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1.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-306115

ABSTRACT

We predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. DL and machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using patient CXRs. A novel radiomic embedding framework was also explored for outcome prediction. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic and DL classification models had mAUCs of 0.78+/-0.02 and 0.81+/-0.04, compared with expert scores mAUCs of 0.75+/-0.02 and 0.79+/-0.05 for mechanical ventilation requirement and mortality prediction, respectively. Combined classifiers using both radiomics and expert severity scores resulted in mAUCs of 0.79+/-0.04 and 0.83+/-0.04 for each prediction task, demonstrating improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances where inclusion of radiomic features in DL improves model predictions, something that might be explored in other pathologies. The models proposed in this study and the prognostic information they provide might aid physician decision making and resource allocation during the COVID-19 pandemic.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-316089

ABSTRACT

Media reports and large-scale surveys have documented significant heterogeneity in employees’ experience with COVID-19 necessitated remote work arrangements. Prior academic research is limited in the extent to which it can account for this heterogeneity because (1) it has evaluated effects of remote work on workers and organizations that have self-selected into remote work arrangements, and (2) has paid little attention to the role that job characteristics play in determining the impact of remote work. This research note overcomes these limitations and explicates factors that account for the diverse impacts of remote work arrangements. We utilize the context of exogenously imposed remote work arrangements during the COVID-19 pandemic to evaluate whether effects of remote work on employee outcomes differ systematically based on the extent to which employees’ work-related tasks are characterized by the need for human proximity (NHP) and whether the use of information and communication technologies (ICTs) have differential impacts on employee outcomes in low and high NHP jobs. Analysis of a multi-source dataset consisting of NHP scores for 107 job types estimated before the COVID-19 induced country-wide lockdown in India (N=3,099) and a survey of employees’ experience working remotely conducted during the lockdown (N=403) reveals that employees working in jobs that entail high NHP are both less productive and experience greater isolation than those working in jobs with low NHP. Although the use of videoconferencing technologies compensates for the relatively lower productivity of employees in jobs with high NHP, it doesn’t similarly reduce the extent to which they experience isolation. These findings generate several important implications for organizational and governmental policy and make important theoretical and methodological contributions to prior research on remote work.

3.
J Clin Med ; 9(12)2020 Dec 21.
Article in English | MEDLINE | ID: covidwho-1463718

ABSTRACT

Patients receiving mechanical ventilation for coronavirus disease 2019 (COVID-19) related, moderate-to-severe acute respiratory distress syndrome (CARDS) have mortality rates between 76-98%. The objective of this retrospective cohort study was to identify differences in prone ventilation effects on oxygenation, pulmonary infiltrates (as observed on chest X-ray (CXR)), and systemic inflammation in CARDS patients by survivorship and to identify baseline characteristics associated with survival after prone ventilation. The study cohort included 23 patients with moderate-to-severe CARDS who received prone ventilation for ≥16 h/day and was segmented by living status: living (n = 6) and deceased (n = 17). Immediately after prone ventilation, PaO2/FiO2 improved by 108% (p < 0.03) for the living and 150% (p < 3 × 10-4) for the deceased. However, the 48 h change in lung infiltrate severity in gravity-dependent lung zones was significantly better for the living than for the deceased (p < 0.02). In CXRs of the lower lungs before prone ventilation, we observed 5 patients with confluent infiltrates bilaterally, 12 patients with ground-glass opacities (GGOs) bilaterally, and 6 patients with mixed infiltrate patterns; 80% of patients with confluent infiltrates were alive vs. 8% of patients with GGOs. In conclusion, our small study indicates that CXRs may offer clinical utility in selecting patients with moderate-to-severe CARDS who will benefit from prone ventilation. Additionally, our study suggests that lung infiltrate severity may be a better indicator of patient disposition after prone ventilation than PaO2/FiO2.

4.
Diagnostics (Basel) ; 11(10)2021 Sep 30.
Article in English | MEDLINE | ID: covidwho-1444130

ABSTRACT

In this study, we aimed to predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXRs) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and random forest (RF) machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using radiomic features extracted from patients' CXRs. Deep learning (DL) approaches were also explored for the clinical outcome prediction task and a novel radiomic embedding framework was introduced. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic classification models had mean area under the receiver operating characteristic curve (mAUCs) of 0.78 ± 0.05 (sensitivity = 0.72 ± 0.07, specificity = 0.72 ± 0.06) and 0.78 ± 0.06 (sensitivity = 0.70 ± 0.09, specificity = 0.73 ± 0.09), compared with expert scores mAUCs of 0.75 ± 0.02 (sensitivity = 0.67 ± 0.08, specificity = 0.69 ± 0.07) and 0.79 ± 0.05 (sensitivity = 0.69 ± 0.08, specificity = 0.76 ± 0.08) for mechanical ventilation requirement and mortality prediction, respectively. Classifiers using both expert severity scores and radiomic features for mechanical ventilation (mAUC = 0.79 ± 0.04, sensitivity = 0.71 ± 0.06, specificity = 0.71 ± 0.08) and mortality (mAUC = 0.83 ± 0.04, sensitivity = 0.79 ± 0.07, specificity = 0.74 ± 0.09) demonstrated improvement over either artificial intelligence or radiologist interpretation alone. Our results also suggest instances in which the inclusion of radiomic features in DL improves model predictions over DL alone. The models proposed in this study and the prognostic information they provide might aid physician decision making and efficient resource allocation during the COVID-19 pandemic.

5.
Journal of Clinical Medicine ; 9(12):4129, 2020.
Article in English | ScienceDirect | ID: covidwho-984453

ABSTRACT

Patients receiving mechanical ventilation for coronavirus disease 2019 (COVID-19) related, moderate-to-severe acute respiratory distress syndrome (CARDS) have mortality rates between 76–98%. The objective of this retrospective cohort study was to identify differences in prone ventilation effects on oxygenation, pulmonary infiltrates (as observed on chest X-ray (CXR)), and systemic inflammation in CARDS patients by survivorship and to identify baseline characteristics associated with survival after prone ventilation. The study cohort included 23 patients with moderate-to-severe CARDS who received prone ventilation for ≥16 h/day and was segmented by living status: living (n = 6) and deceased (n = 17). Immediately after prone ventilation, PaO2/FiO2 improved by 108% (p <0.03) for the living and 150% (p <3 ×10−4) for the deceased. However, the 48 h change in lung infiltrate severity in gravity-dependent lung zones was significantly better for the living than for the deceased (p <0.02). In CXRs of the lower lungs before prone ventilation, we observed 5 patients with confluent infiltrates bilaterally, 12 patients with ground-glass opacities (GGOs) bilaterally, and 6 patients with mixed infiltrate patterns;80% of patients with confluent infiltrates were alive vs. 8% of patients with GGOs. In conclusion, our small study indicates that CXRs may offer clinical utility in selecting patients with moderate-to-severe CARDS who will benefit from prone ventilation. Additionally, our study suggests that lung infiltrate severity may be a better indicator of patient disposition after prone ventilation than PaO2/FiO2.

6.
Case Rep Pulmonol ; 2020: 8849068, 2020.
Article in English | MEDLINE | ID: covidwho-991972

ABSTRACT

Bacterial coinfections are not uncommon with respiratory viral pathogens. These coinfections can add to significant mortality and morbidity. We are currently dealing with the SARS-CoV-2 pandemic, which has affected over 15 million people globally with over half a million deaths. Previous respiratory viral pandemics have taught us that bacterial coinfections can lead to higher mortality and morbidity. However, there is limited literature on the current SARS-CoV-2 pandemic and associated coinfections, which reported infection rates varying between 1% and 8% based on various cross-sectional studies. In one meta-analysis of coinfections in COVID-19, rates of Streptococcus pneumoniae coinfections have been negligible when compared to previous influenza pandemics. Current literature does not favor the use of empiric, broad-spectrum antibiotics in confirmed SARS-CoV-2 infections. We present three cases of confirmed SARS-CoV-2 infections complicated by Streptococcus pneumoniae coinfection. These cases demonstrate the importance of concomitant testing for common pathogens despite the need for antimicrobial stewardship.

7.
ArXiv ; 2020 Jul 15.
Article in English | MEDLINE | ID: covidwho-822389

ABSTRACT

OBJECTIVES: To predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXR) for coronavirus disease 2019 (COVID-19) patients. We also investigate the relative advantages of deep learning (DL), radiomics, and DL of radiomic-embedded feature maps in predicting these outcomes. METHODS: This two-center, retrospective study analyzed deidentified CXRs taken from 514 patients suspected of COVID-19 infection on presentation at Stony Brook University Hospital (SBUH) and Newark Beth Israel Medical Center (NBIMC) between the months of March and June 2020. A DL segmentation pipeline was developed to generate masks for both lung fields and artifacts for each CXR. Machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated on 353 baseline CXRs taken from COVID-19 positive patients. A novel radiomic embedding framework is also explored for outcome prediction. RESULTS: Classification models for mechanical ventilation requirement (test N=154) and mortality (test N=190) had AUCs of up to 0.904 and 0.936, respectively. We also found that the inclusion of radiomic-embedded maps improved DL model predictions of clinical outcomes. CONCLUSIONS: We demonstrate the potential for computerized analysis of baseline CXR in predicting disease outcomes in COVID-19 patients. Our results also suggest that radiomic embedding improves DL models in medical image analysis, a technique that might be explored further in other pathologies. The models proposed in this study and the prognostic information they provide, complementary to other clinical data, might be used to aid physician decision making and resource allocation during the COVID-19 pandemic.

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