scholarly journals Predictors of intubation and mortality in COVID-19 patients: a retrospective study

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Tiziana Cena ◽  
Gianmaria Cammarota ◽  
Danila Azzolina ◽  
Michela Barini ◽  
Simona Bazzano ◽  
...  
Keyword(s):  
Lactate Dehydrogenase ◽  
Hospital Mortality ◽  
Invasive Mechanical Ventilation ◽  
Ground Glass Opacity ◽  
Lactate Dehydrogenase Level ◽  
University Hospital ◽  
Arterial Oxygen ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Hypoxemic Acute Respiratory Failure

Abstract Background Estimating the risk of intubation and mortality among COVID-19 patients can help clinicians triage these patients and allocate resources more efficiently. Thus, here we sought to identify the risk factors associated with intubation and intra-hospital mortality in a cohort of COVID-19 patients hospitalized due to hypoxemic acute respiratory failure (ARF). Results We included retrospectively a total of 187 patients admitted to the subintensive and intensive care units of the University Hospital “Maggiore della Carità” of Novara between March 1st and April 30th, 2020. Based on these patients’ demographic characteristics, early clinical and laboratory variables, and quantitative chest computerized tomography (CT) findings, we developed two random forest (RF) models able to predict intubation and intra-hospital mortality. Variables independently associated with intubation were C-reactive protein (p < 0.001), lactate dehydrogenase level (p = 0.018) and white blood cell count (p = 0.026), while variables independently associated with mortality were age (p < 0.001), other cardiovascular diseases (p = 0.029), C-reactive protein (p = 0.002), lactate dehydrogenase level (p = 0.018), and invasive mechanical ventilation (p = 0.001). On quantitative chest CT analysis, ground glass opacity, consolidation, and fibrosis resulted significantly associated with patient intubation and mortality. The major predictors for both models were the ratio between partial pressure of arterial oxygen and fraction of inspired oxygen, age, lactate dehydrogenase, C-reactive protein, glycemia, CT quantitative parameters, lymphocyte count, and symptom onset. Conclusions Altogether, our findings confirm previously reported demographic, clinical, hemato-chemical, and radiologic predictors of adverse outcome among COVID-19-associated hypoxemic ARF patients. The two newly developed RF models herein described show an overall good level of accuracy in predicting intra-hospital mortality and intubation in our study population. Thus, their future development and implementation may help not only identify patients at higher risk of deterioration more effectively but also rebalance the disproportion between resources and demand.

scholarly journals Comparative Survival Analysis of Immunomodulatory Therapy for COVID-19 ‘Cytokine Storm’: A Retrospective Observational Cohort Study

2020 ◽  
Author(s):  
Sonali Narain ◽  
Dimitre G. Stefanov ◽  
Alice S. Chau ◽  
Andrew G. Weber ◽  
Galina Marder ◽  
...  
Keyword(s):  
Lactate Dehydrogenase ◽  
Hospital Mortality ◽  
Standard Of Care ◽  
P Value ◽  
Cytokine Storm ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Treatment Standard ◽  
Observational Cohort ◽  
Retrospective Observational Cohort Study

AbstractBackgroundCytokine storm is a marker of COVID-19 illness severity and increased mortality. Immunomodulatory treatments have been repurposed to improve mortality outcomes.MethodsWe conducted a retrospective analysis of electronic health records across the Northwell Health system. COVID-19 patients hospitalized between March 1, 2020 and April 15, 2020, were included. Cytokine storm was defined by inflammatory markers: ferritin >700ng/mL, C-reactive protein >30mg/dL, or lactate dehydrogenase >300U/L. Patients were subdivided into six groups -no immunomodulatory treatment (standard of care) and five groups that received either corticosteroids, anti-interleukin 6 (IL-6) antibody (tocilizumab) or anti-IL-1 therapy (anakinra) alone or in combination with corticosteroids. The primary outcome was hospital mortality.ResultsThere were 3,098 patients who met inclusion criteria. The most common comorbidities were hypertension (40-56%), diabetes (32-43%) and cardiovascular disease (2-15%). Patients most frequently met criteria with high lactate dehydrogenase (74.8%) alone, or in combination, followed by ferritin (71.4%) and C-reactive protein (9.4%). More than 80% of patients had an elevated D-dimer. Patients treated with a combination of tocilizumab and corticosteroids (Hazard Ratio [HR]: 0.459, 95% Confidence Interval [CI]: 0.295-0.714; p<0.0001) or corticosteroids alone (HR: 0.696, 95% CI: 0.512-0.946; p=0.01) had improved hospital survival compared to standard of care. Corticosteroids and tocilizumab was associated with increased survival when compared to corticosteroids and anakinra (HR: 0.612, 95% CI: 0.391-0.958; p-value=0.02).ConclusionsWhen compared to standard of care, corticosteroid and tocilizumab used in combination, or corticosteroids alone, was associated with reduced hospital mortality for patients with COVID-19 cytokine storm.

scholarly journals Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10337 ◽  
Author(s):  
Xiaoran Li ◽  
Peilin Ge ◽  
Jocelyn Zhu ◽  
Haifang Li ◽  
James Graham ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lactate Dehydrogenase ◽  
Hospital Mortality ◽  
Risk Score ◽  
C Reactive Protein ◽  
Score System ◽  
Icu Admission ◽  
Reactive Protein ◽  
Risk Score System ◽  
Deep Learning Model

Background This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. Methods This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC). Results The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760–0.785]) and 0.844 (95% CI [0.839–0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726–0.729]) and 0.848 (95% CI [0.847–0.849]), respectively. Conclusions Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.

Experience of using heliox in the treatment of viral pneumonia in COVID-19

MedAlliance ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 8-14
Keyword(s):  
Lactate Dehydrogenase ◽  
Viral Pneumonia ◽  
P Value ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Number Of Patients ◽  
Helium Mixture ◽  
Ct Data ◽  
Group 2 ◽  
Group 1

SummaryIntroduction. With the ongoing COVID-19 pandemic, finding new treatments is an extremely important issue. The effectiveness of heliox was previously demonstrated in the complex treatment of patients with various bron-chopulmonary pathologies. Therefore, this method has been recommended for the treatment of pneumonia associated with COVID-19. Purpose. To study the safety and efficacy of inhaled heliox therapy in the treatment of pneumonia in COVID-19. Materials and methods. A sing-le-center prospective study was carried out for the period from 01.12.2020 to 15.02.2021. The study included 91 pa-tients. The patients were divided into two groups: group 1 (using heliox) included 46 people, and group 2 (con-trol) — 45. Inhalations of a heated oxygen-helium mixture heliox (70% helium, 30% oxygen) were carried out using “Ingalit-B2-01” inhaler. Objective (saturation, O2 flow) and laboratory parameters (lactate dehydrogenase, C-reactive protein), as well as chest organs CT data were studied. Differences between groups were determined using the χ2 test, as well as the Mann–Whitney U-test. The p value <0.05 was considered significant. Results. In group 1, side effects developed in 5 (11.3%) patients. These patients refused to further participate in the study. Final number of patients in group 1 — 41. Among patients of group 1, there was a tendency towards a more rapid normalization of lactate dehydrogenase and C-reactive protein, as well as a decrease in oxygen dependence. In group 1, according to CT data, no progression of pneumonia was recorded. In group 2, progression was observed in 6 (13.3%) patients. The overall effectiveness of treatment among patients in group 1 was 100%, among patients in group 2 — 86.7%. The differences between the groups are statistically sig-nificant (p=0.02). Conclusion. The use of inhalations with a heated oxygen-helium mixture heliox (30% oxygen, 70% helium) has shown its effectiveness and safety in the treatment of viral pneumonia (CT1- 2) associated with COVID-19.

scholarly journals Novel phenotypes of coronavirus disease: a temperature-based trajectory model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yanfei Shen ◽  
Dechang Chen ◽  
Xinmei Huang ◽  
Guolong Cai ◽  
Qianghong Xu ◽  
...  
Keyword(s):  
Hospital Mortality ◽  
Clinical Outcomes ◽  
Odds Ratio ◽  
Gamma Globulin ◽  
Inflammatory Responses ◽  
Potential Temperature ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Fever Onset ◽  
Tertiary Hospitals

Abstract Background Coronavirus disease has heterogeneous clinical features; however, the reasons for the heterogeneity are poorly understood. This study aimed to identify clinical phenotypes according to patients’ temperature trajectory. Method A retrospective review was conducted in five tertiary hospitals in Hubei Province from November 2019 to March 2020. We explored potential temperature-based trajectory phenotypes and assessed patients’ clinical outcomes, inflammatory response, and response to immunotherapy according to phenotypes. Results A total of 1580 patients were included. Four temperature-based trajectory phenotypes were identified: normothermic (Phenotype 1); fever, rapid defervescence (Phenotype 2); gradual fever onset (Phenotype 3); and fever, slow defervescence (Phenotype 4). Compared with Phenotypes 1 and 2, Phenotypes 3 and 4 had a significantly higher C-reactive protein level and neutrophil count and a significantly lower lymphocyte count. After adjusting for confounders, Phenotypes 3 and 4 had higher in-hospital mortality (adjusted odds ratio and 95% confidence interval 2.1, 1.1–4.0; and 3.3, 1.4–8.2, respectively), while Phenotype 2 had similar mortality, compared with Phenotype 1. Corticosteroid use was associated with significantly higher in-hospital mortality in Phenotypes 1 and 2, but not in Phenotypes 3 or 4 (p for interaction < 0.01). A similar trend was observed for gamma-globulin. Conclusions Patients with different temperature-trajectory phenotypes had different inflammatory responses, clinical outcomes, and responses to corticosteroid therapy.

scholarly journals COVID-19 in pediatrics: case report with many complications and a good clinical outcome

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Arianne Gaspar ◽  
Paulo João ◽  
Gabriela Kuzma ◽  
Idilla Floriani ◽  
Luana Amancio
Keyword(s):  
Clinical Outcome ◽  
Recurrent Urinary Tract Infection ◽  
Invasive Mechanical Ventilation ◽  
Pediatric Intensive Care ◽  
Older Individuals ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Global Pandemic ◽  
Previous Diagnosis ◽  
Acute Infectious Disease

Coronavirus disease 2019 (COVID-19) is an acute infectious disease that caused the emergence of the new serious global pandemic. The infection in children is much less prevalent than in adults and most cases are asymptomatic or have mild symptoms. Severe cases represent less than 1% of the total, therefore information about the disease in this age group is scarce compared to data in older individuals. We exposed a case of a 16-year-old male adolescent with a previous diagnosis of myelomeningocele, hydrocephalus with peritoneal ventricle bypass (PVB), recurrent urinary tract infection, epilepsy, and obesity. The patient presented cough and convulsive crises, which worsened during hospitalization with severe acute respiratory syndrome due to SARS-CoV-2, septic shock, and cardiorespiratory arrest and invasive mechanical ventilation (IMV) for 9 days was required. Also presented several other complications and factors of critical prognosis, such as elevated inflammatory markers (C-reactive protein, D-dimer), elevated cardiac troponin, and the necessity of renal replacement therapy. Nevertheless, the clinical outcome was satisfactory and he was discharged after a 40-day stay in the Pediatric Intensive Care Unit.

scholarly journals Lactate dehydrogenase level as an early predictor of transient tachypnea of the newborn duration and complications

2020 ◽  
Author(s):  
Mada Osefori ◽  
Leen Jamel Doya ◽  
Bana Nezha ◽  
Adnan Dayoub
Keyword(s):  
Intensive Care ◽  
Lactate Dehydrogenase ◽  
Neonatal Intensive Care ◽  
Lactate Dehydrogenase Level ◽  
Cross Sectional Study ◽  
Postnatal Period ◽  
University Hospital ◽  
Cross Sectional ◽  
Duration Of Hospitalization ◽  
The Relationship

Abstract Background: Transient tachypnea of the newborn(TTNB) is a common cause of respiratory distress in the postnatal period. It is rarely associated with serious complications that need intensive care. Prediction of the complications during the first hours of hospitalization is very difficult, so the purpose of the current study is to investigate the relationship between lactate dehydrogenase (LDH) level in blood and the course of Transient tachypnea of the newborn (the duration of hospitalization, and the incidence of complications).Material and methods: In a cross-sectional study design included 120 neonates with Transient tachypnea of the newborn who had referred to the Neonatal Intensive Care Unit (NICU) at Tishreen University Hospital over 1 year period from January 2018 to January 2019. The neonates were classified according to Lactate dehydrogenase measurement as normal or high lactate dehydrogenase level in blood.Results: The results showed that there was a significant relationship between the level of lactate dehydrogenase and the duration of hospitalization, the incidence of complications, and the frequency of complications.Conclusions: lactate dehydrogenase might be useful for clinicians to predict the duration of hospitalization and the incidence of complications in neonates with TTNB.

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