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Kokkoris S, Kanavou A, Katsaros D, Karageorgiou S, Kremmydas P, Gkoufa A, Ntaidou T, Giannopoulos C, Kardamitsi MA, Dimopoulou G, Theodorou E, Georgakopoulou VE, Spandidos DA, Orfanos S, Kotanidou A, Routsi C. Temporal trends in laboratory parameters in survivors and non‑survivors of critical COVID‑19 illness and the effect of dexamethasone treatment. Biomed Rep 2024; 20:12. [PMID: 38124763 PMCID: PMC10731161 DOI: 10.3892/br.2023.1700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/13/2023] [Indexed: 12/23/2023] Open
Abstract
Although coronavirus disease 2019 (COVID-19)-induced changes in laboratory parameters in patients upon admission have been well-documented, information on their temporal changes is limited. The present study describes the laboratory trends and the effect of dexamethasone treatment on these parameters, in patients with COVID-19 in the intensive care unit (ICU). Routine laboratory parameters, namely white blood cell (WBC), neutrophil, lymphocyte and platelet (PLT) counts, fibrinogen, C-reactive protein (CRP), lactate dehydrogenase (LDH) and albumin concentrations, were recorded upon admission to the ICU and, thereafter, on days 3, 5, 10, 15 and 21; these values were compared between survivors and non-survivors, as well as between those who were treated with dexamethasone and those who were not. Among the 733 patients in the ICU, (mean age, 65±13 years; 68% males; ICU mortality rate 45%; 76% of patients treated with dexamethasone), the WBC and neutrophil counts were persistently high in all patients, without significant differences over the first 15 days. Initially, low lymphocyte counts exhibited increasing trends, but remained higher in survivors compared to non-survivors (P=0.01). The neutrophil-to-lymphocyte ratio (NLR) was persistently elevated in all patients, although it was significantly higher in non-survivors compared to survivors (P<0.001). The PLT count was initially increased in all patients, although it was significantly decreased in non-survivors over time. The fibrinogen and LDH values remained similarly elevated in all patients. However, the increased levels of CRP, which did not differ between patients upon admission, further increased in non-survivors compared to survivors after day 10 (P=0.001). Declining trends in albumin levels over time, overall, with a significant decrease in non-survivors compared to survivors, were observed. Dexamethasone treatment significantly affected the temporal progression of fibrinogen and CRP in survivors and that of NLR in non-survivors. On the whole, the present study demonstrates that patients in the ICU with COVID-19 present persistently abnormal laboratory findings and significant differences in laboratory trends of NLR, CRP, PLT and albumin, but not in WBC and neutrophil count, and fibrinogen and LDH levels, between survivors and non-survivors. The temporal progression of fibrinogen, CRP and NLR is affected by dexamethasone treatment.
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Affiliation(s)
- Stelios Kokkoris
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Angeliki Kanavou
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Dimitrios Katsaros
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Stavros Karageorgiou
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Panagiotis Kremmydas
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Aikaterini Gkoufa
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
- Department of Infectious Diseases, Laiko General Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Theodora Ntaidou
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Charalampos Giannopoulos
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Marina-Areti Kardamitsi
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Georgia Dimopoulou
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Evangelia Theodorou
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | | | - Demetrios A. Spandidos
- Laboratory of Clinical Virology, Medical School, University of Crete, 71003 Heraklion, Greece
| | - Stylianos Orfanos
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Anastasia Kotanidou
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Christina Routsi
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
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Lu JY, Boparai MS, Shi C, Henninger EM, Rangareddy M, Veeraraghavan S, Mirhaji P, Fisher MC, Duong TQ. Long-term outcomes of COVID-19 survivors with hospital AKI: association with time to recovery from AKI. Nephrol Dial Transplant 2023; 38:2160-2169. [PMID: 36702551 DOI: 10.1093/ndt/gfad020] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Indexed: 01/28/2023] Open
Abstract
BACKGROUND Although coronavirus disease 2019 (COVID-19) patients who develop in-hospital acute kidney injury (AKI) have worse short-term outcomes, their long-term outcomes have not been fully characterized. We investigated 90-day and 1-year outcomes after hospital AKI grouped by time to recovery from AKI. METHODS This study consisted of 3296 COVID-19 patients with hospital AKI stratified by early recovery (<48 hours), delayed recovery (2-7 days) and prolonged recovery (>7-90 days). Demographics, comorbidities and laboratory values were obtained at admission and up to the 1-year follow-up. The incidence of major adverse cardiovascular events (MACE) and major adverse kidney events (MAKE), rehospitalization, recurrent AKI and new-onset chronic kidney disease (CKD) were obtained 90-days after COVID-19 discharge. RESULTS The incidence of hospital AKI was 28.6%. Of the COVID-19 patients with AKI, 58.0% experienced early recovery, 14.8% delayed recovery and 27.1% prolonged recovery. Patients with a longer AKI recovery time had a higher prevalence of CKD (P < .05) and were more likely to need invasive mechanical ventilation (P < .001) and to die (P < .001). Many COVID-19 patients developed MAKE, recurrent AKI and new-onset CKD within 90 days, and these incidences were higher in the prolonged recovery group (P < .05). The incidence of MACE peaked 20-40 days postdischarge, whereas MAKE peaked 80-90 days postdischarge. Logistic regression models predicted 90-day MACE and MAKE with 82.4 ± 1.6% and 79.6 ± 2.3% accuracy, respectively. CONCLUSION COVID-19 survivors who developed hospital AKI are at high risk for adverse cardiovascular and kidney outcomes, especially those with longer AKI recovery times and those with a history of CKD. These patients may require long-term follow-up for cardiac and kidney complications.
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Affiliation(s)
- Justin Y Lu
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
| | - Montek S Boparai
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
| | - Caroline Shi
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
| | - Erin M Henninger
- Center for Health Data Innovation, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
| | - Mahendranath Rangareddy
- Center for Health Data Innovation, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
| | - Sudhakar Veeraraghavan
- Center for Health Data Innovation, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
| | - Parsa Mirhaji
- Center for Health Data Innovation, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
| | - Molly C Fisher
- Department of Medicine, Nephrology Division, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Tim Q Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
- Center for Health Data Innovation, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
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Kokkoris S, Kanavou A, Kremmydas P, Katsaros D, Karageorgiou S, Gkoufa A, Georgakopoulou VE, Spandidos DA, Giannopoulos C, Kardamitsi M, Routsi C. Temporal evolution of laboratory characteristics in patients critically ill with COVID‑19 admitted to the intensive care unit (Review). MEDICINE INTERNATIONAL 2023; 3:52. [PMID: 37810906 PMCID: PMC10557099 DOI: 10.3892/mi.2023.112] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 09/12/2023] [Indexed: 10/10/2023]
Abstract
In the context of coronavirus disease 2019 (COVID-19), laboratory medicine has played a crucial role in both diagnosis and severity assessment. Although the importance of baseline laboratory findings has been extensively reported, data regarding their evolution over the clinical course are limited. The aim of the present narrative review was to provide the dynamic changes of the routine laboratory variables reported in patients with severe COVID-19 over the course of their critical illness. A search was made of the literature for articles providing data on the time-course of routine laboratory tests in patients with severe COVID-19 during their stay in the intensive care unit (ICU). White blood cell, neutrophil and lymphocyte counts, neutrophil to lymphocyte ratio, platelet counts, as well as D-dimer, fibrinogen, C-reactive protein, lactate dehydrogenase and serum albumin levels were selected as disease characteristics and routine laboratory parameters. A total of 25 research articles reporting dynamic trends in the aforementioned laboratory parameters over the clinical course of severe COVID-19 were identified. During the follow-up period provided by each study, the majority of the laboratory values remained persistently abnormal in both survivors and non-survivors. Furthermore, in the majority of studies, the temporal trends of laboratory values distinctly differentiated patients between survivors and non-survivors. In conclusion, there are distinct temporal trends in selected routine laboratory parameters between survivors and non-survivors with severe COVID-19 admitted to the ICU, indicating their importance in the prognosis of clinical outcome.
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Affiliation(s)
- Stelios Kokkoris
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Angeliki Kanavou
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Panagiotis Kremmydas
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Dimitrios Katsaros
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Stavros Karageorgiou
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Aikaterini Gkoufa
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
- Department of Infectious Diseases, Laiko General Hospital, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | | | - Demetrios A. Spandidos
- Laboratory of Clinical Virology, School of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Charalampos Giannopoulos
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Marina Kardamitsi
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
| | - Christina Routsi
- First Department of Intensive Care, Evangelismos Hospital, Medical School, National and Kapodistrian University of Athens, 10676 Athens, Greece
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Xu J, Cao Z, Miao C, Zhang M, Xu X. Predicting omicron pneumonia severity and outcome: a single-center study in Hangzhou, China. Front Med (Lausanne) 2023; 10:1192376. [PMID: 37305146 PMCID: PMC10250627 DOI: 10.3389/fmed.2023.1192376] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 05/08/2023] [Indexed: 06/13/2023] Open
Abstract
Background In December 2022, there was a large Omicron epidemic in Hangzhou, China. Many people were diagnosed with Omicron pneumonia with variable symptom severity and outcome. Computed tomography (CT) imaging has been proven to be an important tool for COVID-19 pneumonia screening and quantification. We hypothesized that CT-based machine learning algorithms can predict disease severity and outcome in Omicron pneumonia, and we compared its performance with the pneumonia severity index (PSI)-related clinical and biological features. Methods Our study included 238 patients with the Omicron variant who have been admitted to our hospital in China from 15 December 2022 to 16 January 2023 (the first wave after the dynamic zero-COVID strategy stopped). All patients had a positive real-time polymerase chain reaction (PCR) or lateral flow antigen test for SARS-CoV-2 after vaccination and no previous SARS-CoV-2 infections. We recorded patient baseline information pertaining to demographics, comorbid conditions, vital signs, and available laboratory data. All CT images were processed with a commercial artificial intelligence (AI) algorithm to obtain the volume and percentage of consolidation and infiltration related to Omicron pneumonia. The support vector machine (SVM) model was used to predict the disease severity and outcome. Results The receiver operating characteristic (ROC) area under the curve (AUC) of the machine learning classifier using PSI-related features was 0.85 (accuracy = 87.40%, p < 0.001) for predicting severity while that using CT-based features was only 0.70 (accuracy = 76.47%, p = 0.014). If combined, the AUC was not increased, showing 0.84 (accuracy = 84.03%, p < 0.001). Trained on outcome prediction, the classifier reached the AUC of 0.85 using PSI-related features (accuracy = 85.29%, p < 0.001), which was higher than using CT-based features (AUC = 0.67, accuracy = 75.21%, p < 0.001). If combined, the integrated model showed a slightly higher AUC of 0.86 (accuracy = 86.13%, p < 0.001). Oxygen saturation, IL-6, and CT infiltration showed great importance in both predicting severity and outcome. Conclusion Our study provided a comprehensive analysis and comparison between baseline chest CT and clinical assessment in disease severity and outcome prediction in Omicron pneumonia. The predictive model accurately predicts the severity and outcome of Omicron infection. Oxygen saturation, IL-6, and infiltration in chest CT were found to be important biomarkers. This approach has the potential to provide frontline physicians with an objective tool to manage Omicron patients more effectively in time-sensitive, stressful, and potentially resource-constrained environments.
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Affiliation(s)
- Jingjing Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chunqin Miao
- Party and Hospital Administration Office, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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5
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Musheyev B, Boparai MS, Kimura R, Janowicz R, Pamlanye S, Hou W, Duong TQ. Longitudinal medical subspecialty follow-up of critically and non-critically ill hospitalized COVID-19 survivors up to 24 months after discharge. Intern Emerg Med 2023; 18:477-486. [PMID: 36719540 PMCID: PMC9887251 DOI: 10.1007/s11739-023-03195-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 01/03/2023] [Indexed: 02/01/2023]
Abstract
Medical specialty usage of COVID-19 survivors after hospital discharge is poorly understood. This study investigated medical specialty usage at 1-12 and 13-24 months post-hospital discharge in critically ill and non-critically ill COVID-19 survivors. This retrospective study followed ICU (N = 89) and non-ICU (N = 205) COVID-19 survivors who returned for follow-up within the Stony Brook Health System post-hospital discharge. Follow-up data including survival, hospital readmission, ongoing symptoms, medical specialty care use, and ICU status were examined 1-12 and 13-24 months after COVID-19 discharge. "New" (not previously seen) medical specialty usage was also identified. Essentially all (98%) patients survived. Hospital readmission was 34%, but functional status scores at discharge were not associated with hospital readmission. Many patients reported ongoing [neuromuscular (50%) respiratory (39%), chronic fatigue (35%), cardiovascular (30%), gastrointestinal (28%), neurocognitive (22%), genitourinary (22%), and mood-related (13%)] symptoms at least once 1-24 months after discharge. Common specialty follow-ups included cardiology (25%), vascular medicine (17%), urology (17%), neurology (16%), and pulmonology (14%), with some associated with pre-existing comorbidities and with COVID-19. Common new specialty visits were vascular medicine (11%), pulmonology (11%), and neurology (9%). ICU patients had more symptoms and follow-ups compared to the non-ICU patients. This study reported high incidence of persistent symptoms and medical specialty care needs in hospitalized COVID-19 survivors 1-24 months post-discharge. Some specialty care needs were COVID-19 related or exacerbated by COVID-19 disease while others were associated with pre-existing medical conditions. Longer follow-up studies of COVID-19 survivor medical care needs are necessary.
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Affiliation(s)
- Benjamin Musheyev
- Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA
- Renaissance School of Medicine at Stony, Brook University, Stony Brook, New York, USA
| | - Montek S Boparai
- Renaissance School of Medicine at Stony, Brook University, Stony Brook, New York, USA
| | - Reona Kimura
- Renaissance School of Medicine at Stony, Brook University, Stony Brook, New York, USA
| | - Rebeca Janowicz
- Department of Physical and Occupational Therapy, Renaissance School of Medicine at Stony Brook Medicine, Stony Brook, New York, USA
| | - Stacey Pamlanye
- Department of Physical and Occupational Therapy, Renaissance School of Medicine at Stony Brook Medicine, Stony Brook, New York, USA
| | - Wei Hou
- Department of Family, Population and Preventative Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York, USA
| | - Tim Q Duong
- Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY, 10467, USA.
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6
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Tredinnick-Rowe J, Symonds R. Rapid systematic review of respiratory rate as a vital sign of clinical deterioration in COVID-19. Expert Rev Respir Med 2022; 16:1227-1236. [PMID: 36644851 DOI: 10.1080/17476348.2023.2169138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 01/12/2023] [Indexed: 01/17/2023]
Abstract
OBJECTIVES This meta-analysis aimed to establish a clinical evidence base for respiratory rate (RR) as a single predictor of early-onset COVID-19. The review also looked to determine the practical implementation of mobile respiratory rate measuring devices where information was available. METHODS We focused on domestic settings with older adults. Relevant studies were identified through MEDLINE, Embase, and CENTRAL databases. A snowballing method was also used. Articles published from the beginning of the COVID-19 pandemic (2019) until Feb 2022 were selected. Databases were searched for terms related to COVID-19 and respiratory rate measurements in domestic patients. RESULTS A total of 2,889 articles were screened for relevant content, of which 60 full-text publications were included. We compared the Odds Ratios and statistically significant results of both vital signs. CONCLUSION Multinational studies across dozens of countries have shown respiratory rate to have predictive accuracy in detecting COVID-19 deterioration. However, considerable variability is present in the data, making it harder to be sure about the effects. There is no meaningful difference in data quality in terms of variability (95% CI intervals) between vital signs as predictors of decline in COVID-19 patients. Contextual and economic factors will likely determine the choice of measurement used.
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7
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Duanmu H, Ren T, Li H, Mehta N, Singer AJ, Levsky JM, Lipton ML, Duong TQ. Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients. Biomed Eng Online 2022; 21:77. [PMID: 36242040 PMCID: PMC9568988 DOI: 10.1186/s12938-022-01045-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/16/2022] [Indexed: 11/10/2022] Open
Abstract
Objectives To use deep learning of serial portable chest X-ray (pCXR) and clinical variables to predict mortality and duration on invasive mechanical ventilation (IMV) for Coronavirus disease 2019 (COVID-19) patients. Methods This is a retrospective study. Serial pCXR and serial clinical variables were analyzed for data from day 1, day 5, day 1–3, day 3–5, or day 1–5 on IMV (110 IMV survivors and 76 IMV non-survivors). The outcome variables were duration on IMV and mortality. With fivefold cross-validation, the performance of the proposed deep learning system was evaluated by receiver operating characteristic (ROC) analysis and correlation analysis. Results Predictive models using 5-consecutive-day data outperformed those using 3-consecutive-day and 1-day data. Prediction using data closer to the outcome was generally better (i.e., day 5 data performed better than day 1 data, and day 3–5 data performed better than day 1–3 data). Prediction performance was generally better for the combined pCXR and non-imaging clinical data than either alone. The combined pCXR and non-imaging data of 5 consecutive days predicted mortality with an accuracy of 85 ± 3.5% (95% confidence interval (CI)) and an area under the curve (AUC) of 0.87 ± 0.05 (95% CI) and predicted the duration needed to be on IMV to within 2.56 ± 0.21 (95% CI) days on the validation dataset. Conclusions Deep learning of longitudinal pCXR and clinical data have the potential to accurately predict mortality and duration on IMV in COVID-19 patients. Longitudinal pCXR could have prognostic value if these findings can be validated in a large, multi-institutional cohort.
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Affiliation(s)
- Hongyi Duanmu
- Department of Radiology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Thomas Ren
- Department of Radiology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Haifang Li
- Department of Radiology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Neil Mehta
- Department of Radiology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Adam J Singer
- Department of Emergency Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Jeffrey M Levsky
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Michael L Lipton
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Tim Q Duong
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
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8
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Rice T. Children Who Lose a Parent in the COVID-19 Era: Considerations on Grief and Mourning. PSYCHOANALYTIC STUDY OF THE CHILD 2022. [DOI: 10.1080/00797308.2022.2120336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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9
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Oskarsdottir T, Sigurdsson MI, Palsson R, Eythorsson E. Longitudinal changes in inflammatory biomarkers among patients with COVID-19: a nationwide study in Iceland. Acta Anaesthesiol Scand 2022; 66:969-977. [PMID: 35748857 PMCID: PMC9350372 DOI: 10.1111/aas.14109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/08/2022] [Accepted: 05/19/2022] [Indexed: 12/15/2022]
Abstract
OBJECTIVES All SARS-CoV-2-positive persons in Iceland were prospectively monitored and those who required outpatient evaluation or were admitted to hospital underwent protocolized evaluation that included a standardized panel of biomarkers. The aim was to describe longitudinal changes in inflammatory biomarkers throughout the infection period of patients with COVID-19 requiring different levels of care. DESIGN Registry-based study SETTING: Nationwide study in Iceland PATIENTS: All individuals who tested positive for SARS-CoV-2 by real-time polymerase chain reaction (RT-PCR) from 28 February to 31 December 2020 in Iceland and had undergone blood tests between five days prior to and 21 days following onset of symptoms. MEASUREMENTS AND MAIN RESULTS Data were collected from the electronic medical record system of Landspitali-The National University Hospital of Iceland. Data analyses were descriptive and the evolution of biomarkers was visualized using Locally Weighted Scatterplot Smoothing (LOWESS) curves stratified by the worst clinical outcome experienced by the patient: outpatient evaluation only, hospitalization, and either intensive care unit (ICU) admission or death. Of 571 included patients, 310 (54.3%) only required outpatient evaluation or treatment, 202 (35.4%) were hospitalized and 59 (10.3%) were either admitted to the ICU or died. An early and persistent separation of the mean lymphocyte count, plasma C-reactive protein (CRP) and ferritin levels was observed between the three outcome groups, which occurred prior to hospitalization for those who later were admitted to ICU or died. Lower lymphocyte count, and higher CRP and ferritin levels correlated with worse clinical outcomes. Patients who were either admitted to the ICU or died had sustained higher white blood cell (WBC) and neutrophil counts, and elevated plasma levels of PCT and D-dimer compared with the other groups. CONCLUSIONS Lymphocyte count and plasma CRP and ferritin levels might be suitable parameters to assess disease severity early during COVID-19 and may serve as predictors of worse outcome.
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Affiliation(s)
- Thora Oskarsdottir
- Landspitali-The National University Hospital of Iceland, Hringbraut, 101 Reykjavík, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavík, Iceland
| | - Martin I Sigurdsson
- Landspitali-The National University Hospital of Iceland, Hringbraut, 101 Reykjavík, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavík, Iceland
| | - Runolfur Palsson
- Landspitali-The National University Hospital of Iceland, Hringbraut, 101 Reykjavík, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavík, Iceland
| | - Elias Eythorsson
- Landspitali-The National University Hospital of Iceland, Hringbraut, 101 Reykjavík, Iceland.,Faculty of Medicine, School of Health Sciences, University of Iceland, Reykjavík, Iceland
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10
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Lu JY, Buczek A, Fleysher R, Hoogenboom WS, Hou W, Rodriguez CJ, Fisher MC, Duong TQ. Outcomes of Hospitalized Patients With COVID-19 With Acute Kidney Injury and Acute Cardiac Injury. Front Cardiovasc Med 2022; 8:798897. [PMID: 35242818 PMCID: PMC8886161 DOI: 10.3389/fcvm.2021.798897] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 12/27/2021] [Indexed: 01/09/2023] Open
Abstract
Purpose This study investigated the incidence, disease course, risk factors, and mortality in COVID-19 patients who developed both acute kidney injury (AKI) and acute cardiac injury (ACI), and compared to those with AKI only, ACI only, and no injury (NI). Methods This retrospective study consisted of hospitalized COVID-19 patients at Montefiore Health System in Bronx, New York between March 11, 2020 and January 29, 2021. Demographics, comorbidities, vitals, and laboratory tests were collected during hospitalization. Predictive models were used to predict AKI, ACI, and AKI-ACI onset. Longitudinal laboratory tests were analyzed with time-lock to discharge alive or death. Results Of the 5,896 hospitalized COVID-19 patients, 44, 19, 9, and 28% had NI, AKI, ACI, and AKI-ACI, respectively. Most ACI presented very early (within a day or two) during hospitalization in contrast to AKI (p < 0.05). Patients with combined AKI-ACI were significantly older, more often men and had more comorbidities, and higher levels of cardiac, kidney, liver, inflammatory, and immunological markers compared to those of the AKI, ACI, and NI groups. The adjusted hospital-mortality odds ratios were 17.1 [95% CI = 13.6–21.7, p < 0.001], 7.2 [95% CI = 5.4–9.6, p < 0.001], and 4.7 [95% CI = 3.7–6.1, p < 0.001] for AKI-ACI, ACI, and AKI, respectively, relative to NI. A predictive model of AKI-ACI onset using top predictors yielded 97% accuracy. Longitudinal laboratory data predicted mortality of AKI-ACI patients up to 5 days prior to outcome, with an area-under-the-curve, ranging from 0.68 to 0.89. Conclusions COVID-19 patients with AKI-ACI had markedly worse outcomes compared to those only AKI, ACI and NI. Common laboratory variables accurately predicted AKI-ACI. The ability to identify patients at risk for AKI-ACI could lead to earlier intervention and improvement in clinical outcomes.
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Affiliation(s)
- Justin Y Lu
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Alexandra Buczek
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Roman Fleysher
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Wouter S Hoogenboom
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Wei Hou
- Department of Family, Population and Preventive Medicine, Stony Brook Medicine, New York, NY, United States
| | - Carlos J Rodriguez
- Cardiology Division, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Molly C Fisher
- Nephrology Division, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Tim Q Duong
- Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States
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11
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Lu JY, Hou W, Duong TQ. Longitudinal prediction of hospital-acquired acute kidney injury in COVID-19: a two-center study. Infection 2022; 50:109-119. [PMID: 34176087 PMCID: PMC8235913 DOI: 10.1007/s15010-021-01646-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 06/20/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND To investigate the temporal characteristics of clinical variables of hospital-acquired acute kidney injury (AKI) in COVID-19 patients and to longitudinally predict AKI onset. METHODS There were 308 hospital-acquired AKI and 721 non-AKI (NAKI) COVID-19 patients from Stony Brook Hospital (New York, USA) data, and 72 hospital-acquired AKI and 303 NAKI COVID-19 patients from Tongji Hospital (Wuhan, China). Demographic, comorbidities, and longitudinal (3 days before and 3 days after AKI onset) clinical variables were used to compute odds ratios for and longitudinally predict hospital-acquired AKI onset. RESULTS COVID-19 patients with AKI were more likely to die than NAKI patients (31.5% vs 6.9%, adjusted p < 0.001, OR = 4.67 [95% CI 3.1, 7.0], Stony Brook data). AKI developed on average 3.3 days after hospitalization. Procalcitonin was elevated prior to AKI onset (p < 0.05), peaked, and remained elevated (p < 0.05). Alanine aminotransferase, aspartate transaminase, ferritin, and lactate dehydrogenase peaked the same time as creatinine, whereas D-dimer and brain natriuretic peptide peaked a day later. C-reactive protein, white blood cell and lymphocyte showed group differences - 2 days prior (p < 0.05). Top predictors were creatinine, procalcitonin, white blood cells, lactate dehydrogenase, and lymphocytes. They predicted AKI onset with areas under curves (AUCs) of 0.78, 0.66, and 0.56 at 0, - 1, and - 2 days prior, respectively. When tested on the Tongji Hospital data, the AUCs were 0.80, 0.79, and 0.77, respectively. CONCLUSIONS Time-locked longitudinal data provide insight into AKI progression. Commonly clinical variables reasonably predict AKI onset a few days prior. This work may lead to earlier recognition of AKI and treatment to improve clinical outcomes.
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Affiliation(s)
- Justin Y. Lu
- grid.251993.50000000121791997Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY 10467 USA
| | - Wei Hou
- grid.459987.e0000 0004 6008 5093Department of Family, Population & Preventive Medicine, Stony Brook Medicine, 101 Nicolls Rd, Stony Brook, NY USA
| | - Tim Q. Duong
- grid.251993.50000000121791997Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E 210th St, Bronx, NY 10467 USA
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12
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Lasso G, Khan S, Allen SA, Mariano M, Florez C, Orner EP, Quiroz JA, Quevedo G, Massimi A, Hegde A, Wirchnianski AS, Bortz RH, Malonis RJ, Georgiev GI, Tong K, Herrera NG, Morano NC, Garforth SJ, Malaviya A, Khokhar A, Laudermilch E, Dieterle ME, Fels JM, Haslwanter D, Jangra RK, Barnhill J, Almo SC, Chandran K, Lai JR, Kelly L, Daily JP, Vergnolle O. Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes. PLoS Comput Biol 2022; 18:e1009778. [PMID: 35041647 PMCID: PMC8812869 DOI: 10.1371/journal.pcbi.1009778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 02/03/2022] [Accepted: 12/20/2021] [Indexed: 02/07/2023] Open
Abstract
The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.
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Affiliation(s)
- Gorka Lasso
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Saad Khan
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Stephanie A. Allen
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Margarette Mariano
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Catalina Florez
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Chemistry and Life Science, United States Military Academy at West Point, West Point, New York, United States of America
| | - Erika P. Orner
- Department of Pathology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Jose A. Quiroz
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Gregory Quevedo
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Aldo Massimi
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Aditi Hegde
- Eastchester High School, 2 Stewart Place, Eastchester, New York, United States of America
| | - Ariel S. Wirchnianski
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Robert H. Bortz
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Ryan J. Malonis
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - George I. Georgiev
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Karen Tong
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Natalia G. Herrera
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Nicholas C. Morano
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Scott J. Garforth
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Avinash Malaviya
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Ahmed Khokhar
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Ethan Laudermilch
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - M. Eugenia Dieterle
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - J. Maximilian Fels
- Department of Cell Biology, Harvard Medical School, Boston, Cambridge, Massachusetts, United States of America
- Department of Microbiology, Harvard Medical School, Boston, Cambridge, Massachusetts, United States of America
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, Cambridge, Massachusetts, United States of America
| | - Denise Haslwanter
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Rohit K. Jangra
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Jason Barnhill
- Department of Chemistry and Life Science, United States Military Academy at West Point, West Point, New York, United States of America
- Department of Radiology and Radiological Services, Uniformed Services University of the Health Sciences, Bethesda, Maryland, United States of America
| | - Steven C. Almo
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Kartik Chandran
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Jonathan R. Lai
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Libusha Kelly
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Johanna P. Daily
- Department of Microbiology and Immunology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Division of Infectious Diseases, Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, United States of America
| | - Olivia Vergnolle
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, New York, United States of America
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