Classification Of Disease Severity Research Articles

Radiographic quantification of left atrial size in dogs with myxomatous mitral valve disease.

BackgroundIn the absence of echocardiography, identification of cardiomegaly via thoracic radiography is a necessary criterion for classification of disease severity in dogs with myxomatous mitral valve disease (MMVD).ObjectiveModified‐vertebral left atrial size (M‐VLAS) facilitates objective radiographic assessment of the left atrium (LA) in 2 dimensions and identifies LA enlargement more accurately than existing methods.AnimalsSixty‐four dogs with various stages of MMVD and 6 control healthy dogs.MethodsRetrospective case–control study. Medical records were searched for dogs with varying severity of MMVD. Modified‐vertebral left atrial size, vertebral left atrial size (VLAS), vertebral heart size (VHS), and radiographic left atrial dimension (RLAD) were measured from thoracic radiographs and compared with echocardiographically derived measurements.ResultsPositive correlation to LA/Ao was identified for M‐VLAS (r = 0.77, P < .001), VLAS (r = 0.76, P < .001), RLAD (r = 0.75, P < .001), and VHS (r = 0.67, P < .001). Receiver operating characteristic analyzes provided an area under the curve of 0.97 (95% CI, 0.94‐1.00) for M‐VLAS, which was superior to VHS (0.90, 95% CI, 0.94‐1.00, P = .03) in identifying dogs with LA/Ao ≥1.6. A cut‐off value of ≥3.4 vertebrae using M‐VLAS provided 92.7% sensitivity and 93.1% specificity in predicting LA enlargement.Conclusions and clinical importanceM‐VLAS, which is superior to VHS, offers an accurate and repeatable way to radiographically identify LA enlargement in dogs with MMVD.

Quantifying Disease Severity Of Cystic Fibrosis Using Quantile Regression Methods

This article presents a classification of disease severity for patients with cystic fibrosis (CF). CF is a genetic disease that dramatically decreases life expectancy and quality. The disease is characterized by polymicrobial infections which lead to lung remodeling and airway mucus plugging. In order to quantify disease severity of CF patients and compute a continuous severity index measure, quantile regression, rank scores, and corresponding normalized ranks are calculated for CF patients. Based on the rank scores calculated from the set of quantile regression models, a continuous severity index is computed for each CF patient and can be considered a robust estimate of CF disease severity.

COVID-19 infection in patients with connective tissue disease: A multicity study in Hubei province, China.

Novel Coronavirus disease 2019 (COVID‐19) has spread rapidly around the world. Individuals with immune dysregulation and/or on immunosuppressive therapy, such as rheumatic patients, are considered at greater risk for infections. However, the risks of patients with each subcategory of rheumatic diseases have not been reported. Here, we identified 100 rheumatic patients from 18,786 COVID‐19 patients hospitalized in 23 centers affiliated to Hubei COVID‐19 Rheumatology Alliance between January 1 and April 1, 2020. Demographic information, medical history, length of hospital stay, classification of disease severity, symptoms and signs, laboratory tests, disease outcome, computed tomography, and treatments information were collected. Compared to gout and ankylosing spondylitis (AS) patients, patients with connective tissue disease (CTD) tend to be more severe after COVID‐19 infection (p = 0.081). CTD patients also had lower lymphocyte counts, hemoglobin, and platelet counts (p values were 0.033, < 0.001, and 0.071, respectively). Hydroxychloroquine therapy and low‐ to medium‐dose glucocorticoids before COVID‐19 diagnosis reduced the progression of COVID‐19 to severe/critical conditions (p = 0.001 for hydroxychloroquine; p = 0.006 for glucocorticoids). Our data suggests that COVID‐19 in CTD patients may be more severe compared to patients with AS or gout.

Evaluation of lung densitometric and volumetric changes in silicosis patients using three-dimensional software for multidetector CT and the relationship with profusion scores

To evaluate the density and volume changes in the lungs of silicosis patients and their relationship with the disease severity classification of the International Labor Organization (ILO). The multidetector computed tomography (CT) images of 44 patients diagnosed with silicosis and 32 controls that underwent thoracic CT due to trauma were evaluated. Patients with silicosis were divided into three categories according to the ILO classification. Data related to the total lung volume, total lung mean density, lung opacity score, percentage of lung high opacity, and mean density in the lower and upper lobes were obtained using three-dimensional (3D) software. There was no significant difference between the total lung mean densities of the silicosis and control groups (p=0.213); however, a significant difference was observed between the two groups in terms of the total lung volume (p<0.0001). According to the ILO classification, there was a significant difference between the disease severity categories in relation to the percentage of lung high opacity (p=0.000005). A strong correlation was detected between disease severity and high opacity percentage (p<0.0001, r=0.804). According to the ILO classification, there was also a significant difference between disease severity categories in terms of the lung opacity score (p=0.000144), as well as a moderate correlation between disease severity and opacity score (p<0.0001, r=0.580). Total lung volume is a CT finding that shows variation in exposure to crystalline silica. The percentage of high opacity determined using multidetector CT is an effective parameter in evaluating disease severity.

Proposition of a severity scale for lichen sclerosus: The "Clinical Lichen Sclerosus Score".

Vulvar lichen sclerosus (VLS) is a chronic inflammatory skin disease of the anogenitalarea leading to itch, burning, sexual dysfunction and impaired quality of life. An unmet need in the context of LS is a practical, easily assessable grading scale to classify disease severity and to allow intra- and interindividual comparisons. The objectives of this study were i) to assess the prevalence and severity of 23 items proposed by a recent Delphi consensus group in patients with adult VLS. ii) to develop a clinical severity scale and, iii) to test the interrater reliability of this novel severity scale. A retrospective assessment of the prevalence and severity of 23 items in 143 patients was performed by using patient records and photo documentation to develop a novel clinical severity scale (i.e. the "Clinical Lichen Sclerosus Score" = CLISSCO) for VLS. Thereafter, the CLISSCO was validated by 16 raters. We found that the items proposed by the consensus group vary markedly in frequency and severity. Following selection of the most relevant items, the CLISSCO was developed consisting of 3 "Symptoms", 3 "Signs" and 6 "Architectural changes" rated on a 0-4 point Likert-scale. The intraclass correlation coefficient was excellent for each item, the applicability of the CLISSCO considered user-friendly by the raters. We conclude that the CLISSCO proved to be a user-friendly, reliable tool to assess disease severity in VLS. However, further studies are needed to validate its applicability and value in daily practice and clinical research.

Predictive value of the neutrophil to lymphocyte ratio for disease deterioration and serious adverse outcomes in patients with COVID-19: a prospective cohort study

BackgroundEarly identification of patients who are at high risk of poor clinical outcomes is of great importance in saving the lives of patients with novel coronavirus disease 2019 (COVID-19) in the context of limited medical resources.ObjectiveTo evaluate the value of the neutrophil to lymphocyte ratio (NLR), calculated at hospital admission and in isolation, for the prediction of the subsequent presence of disease progression and serious clinical outcomes (e.g., shock, death).MethodsWe designed a prospective cohort study of 352 hospitalized patients with COVID-19 between January 9 and February 26, 2020, in Yichang City, Hubei Province. Patients with an NLR equal to or higher than the cutoff value derived from the receiver operating characteristic curve method were classified as the exposed group. The primary outcome was disease deterioration, defined as an increase of the clinical disease severity classification during hospitalization (e.g., moderate to severe/critical; severe to critical). The secondary outcomes were shock and death during the treatment.ResultsDuring the follow-up period, 51 (14.5%) patients’ conditions deteriorated, 15 patients (4.3%) had complicated septic shock, and 15 patients (4.3%) died. The NLR was higher in patients with deterioration than in those without deterioration (median: 5.33 vs. 2.14, P < 0.001), and higher in patients with serious clinical outcomes than in those without serious clinical outcomes (shock vs. no shock: 6.19 vs. 2.25, P < 0.001; death vs. survival: 7.19 vs. 2.25, P < 0.001). The NLR measured at hospital admission had high value in predicting subsequent disease deterioration, shock and death (all the areas under the curve > 0.80). The sensitivity of an NLR ≥ 2.6937 for predicting subsequent disease deterioration, shock and death was 82.0% (95% confidence interval, 69.0 to 91.0), 93.3% (68.0 to 100), and 92.9% (66.0 to 100), and the corresponding negative predictive values were 95.7% (93.0 to 99.2), 99.5% (98.6 to 100) and 99.5% (98.6 to 100), respectively.ConclusionsThe NLR measured at admission and in isolation can be used to effectively predict the subsequent presence of disease deterioration and serious clinical outcomes in patients with COVID-19.

Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs

Rationale and ObjectivesRadiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of disease severity is inconsistent on chest radiographs (CXRs). We aimed to see if an artificial intelligence (AI) system could help improve radiologist interrater agreement.Materials and MethodsWe performed a retrospective multi-radiologist user study to evaluate the impact of an AI system, the PXS score model, on the grading of categorical COVID-19 lung disease severity on 154 chest radiographs into four ordinal grades (normal/minimal, mild, moderate, and severe). Four radiologists (two thoracic and two emergency radiologists) independently interpreted 154 CXRs from 154 unique patients with COVID-19 hospitalized at a large academic center, before and after using the AI system (median washout time interval was 16 days). Three different thoracic radiologists assessed the same 154 CXRs using an updated version of the AI system trained on more imaging data. Radiologist interrater agreement was evaluated using Cohen and Fleiss kappa where appropriate. The lung disease severity categories were associated with clinical outcomes using a previously published outcomes dataset using Fisher's exact test and Chi-square test for trend.ResultsUse of the AI system improved radiologist interrater agreement (Fleiss κ = 0.40 to 0.66, before and after use of the system). The Fleiss κ for three radiologists using the updated AI system was 0.74. Severity categories were significantly associated with subsequent intubation or death within 3 days.ConclusionAn AI system used at the time of CXR study interpretation can improve the interrater agreement of radiologists.

SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection

SummaryEarly and accurate assessment of plant disease severity is key to preventing disease attack. Traditional detection methods rely on manual vision to distinguish between types of disease infection, but this is time consuming, laborious and inaccurate. To address this problem, this paper proposes a deep learning‐based attentional network model (SEV‐Net) for plant disease severity identification and classification. The network embeds the improved channel and spatial attention module into the residual block of ResNet. The proposed attention module reduces the redundancy of information between channels and focuses on the most information‐rich regions of the feature map. In this experiment, SEV‐Net achieved an accuracy of 97.59% and 95.37% for multiple and single plant (Tomato) disease severity classification, which was better than existing attentional networks (SE‐Net and CBAM). Moreover, the combination of visualization techniques showed that SEV‐Net was adept at distinguishing small variations between plant diseases, proving the feasibility and effectiveness of the network. Furthermore, we have also designed and developed an Android application for real‐time classification of plant disease severity. The system deploys the SEV‐Net network model, which has higher classification accuracy and faster recognition speed.

COVID-19 among Minority Children in Detroit, Michigan during the Early National Surge of the Pandemic.

Background. The COVID-19 pandemic has shed light on communities of racial/ethnic minority groups in the US where long-standing health issues and structural inequities are now known to have resulted in increased risk for infection, severe illness, and death from the virus. The objective of our study was to describe demographic characteristics, clinical presentations, medical interventions and outcomes of pediatric patients with COVID-19 treated at Children’s Hospital of Michigan (CHM), a tertiary care center in urban Detroit, an early hotspot during the initial surge of the SARS-CoV-2 pandemic. Methods. A retrospective chart review was performed of children ≤18 years of age who had polymerase chain reaction (RT-PCR) testing via NP swab or serum IgG antibody testing for SARS-CoV-2 during March 1, 2020–June 30, 2020. Results. Seventy-eight COVID-19 infected children were identified of whom 85.8% (67/78) were from minority populations (African American, Hispanic). Hospitalization rate was 82% (64/78). About 44% (34/78) had an associated comorbidity with asthma and obesity being most common. Although all ages were affected, infants <1 year of age had the highest hospitalization rate (19/64, 30%). In all disease severity categories, dichotomized non-whites had more severe disease by percentage within race/ethnicity than Whites, and also within percent disease severity (P-value = .197). Overall, 37% of hospitalized patients required intensive care. Conclusions. Extremely high rates of COVID-19 hospitalization and requirement of ICU care were identified in our patient population. Further studies are needed to better understand the contributing factors to this health disparity in disadvantaged communities.

Lymphopenia: A useful predictor of COVID-19 disease severity and mortality.

Objective:To detect association of lymphopenia with disease severity and mortality.Methods:Total 874 COVID RT-PCR positive patients admitted to KRL Hospital Islamabad from April 2020 to August 2020 were included in this cross-sectional study. Lymphopenia was defined as <1100 cells/micro–L. WHO categories for disease severity were used. Demographic profile, absolute lymphocyte counts and co-morbidities were recorded. Pearson’s Chi Square test was used to see association between lymphopenia and disease severity as well as disease outcome. Regression analysis was used to see whether lymphopenia would predict disease severity. Comparison of means of absolute lymphocyte count in different disease categories was done by ANOVA. Tukey’s test range was then used to find the means different from each other. P-value ≤ 0.05 was considered statistically significantResults:The mean age of patients was 40 ± 12.3 years. Majority patients (73.9%) were asymptomatic. Lymphopenia was present in 6.9% of total patients. Significant association was found between lymphopenia and disease severity as well as lymphopenia and mortality (< 0.001). Lymphopenia was found to be a predictor of disease severity using regression analysis (< 0.001). Comparison of mean absolute lymphocyte count was significant among disease severity categories (< 0.001). On post-hoc analysis, difference in absolute lymphocyte count was significant moving from asymptomatic to mild and then moderate disease category. However no significant difference was seen in absolute lymphocyte count between moderate and severe categories.Conclusion:Results are compatible with worldwide studies and lymphopenia is valid as a marker of disease severity and mortality.

Neutrophil to lymphocyte ratio (NLR) in COVID-19: A cheap prognostic marker in a resource constraint setting.

Objectives:To identify association of neutrophil to lymphocyte ratio with disease severity and mortality.Methods:Total 720 Corona Virus RT-PCR positive patients were included in this cross-sectional study. Patients were admitted to KRL Hospital Islamabad from April 2020 to August 2020. Neutrophil to lymphocyte ratio (NLR) was recorded on admission and then serially. NLR cut-off was 3.0. WHO categories for disease severity (asymptomatic, mild, moderate and severe) were used. Demographic profile, symptoms and co-morbidities were recorded.Results:The mean age of patients was 40 ± 12.4 years with 96% being males. Majority patients (76.5%) were asymptomatic. Amongst symptoms, fever was the most common symptom. Diabetes mellitus was most common recorded co-morbidity. The mean NLR 2.5 ± 2.78. Significant association was found between NLR and disease severity as well as mortality. Difference in mean NLR amongst disease severity categories was also significantConclusion:Results are compatible with worldwide studies and NLR is a cheap and easily available marker of disease severity and mortality.

Analysis of Tertiary Hospital Utilization in Pediatric Orthopaedics: a Study Using Nationwide Sample Data from Korea.

BackgroundIn the Korean medical system, the severity classification for a specific disease depends primarily on its nationwide admission rate in tertiary hospitals. Inversely, one of the important designation criteria for a tertiary hospital is the hospital's treatment ratio of patients classified as having a specific severe disease. Most diseases requiring pediatric orthopaedic surgery (POS) are not currently classified as high severity in terms of disease severity. We investigated the admission rates for the representative POS diseases in tertiary hospitals and compared these rates with those for adult orthopaedic surgery (AOS) diseases.MethodsSeven POS diagnoses and three AOS diagnoses were selected based on frequency of admission. Nationwide sample data were used to investigate the admission rates for these representative diagnoses from 2008 to 2017.ResultsSix of the seven frequent POS diagnoses presented high admission rates in tertiary hospitals (62.5–92.3%). In contrast, all frequent AOS diagnoses presented low admission rates in tertiary hospitals.ConclusionThe admission rates of frequent POS diagnoses in tertiary hospitals are high. Considering that these rates are the most important factors for the classification of disease severity, POS diseases seem to be underestimated in terms of severity. This may lead to a tendency for tertiary hospitals to intentionally reduce the admission of children with POS diseases. As a result, these children may not receive appropriate professional care. Therefore, for the disease severity, POS diseases should be classified differently from general AOS diseases by using different criteria reflecting the patient's age.

Does Stem Cell Implantation Have an Effect on Severity of Retinitis Pigmentosa: Evaluation with a Classification System?

Background: Cell replacement therapies have been evaluated in recent years as an alternative for various retinal pathologies to evaluate the therapeutic efficacy of cell therapy, it is important to measure the severity of the disease. The aim of this study was to evaluate the effect of umbilical cord derived Mesenchymal Stem Cell (UC-MSC) implantation on severity of Retinitis Pigmentosa (RP). Methods: This single-center, clinical study included data of 138 eyes of 92 patients who had a confirmed diagnosis of RP and received stem cell implantation to the suprachoroidal area with a surgical procedure. Patients were evaluated before and 1 year after the surgery regarding to the outcome measures of Best Corrected Visual Acuity (BCVA), Optical Coherence Tomography (OCT) and Visual Field (VF) tests. BCVA, VF width and ellipsoid zone (EZ) width on OCT were recorded for each patient and a scoring criterion was established for each variable varying from 0 to 5 depending on its distribution. The cumulative score (from 0 to 15) was used to classify disease severity from grade 0 to 5. Results: All of the patients completed 12-month follow-up period. The median age of the patients was 40.8 years, 46% were female, 77% had been diagnosed within 10 years and 41% had a family history. 79% of the patients with family history had autosomal recessive inheritance pattern. There were statistically significant improvements in the mean BCVA and VF scores during the study (p < 0.05). The mean score and the mean grade of the disease also improved after the treatment (p < 0.05). There was a negative correlation between BCVA improvement and scoring and grading of the disease. Conclusions: This study demonstrated beneficial effect of suprachoroidally applied UC-MSCs on BCVA, VF and the severity score and grade of the disease during 12-month follow-up period. Cell mediated therapy based on the secretion of Growth Factors (GFs) seems to be an effective and safe option for the treatment of degenerative retinal diseases. This classification is simple, produces objective measure of disease severity and gives opportunity to compare the results of different treatment modalities.

Diagnosis and Management of Intraabdominal Infection: Guidelines by the Chinese Society of Surgical Infection and Intensive Care and the Chinese College of Gastrointestinal Fistula Surgeons

The Chinese guidelines for IAI presented here were developed by a panel that included experts from the fields of surgery, critical care, microbiology, infection control, pharmacology, and evidence-based medicine. All questions were structured in population, intervention, comparison, and outcomes format, and evidence profiles were generated. Recommendations were generated following the principles of the Grading of Recommendations Assessment, Development, and Evaluation system or Best Practice Statement (BPS), when applicable. The final guidelines include 45 graded recommendations and 17 BPSs, including the classification of disease severity, diagnosis, source control, antimicrobial therapy, microbiologic evaluation, nutritional therapy, other supportive therapies, diagnosis and management of specific IAIs, and recognition and management of source control failure. Recommendations on fluid resuscitation and organ support therapy could not be formulated and thus were not included. Accordingly, additional high-quality clinical studies should be performed in the future to address the clinicians' concerns.

Correlation of serum uric acid levels in patients of chronic obstructive pulmonary disease

To evaluate serum uric acid level and correlate with the classification of chronic obstructive pulmonary disease (COPD) severity based on post-bronchodilator FEV1 as per Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines. It is an institution based cross-sectional study in which 100 patients were included. Patients were screened for COPD. Clinical profile and oxygen saturation were included. Patients were classified according to GOLD guidelines. We found that serum uric acid level was at higher side in patients of severe and very severe stages of COPD compared to mild and moderate stages. Inverse relationship found between FEV1 and serum uric acid level. We concluded that uric acid is a widely available, easy to interpret, low cost biomarker, suggests the possible role in the identification of COPD patients of increased risk for adverse outcomes that may need early intensive management. Keywords: Chronic obstructive pulmonary disease, GOLD guidelines, Spirometry, Serum uric acid.

Elevated hydroxycholesterols in Norwegian patients with hereditary spastic paraplegia SPG5

Spastic paraplegia type 5 (SPG5/HSP-CYP7B1) is an autosomal recessive hereditary spastic paraplegia (HSP) caused by biallelic variants in the CYP7B1 gene, resulting in dysfunction of the enzyme oxysterol-7-α-hydroxylase. The consequent accumulation of hydroxycholesterols in plasma seems to be pathognomonic for SPG5, and represent a possible target for treatment. We aimed to characterize Norwegian patients with SPG5, including clinical examinations, genetic analyses, measurements of hydroxycholesterols, electrophysiological investigations and brain imaging. Five unrelated patients carried presumed disease-causing variants in CYP7B1, three of the variants were novel. Four patients presented with pure HSP, one with complex HSP. The three tested patients all had markedly increased levels of 25- and 27-hydroxycholesterol in plasma. Our results suggest that the clinical examination is still the best approach to classify disease severity in patients with SPG5. Plasma hydroxycholesterols were elevated, thus presenting as potentially valuable diagnostic biomarkers, in particular in patients where genetic analyses are inconclusive.

Disease severity classification and COVID-19 outcomes, Republic of Korea.

The surge in coronavirus disease 2019 (COVID-19) cases overwhelmed the health system in the Republic of Korea. To help health-care workers prioritize treatment for patients with more severe disease and to decrease the burden on health systems caused by COVID-19, the government established a system to classify disease severity. Health-care staff in city- and provincial-level patient management teams classified the patients into the different categories according to the patients' pulse, systolic blood pressure, respiratory rate, body temperature and level of consciousness. Patients categorized as having moderate, severe and very severe disease were promptly assigned to beds or negative-pressure isolation rooms for hospital treatment, while patients with mild symptoms were monitored in 16 designated facilities across the country. The case fatality rate was higher in the city of Daegu and the Gyeongsangbuk-do province (1.6%; 124/7756) than the rest of the country (0.5%; 7/1485). From 25 February to 26 March 2020, the ratio of negative-pressure isolation rooms per COVID-19 patient was below 0.15 in the city of Daegu and the Gyeongsangbuk-do province. In the rest of the country, this ratio decreased from 5.56 to 0.63 during the same period. Before the classification system was in place, eight (15.7%) out of the 51 deaths occurred at home or during transfer from home to health-care institutions. Categorizing patients according to their disease severity should be a prioritized measure to ease the burden on health systems and reduce the case fatality rate.

S1066 Impact of Alcohol Misuse on Classification of Disease Severity Using Transient Elastography in Veterans

S1066 Impact of Alcohol Misuse on Classification of Disease Severity Using Transient Elastography in Veterans

A machine learning-based diagnostic model associated with knee osteoarthritis severity

Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student’s t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student’s t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.

Emergency and intensive care medicine aspects of COVID-19 infections

Klinisches ProblemDie COVID-19-Pandemie hat in kürzester Zeit zu einer Anpassung des deutschen Gesundheitswesens an die gegebenen Herausforderungen geführt. Während der kassenärztliche Bereich die Betreuung von gering symptomatischen Patienten mit niedrigem Risiko übernimmt, richten die Krankenhäuser ihre Ressourcen zielgerichtet auf die Versorgung von schwer erkrankten COVID-19-Patienten aus.Aufgaben der Notfall- und IntensivmedizinDie Notfallmedizin hat die Aufgabe, Verdachtsfälle rasch zu detektieren, die Schwere der Krankheit einzustufen sowie die notwendige Initialtherapie einzuleiten. Zudem müssen die Patientenströme in das Krankenhaus so gelenkt werden, dass eine optimale Versorgung stattfindet, ohne eine Infektionsgefährdung für Mitpatienten und Gesundheitspersonal. Insbesondere bei älteren und vorerkrankten Patienten weist eine COVID-19-Infektion eine hohe Letalität auf, wenn sich ein Organversagen entwickelt. Trotz optimaler intensivmedizinischer Therapie bleibt die Letalität in diesen Fällen hoch.Neue TherapieverfahrenDie frühestmögliche Detektion einer SARS-CoV-2-Infektion ist die Hauptaufgabe der Notfallmedizin. Die Intensivmedizin übernimmt die Behandlung des SARS-CoV-2-induzierten Organversagens, wobei das Lungenversagen bei diesen Patienten differenzierter Beatmungstherapien bedarf.DiagnostikAls Virusnachweis wird eine Polymerase-Kettenreaktion (PCR) auf SARS-CoV‑2 durchgeführt, ergänzt um eine Thoraxsonographie, ein Thoraxröntgenbild und eine Thorax-Computertomographie. Die radiologische Diagnostik hilft auch bei der Beurteilung des COVID-19-Pneumonie-Typs.Empfehlung für die PraxisDie SARS-CoV-2-Diagnose ist nur durch den Virusnachweis in der PCR bewiesen. Die frühzeitige sonographische und radiologische ergänzt die Diagnostik und erlaubt die Differenzialtherapie für eine COVID-19-Pneumonie.