A predictive model for disease progression in non-severely ill patients with coronavirus disease 2019

Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review

BACKGROUND

SARS-CoV-2, the unique coronavirus that causes COVID-19, has wreaked damage around the globe, with victims displaying a wide range of difficulties that have encouraged medical professionals to look for innovative technical solutions and therapeutic approaches. Artificial intelligence-based methods have contributed a significant part in tackling complicated issues, and some institutions have been quick to embrace and tailor these solutions in response to the COVID-19 pandemic's obstacles. Here, in this review article, we have covered a few DL techniques for COVID-19 detection and diagnosis, as well as ML techniques for COVID-19 identification, severity classification, vaccine and drug development, mortality rate prediction, contact tracing, risk assessment, and public distancing. This review illustrates the overall impact of AI/ML tools on tackling and managing the outbreak.

PURPOSE

The focus of this research was to undertake a thorough evaluation of the literature on the part of Artificial Intelligence (AI) as a complete and efficient solution in the battle against the COVID-19 epidemic in the domains of detection and diagnostics of disease, mortality prediction and vaccine as well as drug development.

METHODS

A comprehensive exploration of PubMed, Web of Science, and Science Direct was conducted using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) regulations to find all possibly suitable papers conducted and made publicly available between December 1, 2019, and August 2023. COVID-19, along with AI-specific words, was used to create the query syntax.

RESULTS

During the period covered by the search strategy, 961 articles were published and released online. Out of these, a total of 135 papers were chosen for additional investigation. Mortality rate prediction, early detection and diagnosis, vaccine as well as drug development, and lastly, incorporation of AI for supervising and controlling the COVID-19 pandemic were the four main topics focused entirely on AI applications used to tackle the COVID-19 crisis. Out of 135, 60 research papers focused on the detection and diagnosis of the COVID-19 pandemic. Next, 19 of the 135 studies applied a machine-learning approach for mortality rate prediction. Another 22 research publications emphasized the vaccine as well as drug development. Finally, the remaining studies were concentrated on controlling the COVID-19 pandemic by applying AI AI-based approach to it.

CONCLUSION

We compiled papers from the available COVID-19 literature that used AI-based methodologies to impart insights into various COVID-19 topics in this comprehensive study. Our results suggest crucial characteristics, data types, and COVID-19 tools that can aid in medical and translational research facilitation.

Machine learning-based prediction models for home discharge in patients with COVID-19: Development and evaluation using electronic health records

OBJECTIVE

This study aimed to develop and validate predictive models using electronic health records (EHR) data to determine whether hospitalized COVID-19-positive patients would be admitted to alternative medical care or discharged home.

METHODS

We conducted a retrospective cohort study using deidentified data from the University of Florida Health Integrated Data Repository. The study included 1,578 adult patients (≥18 years) who tested positive for COVID-19 while hospitalized, comprising 960 (60.8%) female patients with a mean (SD) age of 51.86 (18.49) years and 618 (39.2%) male patients with a mean (SD) age of 54.35 (18.48) years. Machine learning (ML) model training involved cross-validation to assess their performance in predicting patient disposition.

RESULTS

We developed and validated six supervised ML-based prediction models (logistic regression, Gaussian Naïve Bayes, k-nearest neighbors, decision trees, random forest, and support vector machine classifier) to predict patient discharge status. The models were evaluated based on the area under the receiver operating characteristic curve (ROC-AUC), precision, accuracy, F1 score, and Brier score. The random forest classifier exhibited the highest performance, achieving an accuracy of 0.84 and an AUC of 0.72. Logistic regression (accuracy: 0.85, AUC: 0.71), k-nearest neighbor (accuracy: 0.84, AUC: 0.63), decision tree (accuracy: 0.84, AUC: 0.61), Gaussian Naïve Bayes (accuracy: 0.84, AUC: 0.66), and support vector machine classifier (accuracy: 0.84, AUC: 0.67) also demonstrated valuable predictive capabilities.

SIGNIFICANCE

This study's findings are crucial for efficiently allocating healthcare resources during pandemics like COVID-19. By harnessing ML techniques and EHR data, we can create predictive tools to identify patients at greater risk of severe symptoms based on their medical histories. The models developed here serve as a foundation for expanding the toolkit available to healthcare professionals and organizations. Additionally, explainable ML methods, such as Shapley Additive Explanations, aid in uncovering underlying data features that inform healthcare decision-making processes.

Identification of Multisystem Inflammatory Syndrome in Children Classes and Development of Hyperinflammation Score in Pediatric COVID-19

The aim of this study is to describe characteristics and hospital course of children admitted with COVID-19 to a tertiary care pediatric center in Southeastern United States, and to present the frequency of three classes of multisystem inflammatory syndrome in children (MIS-C) and develop pediatric COVID-19 associated hyperinflammation score (PcHIS). A retrospective cohort study of 68 children was performed. Critical illness was defined as any child requiring respiratory or cardiovascular support or renal replacement therapy. PcHIS was developed by using six variables: fever, hematological dysfunction, coagulopathy, hepatic injury, macrophage activation, and cytokinemia. Centers for Disease Control and Prevention criteria were used to identify MIS-C, and three classes of MIS-C were identified based on the findings of recently published latent class analysis (Class 1: MIS-C without Kawasaki like disease, Class 2: MIS-C with respiratory disease, and Class 3: MIS-C with Kawasaki like disease). The median age was 6.4 years. Fever, respiratory, and gastrointestinal were common presenting symptoms. MIS-C was present in 32 (47%), critical COVID-19 illness in 11 (16%), and 17 (25%) were admitted to the PICU. Children with critical illness were adolescents with elevated body mass index and premorbid conditions. PcHIS score of 3 had a sensitivity of 100% and a specificity of 77% for predicting critical COVID-19 illness. Among MIS-C patients, 15 (47%) were in Class 1, 8 (25%) were in Class 2, and 9 (28%) were in Class 3. We conclude that most children with COVID-19 have mild-to-moderate illness. Critical COVID-19 is mainly seen in obese adolescents with premorbid conditions. Three Classes of MIS-C are identifiable based on clinical features. Validation and clinical implication of inflammation score in pediatric COVID-19 need further investigation.

Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial

BACKGROUND

We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown.

OBJECTIVES

The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS).

METHODS

We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays.

RESULTS

Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location.

CONCLUSION

An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT04570488.

Predicting progression to severe COVID-19 using the PAINT score

Objectives

One of the major challenges in treating patients with coronavirus disease 2019 (COVID-19) is predicting the severity of disease. We aimed to develop a new score for predicting progression from mild/moderate to severe COVID-19.

Methods

A total of 239 hospitalized patients with COVID-19 from two medical centers in China between February 6 and April 6, 2020 were retrospectively included. The prognostic abilities of variables, including clinical data and laboratory findings from the electronic medical records of each hospital, were analysed using the Cox proportional hazards model and Kaplan–Meier methods. A prognostic score was developed to predict progression from mild/moderate to severe COVID-19.

Results

Among the 239 patients, 216 (90.38%) patients had mild/moderate disease, and 23 (9.62%) progressed to severe disease. After adjusting for multiple confounding factors, pulmonary disease, age > 75, IgM, CD16⁺/CD56⁺ NK cells and aspartate aminotransferase were independent predictors of progression to severe COVID-19. Based on these five factors, a new predictive score (the ‘PAINT score’) was established and showed a high predictive value (C-index = 0.91, 0.902 ± 0.021, p < 0.001). The PAINT score was validated using a nomogram, bootstrap analysis, calibration curves, decision curves and clinical impact curves, all of which confirmed its high predictive value.

Conclusions

The PAINT score for progression from mild/moderate to severe COVID-19 may be helpful in identifying patients at high risk of progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07466-4.

Validation of Machine Learning Models to Predict Adverse Outcomes in Patients with COVID-19: A Prospective Pilot Study

PURPOSE

We previously developed learning models for predicting the need for intensive care and oxygen among patients with coronavirus disease (COVID-19). Here, we aimed to prospectively validate the accuracy of these models.

MATERIALS AND METHODS

Probabilities of the need for intensive care [intensive care unit (ICU) score] and oxygen (oxygen score) were calculated from information provided by hospitalized COVID-19 patients (n=44) via a web-based application. The performance of baseline scores to predict 30-day outcomes was assessed.

RESULTS

Among 44 patients, 5 and 15 patients needed intensive care and oxygen, respectively. The area under the curve of ICU score and oxygen score to predict 30-day outcomes were 0.774 [95% confidence interval (CI): 0.614-0.934] and 0.728 (95% CI: 0.559-0.898), respectively. The ICU scores of patients needing intensive care increased daily by 0.71 points (95% CI: 0.20-1.22) after hospitalization and by 0.85 points (95% CI: 0.36-1.35) after symptom onset, which were significantly different from those in individuals not needing intensive care (p=0.002 and <0.001, respectively). Trends in daily oxygen scores overall were not markedly different; however, when the scores were evaluated within <7 days after symptom onset, the patients needing oxygen showed a higher daily increase in oxygen scores [1.81 (95% CI: 0.48-3.14) vs. -0.28 (95% CI: 1.00-0.43), p=0.007].

CONCLUSION

Our machine learning models showed good performance for predicting the outcomes of COVID-19 patients and could thus be useful for patient triage and monitoring.

Effect of the COVID-19 pandemic on stress, anxiety, and depressive disorders in German primary care: A cross-sectional study

Several studies revealed that mental disorders' prevalence increased during the COVID-19 pandemic, particularly in young and female individuals. Such studies represent individuals' subjective perceptions and not the number of mental health cases treated in primary care. Thus, this study aimed to describe the changes in depression, anxiety, and stress disorder diagnoses in General Practitioner (GP) practices during the COVID-19 pandemic. More than three million patients of 757 German GP practices were included in this cross-sectional analysis. Descriptive statistics were used to assess changes in the number of incident depression, anxiety disorders, and reaction to severe stress and adjustment disorders documented by GPs in 2020 compared to the average of the years 2017-2019. There was a tremendous decrease in mental health diagnoses during the first lockdown that was only slightly compensated later. Overall populations and the entire year 2020, there was no change in documented depression (0%) and stress disorders (1%), but anxiety disorders were more often documented (+19%), especially for the elderly population (>80 years; +24%). This population group also received more frequently new depression (+12%) and stress disorder diagnoses (23%). The younger population was diagnosed more frequently at the end of 2020, nine months after the first lockdown. Anxiety disorders but not depression and stress diagnoses were elevated, which is not in line with previously published studies. We speculate that the elderly population was affected most by the pandemic immediately after the first lockdown was announced. The younger population has probably become more and more affected the longer the pandemic lasts.

EULAR points to consider on pathophysiology and use of immunomodulatory therapies in COVID-19

OBJECTIVES

Severe systemic inflammation associated with some stages of COVID-19 and in fatal cases led therapeutic agents developed or used frequently in Rheumatology being at the vanguard of experimental therapeutics strategies. The aim of this project was to elaborate EULAR Points to consider (PtCs) on COVID-19 pathophysiology and immunomodulatory therapies.

METHODS

PtCs were developed in accordance with EULAR standard operating procedures for endorsed recommendations, led by an international multidisciplinary Task Force, including rheumatologists, translational immunologists, haematologists, paediatricians, patients and health professionals, based on a systemic literature review up to 15 December 2020. Overarching principles (OPs) and PtCs were formulated and consolidated by formal voting.

RESULTS

Two OPs and fourteen PtCs were developed. OPs highlight the heterogeneous clinical spectrum of SARS-CoV-2 infection and the need of a multifaceted approach to target the different pathophysiological mechanisms. PtCs 1-6 encompass the pathophysiology of SARS-CoV-2 including immune response, endothelial dysfunction and biomarkers. PtCs 7-14 focus on the management of SARS-CoV-2 infection with immunomodulators. There was evidence supporting the use of glucocorticoids, especially dexamethasone, in COVID-19 cases requiring oxygen therapy. No other immunomodulator demonstrated efficacy on mortality to date, with however inconsistent results for tocilizumab. Immunomodulatory therapy was not associated with higher infection rates.

CONCLUSIONS

Multifactorial pathophysiological mechanisms, including immune abnormalities, play a key role in COVID-19. The efficacy of glucocorticoids in cases requiring oxygen therapy suggests that immunomodulatory treatment might be effective in COVID-19 subsets. Involvement of rheumatologists, as systemic inflammatory diseases experts, should continue in ongoing clinical trials delineating optimal immunomodulatory therapy utilisation in COVID-19.

Artificial intelligence-based approaches for COVID-19 patient management

During the highly infectious pandemic of coronavirus disease 2019 (COVID-19), artificial intelligence (AI) has provided support in addressing challenges and accelerating achievements in controlling this public health crisis. It has been applied in fields varying from outbreak forecasting to patient management and drug/vaccine development. In this paper, we specifically review the current status of AI-based approaches for patient management. Limitations and challenges still exist, and further needs are highlighted.

Glycemic status affects the severity of coronavirus disease 2019 in patients with diabetes mellitus: an observational study of CT radiological manifestations using an artificial intelligence algorithm

AIMS

Increasing evidence suggests that poor glycemic control in diabetic individuals is associated with poor coronavirus disease 2019 (COVID-19) pneumonia outcomes and influences chest computed tomography (CT) manifestations. This study aimed to explore the impact of diabetes mellitus (DM) and glycemic control on chest CT manifestations, acquired using an artificial intelligence (AI)-based quantitative evaluation system, and COVID-19 disease severity and to investigate the association between CT lesions and clinical outcome.

METHODS

A total of 126 patients with COVID-19 were enrolled in this retrospective study. According to their clinical history of DM and glycosylated hemoglobin (HbA1c) level, the patients were divided into 3 groups: the non-DM group (Group 1); the well-controlled blood glucose (BG) group, with HbA1c < 7% (Group 2); and the poorly controlled BG group, with HbA1c ≥ 7% (Group 3). The chest CT images were analyzed with an AI-based quantitative evaluation system. Three main quantitative CT features representing the percentage of total lung lesion volume (PLV), percentage of ground-glass opacity volume (PGV) and percentage of consolidation volume (PCV) in bilateral lung fields were used to evaluate the severity of pneumonia lesions.

RESULTS

Patients in Group 3 had the highest percentage of severe or critical illness, with 12 (32%) cases, followed by 6 (11%) and 7 (23%) cases in Groups 1 and 2, respectively (p = 0.042). The composite endpoints, including death or using mechanical ventilation or admission to the intensive care unit (ICU), were 3 (5%), 5 (16%) and 10 (26%) in Groups 1, 2 and 3, respectively (p = 0.013). The PLV, PGV and PCV in bilateral lung fields were significantly different among the three groups (all p < 0.001): the median PLVs were 12.5% (Group 3), 3.8% (Group 2) and 2.4% (Group 1); the median PGVs were 10.2% (Group 3), 3.6% (Group 2) and 1.9% (Group 1); and the median PCVs were 1.8% (Group 3), 0.3% (Group 2) and 0.1% (Group 1). In the linear regression analyses, which were adjusted for age, sex, BMI, and comorbidities, HbA1c remained positively associated with PLV (β = 0.401, p < 0.001), PGV (β = 0.364, p = 0.001) and PCV (β = 0.472, p < 0.001); this relationship was also observed between fasting blood glucose (FBG) and the three CT quantitative parameters. In the logistic regression analyses, PLV [OR 1.067 (1.032, 1.103)], PGV [OR 1.076 (1.034, 1.120)] and PCV [OR 1.280 (1.110, 1.476)] levels were independent predictors of the composite endpoints, as well as the areas under the ROC (AUCs) for PLV [AUC 0.796 (0.691, 0.900)], PGV [AUC 0.783 (0.678, 0.889)] and PCV [AUC 0.816 (0.722, 0.911)]; the ORs were still significant for CT lesions after adjusting for age, sex and poorly controlled diabetes.

CONCLUSIONS

Increased blood glucose level was correlated with the severity of lung involvement, as evidenced by certain chest CT parameters, and clinical prognosis in diabetic COVID-19 patients. There was a positive correlation between blood glucose level (both HbA1c and FBG) on admission and lung lesions. Moreover, the CT lesion severity by AI quantitative analysis was correlated with clinical outcomes.

Development and Validation of CoV19-OM Intensive Care Unit Score: An Early ICU Identification for Laboratory-Confirmed SARS-CoV-2 Patients from a Retrospective Cohort Study in Oman

Objective

To develop and validate a clinical score that will identify potential admittance to an intensive care unit (ICU) for a coronavirus disease 2019 (COVID-19) case.

Materials and methods

The clinical scoring system was developed using a least absolute shrinkage and selection operator logistic regression. The prediction algorithm was constructed and cross-validated using a development cohort of 313 COVID-19 patients, and was validated using an independent retrospective set of 64 COVID-19 patients.

Results

The majority of patients were Omani in nationality (n = 181, 58%). Multivariate logistic regression identified eight independent predictors of ICU admission that were included in the clinical score: hospitalization (OR, 1.079; 95% CI, 1.058–1.100), absolute lymphocyte count (OR, 0.526; 95% CI, 0.379–0.729), C-reactive protein (OR, 1.009; 95% CI, 1.006–1.011), lactate dehydrogenase (OR, 1.0008; 95% CI, 1.0004–1.0012), CURB-65 score (OR, 2.666; 95% CI, 2.212–3.213), chronic kidney disease with an estimated glomerular filtration rate of less than 70 (OR, 0.249; 95% CI, 0.155–0.402), shortness of breath (OR, 3.494; 95% CI, 2.528–6.168), and bilateral infiltrates in chest radiography (OR, 6.335; 95% CI, 3.427–11.713). The mean area under a curve (AUC) for the development cohort was 0.86 (95% CI, 0.85–0.87), and for the validation cohort, 0.85 (95% CI, 0.82–0.88).

Conclusion

This study presents a web application for identifying potential admittance to an ICU for a COVID-19 case, according to a clinical risk score based on eight significant characteristics of the patient (http://3.14.27.202/cov19-icu-score/).

Effect of the COVID-19 lockdown on disease recognition and utilisation of healthcare services in the older population in Germany: a cross-sectional study

BACKGROUND

There is little evidence about the utilisation of healthcare services and disease recognition in the older population, which was urged to self-isolate during the COVID-19 lockdown.

OBJECTIVES

We aimed to describe the utilisation of physician consultations, specialist referrals, hospital admissions and the recognition of incident diseases in Germany for this age group during the COVID-19 lockdown.

DESIGN

Cross-sectional observational study.

SETTING

1,095 general practitioners (GPs) and 960 specialist practices in Germany.

SUBJECTS

2.45 million older patients aged 65 or older.

METHODS

The number of documented physician consultations, specialist referrals, hospital admissions and incident diagnoses during the imposed lockdown in 2020 was descriptively analysed and compared to 2019.

RESULTS

Physician consultations decrease slightly in February (-2%), increase before the imposed lockdown in March (+9%) and decline in April (-18%) and May (-14%) 2020 compared to the same periods in 2019. Volumes of hospital admissions decrease earlier and more intensely than physician consultations (-39 versus -6%, respectively). Overall, 15, 16 and 18% fewer incident diagnoses were documented by GPs, neurologists and diabetologists, respectively, in 2020. Diabetes, dementia, depression, cancer and stroke were diagnosed less frequently during the lockdown (-17 to -26%), meaning that the decrease in the recognition of diseases was greater than the decrease in physician consultations.

CONCLUSION

The data suggest that organisational changes were adopted quickly by practice management but also raise concerns about the maintenance of routine care. Prospective studies should evaluate the long-term effects of lockdowns on patient-related outcomes.

Pathophysiology of acute respiratory syndrome coronavirus 2 infection: a systematic literature review to inform EULAR points to consider

BACKGROUND

The SARS-CoV-2 pandemic is a global health problem. Beside the specific pathogenic effect of SARS-CoV-2, incompletely understood deleterious and aberrant host immune responses play critical roles in severe disease. Our objective was to summarise the available information on the pathophysiology of COVID-19.

METHODS

Two reviewers independently identified eligible studies according to the following PICO framework: P (population): patients with SARS-CoV-2 infection; I (intervention): any intervention/no intervention; C (comparator): any comparator; O (outcome) any clinical or serological outcome including but not limited to immune cell phenotype and function and serum cytokine concentration.

RESULTS

Of the 55 496 records yielded, 84 articles were eligible for inclusion according to question-specific research criteria. Proinflammatory cytokine expression, including interleukin-6 (IL-6), was increased, especially in severe COVID-19, although not as high as other states with severe systemic inflammation. The myeloid and lymphoid compartments were differentially affected by SARS-CoV-2 infection depending on disease phenotype. Failure to maintain high interferon (IFN) levels was characteristic of severe forms of COVID-19 and could be related to loss-of-function mutations in the IFN pathway and/or the presence of anti-IFN antibodies. Antibody response to SARS-CoV-2 infection showed a high variability across individuals and disease spectrum. Multiparametric algorithms showed variable diagnostic performances in predicting survival, hospitalisation, disease progression or severity, and mortality.

CONCLUSIONS

SARS-CoV-2 infection affects both humoral and cellular immunity depending on both disease severity and individual parameters. This systematic literature review informed the EULAR 'points to consider' on COVID-19 pathophysiology and immunomodulatory therapies.

Role of Machine Learning Techniques to Tackle the COVID-19 Crisis: Systematic Review

BACKGROUND

SARS-CoV-2, the novel coronavirus responsible for COVID-19, has caused havoc worldwide, with patients presenting a spectrum of complications that have pushed health care experts to explore new technological solutions and treatment plans. Artificial Intelligence (AI)-based technologies have played a substantial role in solving complex problems, and several organizations have been swift to adopt and customize these technologies in response to the challenges posed by the COVID-19 pandemic.

OBJECTIVE

The objective of this study was to conduct a systematic review of the literature on the role of AI as a comprehensive and decisive technology to fight the COVID-19 crisis in the fields of epidemiology, diagnosis, and disease progression.

METHODS

A systematic search of PubMed, Web of Science, and CINAHL databases was performed according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines to identify all potentially relevant studies published and made available online between December 1, 2019, and June 27, 2020. The search syntax was built using keywords specific to COVID-19 and AI.

RESULTS

The search strategy resulted in 419 articles published and made available online during the aforementioned period. Of these, 130 publications were selected for further analyses. These publications were classified into 3 themes based on AI applications employed to combat the COVID-19 crisis: Computational Epidemiology, Early Detection and Diagnosis, and Disease Progression. Of the 130 studies, 71 (54.6%) focused on predicting the COVID-19 outbreak, the impact of containment policies, and potential drug discoveries, which were classified under the Computational Epidemiology theme. Next, 40 of 130 (30.8%) studies that applied AI techniques to detect COVID-19 by using patients' radiological images or laboratory test results were classified under the Early Detection and Diagnosis theme. Finally, 19 of the 130 studies (14.6%) that focused on predicting disease progression, outcomes (ie, recovery and mortality), length of hospital stay, and number of days spent in the intensive care unit for patients with COVID-19 were classified under the Disease Progression theme.

CONCLUSIONS

In this systematic review, we assembled studies in the current COVID-19 literature that utilized AI-based methods to provide insights into different COVID-19 themes. Our findings highlight important variables, data types, and available COVID-19 resources that can assist in facilitating clinical and translational research.

The situation of elderly with cognitive impairment living at home during lockdown in the Corona-pandemic in Germany

BACKGROUND

The outbreak of the Corona virus is a challenge for health care systems worldwide. The aim of this study is to analyze a) knowledge about, and feelings related to the Corona-pandemic. Describe b) loneliness, depression and anxiety and, c) the perceived, immediate impact of the lockdown on frequency of social contacts and quality of health care provision of people with cognitive impairment during social distancing and lockdown in the primary care system and living at home in Germany.

METHODS

This analysis is based on data of a telephone-based assessment in a convenience sample of n = 141 people with known cognitive impairment in the primary care setting. Data on e.g. cognitive and psychological status prior to the pandemic was available. Attitudes, knowledge about and perceived personal impact of the pandemic, social support, loneliness, anxiety, depression, change in the frequency of social activities due to the pandemic and perceived impact of the pandemic on health care related services were assessed during the time of lockdown.

RESULTS

The vast majority of participants are sufficiently informed about Corona (85%) and most think that the measures taken are appropriate (64%). A total of 11% shows one main symptom of a depression according to DSM-5. The frequency of depressive symptoms has not increased between the time before pandemic and lockdown in almost all participants. The sample shows minimal (65.0%) or low symptoms of anxiety (25%). The prevalence of loneliness is 10%. On average seven activities have decreased in frequency due to the pandemic. Social activities related to meeting people, dancing or visiting birthdays have decreased significantly. Talking with friends by phone and activities like gardening have increased. Utilization of health care services like day clinics, relief services and prescribed therapies have been reported to have worsened due to the pandemic. Visits to general practitioners decreased.

CONCLUSIONS

The study shows a small impact of the pandemic on psychological variables like depression, anxiety and loneliness in the short-term in Germany. There is a decrease in social activities as expected. The impact on health care provision is prominent. There is a need for qualitative, in-depth studies to further interpret the results.

A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients

The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4-88.7] and 90.8% [90.8-90.8]) and discrimination (95.1% [95.1-95.2] and 86.8% [86.8-86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows.