challenges. One of such challenges is the advent of COVID-19, which has adverse effects beyond comprehension. Therefore, utilizing the basic functionalities of IoT, this work presents a real-time rule-based Fuzzy Logic classifier for COVID-19 Detection (FLCD). The proposed model deploys the IoT framework to collect real-time symptoms data from users to detect symptomatic and asymptomatic Covid-19 patients. Moreover, the proposed framework is also capable of monitoring the treatment response of infected people. FLCD constitutes three components: symptom data collection using wearable sensors, data fusion through Rule-Based Fuzzy Logic classifier, and cloud infrastructure to store data with a possible verdict (normal, mild, serious, or critical). After extracting the relevant features, experiments with a synthetic COVID-19 symptom dataset are conducted to ensure effective and accurate detection of COVID-19 cases. As a result, FLCD successfully acquired 95% accuracy, 94.73% precision, 93.35% recall, and showed a minimum error rate of 2.52%.

Diagnosis/Prognosis of COVID-19 Chest Images via Machine Learning and Hypersignal Processing: Challenges, opportunities, and applications

The novel coronavirus disease, COVID-19, has rapidly and abruptly changed the world as we knew it in 2020. It has become the most unprecedented challenge to analytic epidemiology (AE) in general and signal processing (SP) theories specifically. In this regard, medical imaging plays an important role for the management of COVID-19. SP and deep learning (DL) models can assist in the development of robust radiomics solutions for the diagnosis/prognosis, severity assessment, treatment response, and monitoring of COVID-19 patients.

Monocyte Distribution Width (MDW) as novel inflammatory marker with prognostic significance in COVID-19 patients

Monocyte Distribution Width (MDW), a new cytometric parameter correlating with cytomorphologic changes occurring upon massive monocyte activation, has recently emerged as promising early biomarker of sepsis. Similar to sepsis, monocyte/macrophage subsets are considered key mediators of the life-threatening hyper-inflammatory disorder characterizing severe COVID-19. In this study, we longitudinally analyzed MDW values in a cohort of 87 COVID-19 patients consecutively admitted to our hospital, showing significant correlations between MDW and common inflammatory markers, namely CRP (p < 0.001), fibrinogen (p < 0.001) and ferritin (p < 0.01). Moreover, high MDW values resulted to be prognostically associated with fatal outcome in COVID-19 patients (AUC = 0.76, 95% CI: 0.66–0.87, sensitivity 0.75, specificity 0.70, MDW threshold 26.4; RR = 4.91, 95% CI: 1.73–13.96; OR = 7.14, 95% CI: 2.06–24.71). This pilot study shows that MDW can be useful in the monitoring of COVID-19 patients, as this innovative hematologic biomarker is: (1) easy to obtain, (2) directly related to the activation state of a fundamental inflammatory cell subset (i.e. monocytes, pivotal in both cytokine storm and sepsis immunopathogenesis), (3) well correlated with clinical severity of COVID-19-associated inflammatory disorder, and, in turn, (4) endowed with relevant prognostic significance. Additional studies are needed to define further the clinical impact of MDW testing in the management of COVID-19 patients.