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1.
JAMA Netw Open ; 5(7): e2220512, 2022 Jul 01.
Article in English | MEDLINE | ID: covidwho-1919178

ABSTRACT

Importance: The Agency for Healthcare Research and Quality (AHRQ) Safety Program for Improving Antibiotic Use aimed to improve antibiotic prescribing in ambulatory care practices by engaging clinicians and staff to incorporate antibiotic stewardship into practice culture, communication, and decision-making. Little is known about implementation of antibiotic stewardship in ambulatory care practices. Objective: To examine changes in visits and antibiotic prescribing during the AHRQ Safety Program. Design, Setting, and Participants: This cohort study evaluated a quality improvement intervention in ambulatory care throughout the US in 389 ambulatory care practices from December 1, 2019, to November 30, 2020. Exposures: The AHRQ Safety Program used webinars, audio presentations, educational tools, and office hours to engage stewardship leaders and clinical staff to address attitudes and cultures that challenge judicious antibiotic prescribing and incorporate best practices for the management of common infections. Main Outcomes and Measures: The primary outcome of the Safety Program was antibiotic prescriptions per 100 acute respiratory infection (ARI) visits. Data on total visits and ARI visits were also collected. The number of visits and prescribing rates from baseline (September 1, 2019) to completion of the program (November 30, 2020) were compared. Results: Of 467 practices enrolled, 389 (83%) completed the Safety Program; of these, 292 (75%) submitted complete data with 6 590 485 visits to 5483 clinicians. Participants included 82 (28%) primary care practices, 103 (35%) urgent care practices, 34 (12%) federally supported practices, 39 (13%) pediatric urgent care practices, 21 (7%) pediatric-only practices, and 14 (5%) other practice types. Visits per practice per month decreased from a mean of 1624 (95% CI, 1317-1931) at baseline to a nadir of 906 (95% CI, 702-1111) early in the COVID-19 pandemic (April 2020), and were 1797 (95% CI, 1510-2084) at the end of the program. Total antibiotic prescribing decreased from 18.2% of visits at baseline to 9.5% at completion of the program (-8.7%; 95% CI, -9.9% to -7.6%). Acute respiratory infection visits per practice per month decreased from baseline (n = 321) to a nadir of 76 early in the pandemic (May 2020) and gradually increased through completion of the program (n = 239). Antibiotic prescribing for ARIs decreased from 39.2% at baseline to 24.7% at completion of the program (-14.5%; 95% CI, -16.8% to -12.2%). Conclusions and Relevance: In this study of US ambulatory practices that participated in the AHRQ Safety Program, significant reductions in the rates of overall and ARI-related antibiotic prescribing were noted, despite normalization of clinic visits by completion of the program. The forthcoming AHRQ Safety Program content may have utility in ambulatory practices across the US.


Subject(s)
COVID-19 , Respiratory Tract Infections , Anti-Bacterial Agents/therapeutic use , Child , Cohort Studies , Health Services Research , Humans , Pandemics , United States
2.
JAMA Intern Med ; 182(6): 612-621, 2022 06 01.
Article in English | MEDLINE | ID: covidwho-1798074

ABSTRACT

Importance: Awake prone positioning may improve hypoxemia among patients with COVID-19, but whether it is associated with improved clinical outcomes remains unknown. Objective: To determine whether the recommendation of awake prone positioning is associated with improved outcomes among patients with COVID-19-related hypoxemia who have not received mechanical ventilation. Design, Setting, and Participants: This pragmatic nonrandomized controlled trial was conducted at 2 academic medical centers (Vanderbilt University Medical Center and NorthShore University HealthSystem) during the COVID-19 pandemic. A total of 501 adult patients with COVID-19-associated hypoxemia who had not received mechanical ventilation were enrolled from May 13 to December 11, 2020. Interventions: Patients were assigned 1:1 to receive either the practitioner-recommended awake prone positioning intervention (intervention group) or usual care (usual care group). Main Outcomes and Measures: Primary outcome analyses were performed using a bayesian proportional odds model with covariate adjustment for clinical severity ranking based on the World Health Organization ordinal outcome scale, which was modified to highlight the worst level of hypoxemia on study day 5. Results: A total of 501 patients (mean [SD] age, 61.0 [15.3] years; 284 [56.7%] were male; and most [417 (83.2%)] were self-reported non-Hispanic or non-Latinx) were included. Baseline severity was comparable between the intervention vs usual care groups, with 170 patients (65.9%) vs 162 patients (66.7%) receiving oxygen via standard low-flow nasal cannula, 71 patients (27.5%) vs 62 patients (25.5%) receiving oxygen via high-flow nasal cannula, and 16 patients (6.2%) vs 19 patients (7.8%) receiving noninvasive positive-pressure ventilation. Nursing observations estimated that patients in the intervention group spent a median of 4.2 hours (IQR, 1.8-6.7 hours) in the prone position per day compared with 0 hours (IQR, 0-0.7 hours) per day in the usual care group. On study day 5, the bayesian posterior probability of the intervention group having worse outcomes than the usual care group on the modified World Health Organization ordinal outcome scale was 0.998 (posterior median adjusted odds ratio [aOR], 1.63; 95% credibility interval [CrI], 1.16-2.31). However, on study days 14 and 28, the posterior probabilities of harm were 0.874 (aOR, 1.29; 95% CrI, 0.84-1.99) and 0.673 (aOR, 1.12; 95% CrI, 0.67-1.86), respectively. Exploratory outcomes (progression to mechanical ventilation, length of stay, and 28-day mortality) did not differ between groups. Conclusions and Relevance: In this nonrandomized controlled trial, prone positioning offered no observed clinical benefit among patients with COVID-19-associated hypoxemia who had not received mechanical ventilation. Moreover, there was substantial evidence of worsened clinical outcomes at study day 5 among patients recommended to receive the awake prone positioning intervention, suggesting potential harm. Trial Registration: ClinicalTrials.gov Identifier: NCT04359797.


Subject(s)
COVID-19 , Adult , Bayes Theorem , COVID-19/therapy , Female , Humans , Hypoxia/etiology , Hypoxia/therapy , Male , Middle Aged , Oxygen , Pandemics , Prone Position , Respiration, Artificial , Wakefulness
3.
BMC Psychiatry ; 22(1): 156, 2022 03 01.
Article in English | MEDLINE | ID: covidwho-1724452

ABSTRACT

BACKGROUND: The outbreak of the COVID-19 pandemic has caused extensive public health concerns, posing significant challenges to healthcare services. One particular area of concern is the mental health of patients with mental disorder, who are often a neglected group. The aim of this study was to investigate the prevalence of, and associated factors for symptoms of post-traumatic stress disorder (PTSD) among patients with mental disorder in China during the COVID-19 pandemic. METHODS: Self-reported questionnaires were distributed to patients in four psychiatric hospitals in Beijing, China, between April 28th and May 30th, 2020. Information regarding sociodemographic characteristics, COVID-19 related factors, support, psychosomatic factors, and PTSD symptoms were collected using a series of scales, such as the Impact of Event Scale-Revised, the 7-item Generalized Anxiety Disorder Scale, the 9-item Patient Health Questionnaire depression scale, and so on. Multivariate regression was used to identify factors related to PTSD symptoms. RESULTS: A total of 1,055 patients with mental disorder were included in the final sample. The prevalence of PTSD symptoms was 41.3%. Hierarchical linear regression demonstrated that fear of the pandemic and anxiety were shared associated factors for both symptoms of PTSD and their subscales. Additionally, age was an associated factor for the total PTSD (ß = 0.12, p < 0.01), intrusion (ß = 0.18, p < 0.001), and avoidance (ß = 0.1, p < 0.05) symptoms; depression was an associated factor for the total PTSD (ß = 0.13, p < 0.001), intrusion (ß = 0.11, p < 0.01), and hyperarousal (ß = 0.19, p < 0.001) symptoms. CONCLUSIONS: The prevalence of PTSD symptoms was high among patients with mental disorder during the COVID-19 pandemic in China. This study found that age, fear of the pandemic, anxiety and depression are significant associated factors of PTSD symptoms in patients with mental disorder during the pandemic. We call for higher awareness and introduction of PTSD interventions to relieve the psychological stress in these patients.


Subject(s)
COVID-19 , Stress Disorders, Post-Traumatic , Anxiety/epidemiology , COVID-19/epidemiology , Cross-Sectional Studies , Depression/epidemiology , Humans , Pandemics , Prevalence , SARS-CoV-2 , Stress Disorders, Post-Traumatic/psychology
4.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-310889

ABSTRACT

Background: Corticosteroid has been proved to be one of the few effective treatments for COVID-19 patients. However, not all the patients were suitable for corticosteroid therapy. In this study, we aimed to propose a machine learning model to forecast the response to corticosteroid therapy in COVID-19 patients.Methods: We retrospectively collected 666 COVID-19 patients receiving corticosteroid therapy between January 27, 2020, and March 30, 2020, from two hospitals in China. The response to corticosteroid therapy was evaluated by hospitalization time, oxygen supply duration, and the outcomes of patients. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms were applied in the training cohort and assessed in an internal and an external validation dataset, respectively.Findings: Two (C reactive protein, lymphocyte percent) of 36 candidate immune/inflammatory features were finally used for model development. All five models displayed promising predictive performance. Notably, the ensemble model, PRCTC (prediction of response to corticosteroid therapy in COVID-19 patients), derived from Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), and Neural Network (NN) achieved the best performance with an area under the curve (AUC) of 0·810 (95% confidence interval [CI] 0·760–0·861) in internal validation cohort and 0·845 (95% CI 0·779–0·911) in external validation cohort to predict patients’ response to corticosteroid therapy.Interpretation: PRCTC proposed with robustness, universality, and scalability is hopeful to provide tangible and prompt clinical decision support in management of COVID-19 patients, and potentially extends to other medication prediction.Funding: Natural Science Foundation of China.Declaration of Interests: All authors declared no competing interest.Ethics Approval Statement: This study was approved by the Research Ethics Commission of Tongji Medical College, Huazhong University of Science and Technology (TJIRB20200406) with waived informed consent by the Ethics Commission mentioned above.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-309456

ABSTRACT

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2,148 COVID-19 cases and 1,182 CAP cases from five hospitals, are conducted to evaluate the performance of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.

7.
Aging (Albany NY) ; 14(1): 54-72, 2022 01 12.
Article in English | MEDLINE | ID: covidwho-1622954

ABSTRACT

Corticosteroid has been proved to be one of the few effective treatments for COVID-19 patients. However, not all the patients were suitable for corticosteroid therapy. In this study, we aimed to propose a machine learning model to forecast the response to corticosteroid therapy in COVID-19 patients. We retrospectively collected the clinical data about 666 COVID-19 patients receiving corticosteroid therapy between January 27, 2020, and March 30, 2020, from two hospitals in China. The response to corticosteroid therapy was evaluated by hospitalization time, oxygen supply duration, and the outcomes of patients. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five prediction models were applied in the training cohort and assessed in an internal and an external validation dataset, respectively. Finally, two (C reactive protein, lymphocyte percent) of 36 candidate immune/inflammatory features were finally used for model development. All five models displayed promising predictive performance. Notably, the ensemble model, PRCTC (prediction of response to corticosteroid therapy in COVID-19 patients), derived from three prediction models including Gradient Boosted Decision Tree (GBDT), Neural Network (NN), and logistic regression (LR), achieved the best performance with an area under the curve (AUC) of 0.810 (95% confidence interval [CI] 0.760-0.861) in internal validation cohort and 0.845 (95% CI 0.779-0.911) in external validation cohort to predict patients' response to corticosteroid therapy. In conclusion, PRCTC proposed with universality and scalability is hopeful to provide tangible and prompt clinical decision support in management of COVID-19 patients and potentially extends to other medication predictions.


Subject(s)
Adrenal Cortex Hormones/administration & dosage , COVID-19/drug therapy , Machine Learning , Aged , Algorithms , COVID-19/virology , China , Female , Humans , Logistic Models , Male , Middle Aged , Neural Networks, Computer , Retrospective Studies , SARS-CoV-2/physiology , Treatment Outcome
9.
Open forum infectious diseases ; 8(Suppl 1):S97-S97, 2021.
Article in English | EuropePMC | ID: covidwho-1564344

ABSTRACT

Background The AHRQ Safety Program for Improving Antibiotic Use aimed to improve antibiotic use by engaging clinicians and staff to incorporate antibiotic stewardship (AS) into practice culture, communication, and decision making. We report on changes in visits and antibiotic prescribing in AHRQ Safety Program ambulatory practices during the COVID-19 pandemic. Methods The Safety Program used webinars, audio presentations, educational tools, and office hours to engage clinician champions and staff leaders to: (a) address attitudes and culture that pose challenges to judicious antibiotic prescribing and (b) incorporate best practices for the management of common infections into their workflow using the Four Moments of Antibiotic Decision Making framework. Total visits (in-person and virtual), acute respiratory infection (ARI) visits, and antibiotic prescribing data were collected. Using linear mixed models to account for random effects of participating practices and repeated measurements of outcomes within practices over time, data from the pre-intervention period (September-November 2019) and the Ambulatory Care Safety Program (December 2019-November 2020) were compared. Results Of 467 practices enrolled, 389 (83%) completed the program, including 162 primary care practices (42%;23 [6%] pediatric), 160 urgent care practices (41%;40 [10%] pediatric), and 49 federally-supported practices (13%). 292 practices submitted complete data for analysis, including 6,590,485 visits. Visits/practice-month declined March-May 2020 but gradually returned to baseline by program end (Figure 1). Total antibiotic prescribing declined by 9 prescriptions/100 visits (95% CI: -10 to -8). ARI visits/practice-month declined significantly in March-May 2020, then increased but remained below baseline by program end (Figure 2). ARI-related antibiotic prescriptions decreased by 15/100 ARI visits by program end (95% CI: -17 to -12). The greatest reduction was in penicillin class prescriptions with a reduction of 7/100 ARI visits by program end (95% CI: -9 to -6). Conclusion During the COVID-19 pandemic, a national ambulatory AS program was associated with declines in overall and ARI-related antibiotic prescribing. Disclosures Pranita Tamma, MD, MHS, Nothing to disclose Sara E. Cosgrove, MD, MS, Basilea (Individual(s) Involved: Self): Consultant Jeffrey A. Linder, MD, MPH, FACP, Amgen (Shareholder)Biogen (Shareholder)Eli Lilly (Shareholder)

10.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-296121

ABSTRACT

Background: The outbreak of the COVID-19 pandemic has caused extensive public health concerns, posing significant challenges to healthcare services. One particular area of concern is the mental health of psychiatric patients, who are often a neglected group. The aim of this study was to investigate the prevalence of, and associated factors for symptoms of post-traumatic stress disorder (PTSD) among psychiatric patients in China during the COVID-19 pandemic. Methods: : Self-reported questionnaires were distributed to patients in four psychiatric hospitals in Beijing, China, between April 28 th and May 30 th , 2020. Information regarding sociodemographic characteristics, COVID-19 related factors, support, psychosomatic factors, and PTSD symptoms was collected data using a series of scales, such as the Impact of Event Scale-Revised, the 7-item Generalized Anxiety Disorder Scale, the 9-item Patient Health Questionnaire depression scale, and so on. Multivariate regression was used to identify factors related to PTSD symptoms. Results: : A total of 1,055 psychiatric patients were included in the final sample. The prevalence of PTSD symptoms was 41.3%. Hierarchical linear regression demonstrated that fear of the pandemic and anxiety were shared associated factors for both symptoms of PTSD and its subscales. Additionally, age was an associated factor for the total PTSD ( β = 0.12, p < 0.01), intrusion ( β = 0.18, p < 0.001), and avoidance ( β = 0.1, p < 0.05) symptoms;depression was an associated factor for the total PTSD s ( β = 0.13, p < 0.001), intrusion ( β = 0.11, p < 0.01), and hyperarousal ( β = 0.19, p < 0.001) symptoms. Conclusions: : The prevalence of PTSD symptoms was high among psychiatric patients during the COVID-19 pandemic in China. This study found that age, fear of the pandemic, anxiety and depression are significant associated factors of PTSD symptoms in psychiatric patients during the pandemic. We call for higher awareness and introduction of PTSD interventions to relieve the psychological stress in these patients.

11.
Am J Otolaryngol ; 43(1): 103263, 2022.
Article in English | MEDLINE | ID: covidwho-1469812

ABSTRACT

OBJECTIVES: During the COVID-19 pandemic, maintenance of safe and timely oncologic care has been challenging. The goal of this study is to compare presenting symptoms, staging, and treatment of head and neck mucosal squamous cell carcinoma during the pandemic with an analogous timeframe one year prior. MATERIALS AND METHODS: Retrospective cohort study at a single tertiary academic center of new adult patients evaluated in a head and neck surgical oncology clinic from March -July 2019 (pre-pandemic control) and March - July 2020 (COVID-19 pandemic). RESULTS: During the pandemic, the proportion of patients with newly diagnosed malignancies increased by 5%, while the overall number of new patients decreased (n = 575) compared to the control year (n = 776). For patients with mucosal squamous cell carcinoma (SCC), median time from referral to initial clinic visit decreased from 11 days (2019) to 8 days (2020) (p = 0.0031). There was no significant difference in total number (p = 0.914) or duration (p = 0.872) of symptoms. During the pandemic, patients were more likely to present with regional nodal metastases (adjusted odds ratio (OR) 2.846, 95% CI 1.072-3.219, p = 0.028) and more advanced clinical nodal (N) staging (p = 0.011). No significant difference was seen for clinical tumor (T) (p = 0.502) or metastasis (M) staging (p = 0.278). No significant difference in pathologic T (p = 0.665), or N staging (p = 0.907) was found between the two periods. CONCLUSION: Head and neck mucosal SCC patients presented with more advanced clinical nodal disease during the early months of the COVID-19 pandemic despite no change in presenting symptoms.


Subject(s)
COVID-19/epidemiology , Squamous Cell Carcinoma of Head and Neck/epidemiology , Squamous Cell Carcinoma of Head and Neck/pathology , Aged , Female , Humans , Lymphatic Metastasis , Male , Middle Aged , Neoplasm Staging , Pandemics , Retrospective Studies , SARS-CoV-2 , Squamous Cell Carcinoma of Head and Neck/therapy , Tennessee/epidemiology
12.
Ann Palliat Med ; 10(7): 7270-7279, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1311481

ABSTRACT

BACKGROUND: We aim to investigate the clinical characteristics and survival rate of coronavirus disease 2019 (COVID-19) patients. METHODS: Ninety-seven COVID-19 patients were enrolled. The laboratory results, lung imaging and medical treatment were compared. Patients were followed up after 1 year, and the Kaplan-Meier test was used for survival analysis. RESULTS: Compared with the non-severe group, the age of the severe group was older, and the proportion of concomitant diseases were higher. As fever was the primary clinical manifestation, dyspnea and anorexia were more common in severe patients. Lung imaging manifestations and laboratory indicators were worse in the severe group. Accordingly, the treatment of glucocorticoid, antibiotics, and advanced life support were in high proportion. Of the 97 patients with COVID-19, 4 severe patients died within one month during the 1-year follow-up, with the median survival time of 47.0 weeks (95% CI: 45.1-48.9). CONCLUSIONS: Severe cases of COVID-19 are characterized by advanced age, more concomitant diseases and complications, which lead to a decreased short-term survival rate. However, there were no deaths after one month, which implied a good prognosis if the risk period were passed smoothly.


Subject(s)
COVID-19 , Humans , Lung , Retrospective Studies , SARS-CoV-2 , Survival Analysis
13.
Pattern Recognit ; 118: 108006, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1230705

ABSTRACT

The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.

14.
J Thorac Dis ; 13(3): 1380-1395, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-1175846

ABSTRACT

BACKGROUND: Most evidence regarding the risk factors for early in-hospital mortality in patients with severe COVID-19 focused on laboratory data at the time of hospital admission without adequate adjustment for confounding variables. A multicenter, age-matched, case-control study was therefore designed to explore the dynamic changes in laboratory parameters during the first 10 days after admission and identify early risk indicators for in-hospital mortality in this patient cohort. METHODS: Demographics and clinical data were extracted from the medical records of 93 pairs of patients who had been admitted to hospital with severe COVID-19. These patients had either been discharged or were deceased by March 3, 2020. Data from days 1, 4, 7, and 10 of hospital admission were compared between survivors and non-survivors. Univariate and multivariate conditional logistic regression analyses were employed to identify early risk indicators of in-hospital death in this cohort. RESULTS: On admission, in-hospital mortality was associated with five risk indicators (ORs in descending order): aspartate aminotransferase (AST, >32 U/L) 43.20 (95% CI: 2.63, 710.04); C-reactive protein (CRP) greater than 100 mg/L 13.61 (1.78, 103.941); lymphocyte count lower than 0.6×109/L 9.95 (1.30, 76.42); oxygen index (OI) less than 200 8.23 (1.04, 65.15); and D-dimer over 1 mg/L 8.16 (1.23, 54.34). Sharp increases in D-dimer at day 4, accompanied by decreasing lymphocyte counts, deteriorating OI, and persistent remarkably high CRP concentration were observed among non-survivors during the early stages of hospital admission. CONCLUSIONS: The potential risk factors of high D-dimer, CRP, AST, low lymphocyte count and OI could help clinicians identify patients at high risk of death early in the hospital admission. This might assist with rationalization of health care resources.

15.
J Intensive Care ; 9(1): 19, 2021 Feb 18.
Article in English | MEDLINE | ID: covidwho-1090600

ABSTRACT

BACKGROUND: Immune and inflammatory dysfunction was reported to underpin critical COVID-19(coronavirus disease 2019). We aim to develop a machine learning model that enables accurate prediction of critical COVID-19 using immune-inflammatory features at admission. METHODS: We retrospectively collected 2076 consecutive COVID-19 patients with definite outcomes (discharge or death) between January 27, 2020 and March 30, 2020 from two hospitals in China. Critical illness was defined as admission to intensive care unit, receiving invasive ventilation, or death. Least Absolute Shrinkage and Selection Operator (LASSO) was applied for feature selection. Five machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosted Decision Tree (GBDT), K-Nearest Neighbor (KNN), and Neural Network (NN) were built in a training dataset, and assessed in an internal validation dataset and an external validation dataset. RESULTS: Six features (procalcitonin, [T + B + NK cell] count, interleukin 6, C reactive protein, interleukin 2 receptor, T-helper lymphocyte/T-suppressor lymphocyte) were finally used for model development. Five models displayed varying but all promising predictive performance. Notably, the ensemble model, SPMCIIP (severity prediction model for COVID-19 by immune-inflammatory parameters), derived from three contributive algorithms (SVM, GBDT, and NN) achieved the best performance with an area under the curve (AUC) of 0.991 (95% confidence interval [CI] 0.979-1.000) in internal validation cohort and 0.999 (95% CI 0.998-1.000) in external validation cohort to identify patients with critical COVID-19. SPMCIIP could accurately and expeditiously predict the occurrence of critical COVID-19 approximately 20 days in advance. CONCLUSIONS: The developed online prediction model SPMCIIP is hopeful to facilitate intensive monitoring and early intervention of high risk of critical illness in COVID-19 patients. TRIAL REGISTRATION: This study was retrospectively registered in the Chinese Clinical Trial Registry ( ChiCTR2000032161 ). vv.

16.
Oncoimmunology ; 10(1): 1854424, 2021 01 04.
Article in English | MEDLINE | ID: covidwho-1035815

ABSTRACT

Patients with malignancy were reportedly more susceptible and vulnerable to Coronavirus Disease 2019 (COVID-19), and witnessed a greater mortality risk in COVID-19 infection than noncancerous patients. But the role of immune dysregulation of malignant patients on poor prognosis of COVID-19 has remained insufficiently investigated. Here we conducted a retrospective cohort study that included 2,052 patients hospitalized with COVID-19 (Cancer, n = 93; Non-cancer, n = 1,959), and compared the immunological characteristics of both cohorts. We used stratification analysis, multivariate regressions, and propensity-score matching to evaluate the effect of immunological indices. In result, COVID-19 patients with cancer had ongoing and significantly elevated inflammatory factors and cytokines (high-sensitivity C-reactive protein, procalcitonin, interleukin (IL)-2 receptor, IL-6, IL-8), as well as decreased immune cells (CD8 + T cells, CD4 + T cells, B cells, NK cells, Th and Ts cells) than those without cancer. The mortality rate was significantly higher in cancer cohort (24.7%) than non-cancer cohort (10.8%). By stratification analysis, COVID-19 patients with immune dysregulation had poorer prognosis than those with the relatively normal immune system both in cancer and non-cancer cohort. By logistic regression, Cox regression, and propensity-score matching, we found that prior to adjustment for immunological indices, cancer history was associated with an increased mortality risk of COVID-19 (p < .05); after adjustment for immunological indices, cancer history was no longer an independent risk factor for poor prognosis of COVID-19 (p > .30). In conclusion, COVID-19 patients with cancer had more severely dysregulated immune responses than noncancerous patients, which might account for their poorer prognosis. Clinical Trial: This study has been registered on the Chinese Clinical Trial Registry (No. ChiCTR2000032161).


Subject(s)
COVID-19/mortality , Neoplasms/immunology , SARS-CoV-2/immunology , Aged , COVID-19/diagnosis , COVID-19/immunology , COVID-19/virology , Case-Control Studies , China/epidemiology , Female , Humans , Male , Middle Aged , Neoplasms/mortality , Prognosis , Retrospective Studies , Risk Factors , SARS-CoV-2/isolation & purification , Severity of Illness Index
17.
J Med Internet Res ; 23(1): e25535, 2021 01 06.
Article in English | MEDLINE | ID: covidwho-1011363

ABSTRACT

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.


Subject(s)
COVID-19/diagnosis , Decision Support Systems, Clinical , Health , Machine Learning , Pneumonia, Viral/diagnosis , COVID-19/diagnostic imaging , Diagnosis, Differential , Humans , Middle Aged , Pneumonia, Viral/diagnostic imaging , SARS-CoV-2 , Support Vector Machine , Tomography, X-Ray Computed
18.
Oncoimmunology ; 10(1): 1854424, 2021 01 04.
Article in English | MEDLINE | ID: covidwho-1010163

ABSTRACT

Patients with malignancy were reportedly more susceptible and vulnerable to Coronavirus Disease 2019 (COVID-19), and witnessed a greater mortality risk in COVID-19 infection than noncancerous patients. But the role of immune dysregulation of malignant patients on poor prognosis of COVID-19 has remained insufficiently investigated. Here we conducted a retrospective cohort study that included 2,052 patients hospitalized with COVID-19 (Cancer, n = 93; Non-cancer, n = 1,959), and compared the immunological characteristics of both cohorts. We used stratification analysis, multivariate regressions, and propensity-score matching to evaluate the effect of immunological indices. In result, COVID-19 patients with cancer had ongoing and significantly elevated inflammatory factors and cytokines (high-sensitivity C-reactive protein, procalcitonin, interleukin (IL)-2 receptor, IL-6, IL-8), as well as decreased immune cells (CD8 + T cells, CD4 + T cells, B cells, NK cells, Th and Ts cells) than those without cancer. The mortality rate was significantly higher in cancer cohort (24.7%) than non-cancer cohort (10.8%). By stratification analysis, COVID-19 patients with immune dysregulation had poorer prognosis than those with the relatively normal immune system both in cancer and non-cancer cohort. By logistic regression, Cox regression, and propensity-score matching, we found that prior to adjustment for immunological indices, cancer history was associated with an increased mortality risk of COVID-19 (p < .05); after adjustment for immunological indices, cancer history was no longer an independent risk factor for poor prognosis of COVID-19 (p > .30). In conclusion, COVID-19 patients with cancer had more severely dysregulated immune responses than noncancerous patients, which might account for their poorer prognosis. Clinical Trial: This study has been registered on the Chinese Clinical Trial Registry (No. ChiCTR2000032161).


Subject(s)
COVID-19/mortality , Neoplasms/immunology , SARS-CoV-2/immunology , Aged , COVID-19/diagnosis , COVID-19/immunology , COVID-19/virology , Case-Control Studies , China/epidemiology , Female , Humans , Male , Middle Aged , Neoplasms/mortality , Prognosis , Retrospective Studies , Risk Factors , SARS-CoV-2/isolation & purification , Severity of Illness Index
20.
Med Image Anal ; 68: 101910, 2021 02.
Article in English | MEDLINE | ID: covidwho-943426

ABSTRACT

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2148 COVID-19 cases and 1182 CAP cases from five hospitals, are conducted to evaluate the prediction accuracy of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.


Subject(s)
COVID-19/diagnostic imaging , Community-Acquired Infections/diagnostic imaging , Diagnosis, Computer-Assisted/methods , Machine Learning , Pneumonia, Viral/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Tomography, X-Ray Computed , China , Community-Acquired Infections/virology , Datasets as Topic , Diagnosis, Differential , Humans , Pneumonia, Viral/virology , SARS-CoV-2
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