Fever in the ICU: A Predictor of Mortality in Mechanically Ventilated COVID-19 Patients

Prevalence of fever and its associated risk factors among patients hospitalised with coronavirus disease 2019 (COVID-19) at the Eastern Regional Hospital, Koforidua, Ghana

BACKGROUND

In Ghana, temperature check at various points of entry was adopted as a means of screening people for coronavirus disease 2019 without taking into consideration data on the local prevalence of fever associated with the disease. Our objective was to assess fever prevalence and its associated risk factors among patients hospitalised with coronavirus disease 2019 at the Eastern Regional Hospital, Koforidua in Ghana.

METHODS

We reviewed medical records of 301 coronavirus disease 2019 patients who were admitted at the Eastern Regional Hospital, Koforidua between May 5, 2020, and August 31, 2021. Data collected on a pre-designed extraction sheet was processed, entered and analysed using Microsoft excel 2019 and Stata/IC version 16.1 software. Prevalence of fever was estimated and a multivariable logistic regression model was fitted to establish risk factors associated with fever among hospitalised coronavirus disease 2019 patients. A relationship was accepted to be significant at 5% level of significance.

RESULTS

The prevalence of fever among hospitalised coronavirus disease 2019 patients was 21.6% (95% CI, 17.1%-26.7%). Risk factors associated with fever were age group [0-19 years (AOR, 5.75; 95% CI, 1.46-22.68; p = 0.013); 20-39 years (AOR, 3.22; 95% CI, 1.42-7.29; p = 0.005)], comorbidity (AOR, 2.18; 95% CI, 1.04-4.59; p = 0.040), and disease severity [moderate (AOR, 3.89; 95% CI, 1.44-10.49; p = 0.007); severe (AOR, 4.08; 95% CI, 1.36-12.21; p = 0.012); critical (AOR, 4.85; 95% CI, 1.03-22.85; p = 0.046)].

CONCLUSIONS

The prevalence of fever was low among hospitalised coronavirus disease 2019 patients at the Eastern Regional Hospital, Koforidua. However, there was an increasing risk of fever as the disease severity progresses. Fever screening may be utilised better in disease of higher severity; it should not be used alone especially in mild disease.

Wireless, battery-free, multifunctional integrated bioelectronics for respiratory pathogens monitoring and severity evaluation

The rapid diagnosis of respiratory virus infection through breath and blow remains challenging. Here we develop a wireless, battery-free, multifunctional pathogenic infection diagnosis system (PIDS) for diagnosing SARS-CoV-2 infection and symptom severity by blow and breath within 110 s and 350 s, respectively. The accuracies reach to 100% and 92% for evaluating the infection and symptom severity of 42 participants, respectively. PIDS realizes simultaneous gaseous sample collection, biomarker identification, abnormal physical signs recording and machine learning analysis. We transform PIDS into other miniaturized wearable or portable electronic platforms that may widen the diagnostic modes at home, outdoors and public places. Collectively, we demonstrate a general-purpose technology for rapidly diagnosing respiratory pathogenic infection by breath and blow, alleviating the technical bottleneck of saliva and nasopharyngeal secretions. PIDS may serve as a complementary diagnostic tool for other point-of-care techniques and guide the symptomatic treatment of viral infections.

Global prevalence of COVID-19-induced acute respiratory distress syndrome: systematic review and meta-analysis

BACKGROUND

Acute respiratory distress syndrome (ARDS) is potentially a fatal form of respiratory failure among COVID-19 patients. Globally, there are inconsistent findings regarding ARDS among COVID-19 patients. Therefore, this study aimed to estimate the pooled prevalence of COVID-19-induced ARDS among COVID-19 patients worldwide.

METHODS

To retrieve relevant studies, the authors searched Embase, MEDLINE, PubMed, Web of Science, Cochrane Library, Google, and Google Scholar using a combination of search terms. The search was conducted for articles published from December 2019 to September 2022. Articles were searched and screened by title (ti), abstract (ab), and full-text (ft) by two reviewers independently. The quality of each included article was assessed using the Newcastle-Ottawa Assessment Scale. Data were entered into Microsoft Word and exported to Stata version 14 for analysis. Heterogeneity was detected using the Cochrane Q statistics and I-square (I2). Then the sources of variations were identified by subgroup and meta-regression analysis. A random effect meta-analysis model was used. The publication bias was detected using the graphic asymmetry test of the funnel plot and/or Egger's test (p value < 0.05). To treat the potential publication bias, trim and fill analysis were computed. The protocol has been registered in an international database, the Prospective Register of Systematic Reviews (PROSPERO) with reference number: CRD42023438277.

RESULTS

A total of 794 studies worldwide were screened for their eligibility. Of these 11 studies with 2845 participants were included in this systematic review and meta-analysis. The overall pooled prevalence of COVID-19-induced ARDS in the world was found to be 32.2% (95%CI = 27.70-41.73%), I2 = 97.3%, and p value < 0.001).

CONCLUSION

The pooled prevalence of COVID-19-induced ARDS was found to be high. The virus remains a global burden because its genetic causes are constantly changing or it mutated throughout the pandemic to emerge a new strain of infection. Therefore, interventions such as massive vaccination, early case detection, screening, isolation, and treatment of the cases need to be implemented to tackle its severity.

Association between acute liver injury & severity and mortality of COVID-19 patients: A systematic review and meta-analysis

Background

Acute liver injury (ALI), a complication often seen in COVID-19 patients, can lead to severe liver damage, multi-organ failure, acute vascular events, and can potentially escalate to patient mortality. Given this, we initiated a meta-analysis to investigate the correlation between ALI and adverse outcomes in COVID-19 patients.

Methods

We conducted an exhaustive search of databases, including Medline, Embase, PubMed Central, ScienceDirect, Google Scholar, and the Cochrane Library, from the November 2019 until January 2022. The quality of the included studies was evaluated using the Newcastle Ottawa (NO) scale. Our meta-analysis was carried out using a random-effects model and results were presented as pooled odds ratios (ORs) with their corresponding 95% confidence intervals (CIs).

Results

Our analysis incorporated 20 studies involving a total of 13,850 participants, predominantly from China and the United States. According to the NO scale, the majority of these studies were categorized as low-quality. Patients with ALI faced approximately 7 times higher odds of severe COVID-19 symptoms (pooled OR = 7.09; 95%CI: 4.97 to 10.12) and over 5 times higher odds of mortality (pooled OR = 5.50; 95%CI: 3.37 to 8.99) when compared to those without ALI.

Conclusion

Our findings affirm that ALI is a potent predictor of adverse outcomes, including severity and mortality, among COVID-19 patients. Recognizing and promptly addressing ALI in COVID-19 patients could be pivotal in improving prognosis and tailoring individualized patient management strategies. This underscores the need for clinicians to be vigilant about liver complications in the COVID-19 patients and integrate appropriate interventions in the treatment paradigm.

Targeted Temperature Management After Cardiac Arrest in COVID-19 Patients

There is a paucity of evidence regarding the utility of targeted temperature management (TTM) in COVID-19 patients who suffer cardiac arrest. This systematic review and meta-analysis aimed to use the available data of how temperature predicts outcomes in COVID-19 patients and the association between active cooling and outcomes in non-COVID-19 cardiac arrest patients to give recommendations for the utility of TTM in COVID-19 survivors of cardiac arrest. The PubMed, Embase, and Web of Science databases were queried in August 2022 for two separate searches: (1) temperature as a predictor of clinical outcomes in COVID-19 and (2) active cooling after return of spontaneous circulation (ROSC) in non-COVID-19. Forest plots were generated to summarize the results. Of the 4209 abstracts screened, none assessed the target population of TTM in COVID-19 victims of cardiac arrest. One retrospective cohort study evaluated hyperthermia in critically ill COVID-19 patients, two retrospective cohort studies evaluated hypothermia in septic COVID-19 patients, and 20 randomized controlled trials evaluated active cooling in non-COVID-19 patients after ROSC. Risk of death was higher in COVID-19 patients who presented with hyperthermia (risk ratio [RR] = 1.87) or hypothermia (RR = 1.77; p < 0.001). In non-COVID-19 victims of cardiac arrest, there was no significant difference in mortality (RR = 0.94; p = 0.098) or favorable neurological outcome (RR = 1.05; p = 0.41) with active cooling after ROSC. Further studies are needed to evaluate TTM in COVID-19 victims of cardiac arrest. However, given the available evidence that hyperthermia or hypothermia in COVID-19 patients is associated with increased mortality as well as our findings suggesting limited utility for active cooling in non-COVID-19 cardiac arrest patients, we posit that TTM to normothermia (core body temperature ∼37°C) would most likely be optimal for the best outcomes in COVID-19 survivors of cardiac arrest.

Hyperpyrexia in a previously healthy pregnant female with COVID pneumonia: a case report and review of the literature

BACKGROUND

Infection due to the SARS-CoV-2 virus can have a wide range of presentations from asymptomatic/mildly symptomatic to severe disease with multiorgan failure. Fever is a common symptom. But hyperpyrexia defined as temperature > 41.5 °C is not usual in COVID-19.

CASE PRESENTATION

A 24-year-old previously well Sri Lankan female in the 24th week of gestation of her first pregnancy presented with fever and shortness of breath. She was confirmed to have coronavirus disease-2019 (COVID-19). History was suggestive of late presentation on approximately the eighth day of the illness. She had rapidly deteriorating hypoxia due to COVID pneumonia requiring mechanical ventilation two days after the admission. There was evidence of cytokine storm without any secondary bacterial infection. She received glucocorticoids, tocilizumab, and intravenous antibiotics. Although she initially showed mild improvements, she subsequently developed high-grade fever with the axillary temperature rising to 41.7 °C starting from the seventh day of admission. There were no causative medicines or risk factors to explain hyperpyrexia. She died on the ninth day of admission.

CONCLUSIONS

There are no reports of patients developing this complication during pregnancy. The pathophysiology of this rare life-threatening complication remains elusive. Detailed reporting and in-depth analysis of such patients will facilitate the understanding of the associations and successful management of these patients.

Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the wide variety of patient profiles, and the imbalances within health supply chains still represent a challenge for policymakers. This paper aims to use Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to support ICU bed capacity management during Covid-19. The proposed approach was validated in a Spanish hospital chain where we initially identified the predictors of ICU admission in Covid-19 patients. Second, we applied Random Forest (RF) to predict ICU admission likelihood using patient data collected in the Emergency Department (ED). Finally, we included the RF outcomes in a DES model to assist decision-makers in evaluating new ICU bed configurations responding to the patient transfer expected from downstream services. The results evidenced that the median bed waiting time declined between 32.42 and 48.03 min after intervention.

Temperature management using an intervascular cooling device for a COVID-19 patient with refractory hyperthermia

COVID-19 is a life-threatening disease complicated by hyperinflammation followed by multi-organ failure. Although refractory hyperthermia in COVID-19 contributes to an unfavorable prognosis, little is known about effective interventions. We present a case of successful temperature management using an intravascular cooling device in a patient with COVID-19 who developed refractory hyperthermia.

Prognostic factors for favorable outcomes after veno-venous extracorporeal membrane oxygenation in critical care patients with COVID-19

BACKGROUND

Patients with COVID-19 and severe acute respiratory failure may require veno-venous extracorporeal membrane oxygenation (VV ECMO). Yet, this procedure is resource-intensive and high mortality rates have been reported. Thus, predictors for identifying patients who will benefit from VV ECMO would be helpful.

METHODS

This retrospective study included 129 patients with COVID-19 and severe acute respiratory failure, who had received VV ECMO at the University Medical Center Regensburg, Germany, between 1 March 2020 and 31 December 2021. Patient-specific factors and relevant intensive-care parameters at the time of the decision to start VV ECMO were investigated regarding their value as predictors of patient survival. In addition, the intensive-care course of the first 10 days of VV ECMO was compared between survivors and patients who had died in the intensive care unit.

RESULTS

The most important parameters for predicting outcome were patient age and platelet count, which differed significantly between survivors and non-survivors (age: 52.6±8.1 vs. 57.4±10.1 years, p<0.001; platelet count before VV ECMO: 321.3±132.2 vs. 262.0±121.0 /nL, p = 0.006; average on day 10: 199.2±88.0 vs. 147.1±57.9 /nL, p = 0.002). A linear regression model derived from parameters collected before the start of VV ECMO only included age and platelet count. Patients were divided into two groups by using receiver operating characteristics (ROC) analysis: group 1: 78% of patients, mortality 26%; group 2: 22% of patients, mortality 75%. A second linear regression model included average blood pH, minimum paO2, and average pump flow on day 10 of VV ECMO in addition to age and platelet count. The ROC curve resulted in two cut-off values and thus in three groups: group 1: 25% of patients, mortality 93%; group 2: 45% of patients, mortality 31%; group 3: 30% of patients, mortality 0%.

Comparison between the first and second COVID-19 waves in Internal Medicine wards in Milan, Italy: a retrospective observational study

COVID-19 spread in two pandemic waves in Italy between 2020 and 2021. The aim of this study is to compare the first with the second COVID-19 wave, analyzing modifiable and non-modifiable factors and how these factors affected mortality in patients hospitalized in Internal Medicine wards. Consecutive patients with SARS-CoV-2 infection and dyspnea requiring O2 supplementation were included. The severity of lung involvement was categorized according to the patients' oxygen need. Six hundred and ten SARS-CoV-2 hospitalized patients satisfied the inclusion criteria. The overall estimated 4-week mortality was similar in the two pandemic waves. Several variables were associated with mortality after univariate analysis, but they lacked the significance after multivariable adjustment. Steroids did not exert any protective effect when analyzed in time-dependent models in the whole sample; however, steroids seemed to exert a protective effect in more severe patients. When analyzing the progression to different states of O2 supplementation during hospital stay, mortality was almost exclusively associated with the use of high-flow O2 or CPAP. The analysis of the transition from one state to the other by Cox-Markov models confirmed that age and the severity of lung involvement at admission, along with fever, were relevant factor for mortality or progression.

Respiratory Physiology of COVID-19 and Influenza Associated Acute Respiratory Distress Syndrome

Background: There is ongoing debate whether lung physiology of COVID-19-associated acute respiratory distress syndrome (ARDS) differs from ARDS of other origin. Objective: The aim of this study was to analyze and compare how critically ill patients with COVID-19 and Influenza A or B were ventilated in our tertiary care center with or without extracorporeal membrane oxygenation (ECMO). We ask if acute lung failure due to COVID-19 requires different intensive care management compared to conventional ARDS. Methods: 25 patients with COVID-19-associated ARDS were matched to a cohort of 25 Influenza patients treated in our center from 2011 to 2021. Subgroup analysis addressed whether patients on ECMO received different mechanical ventilation than patients without extracorporeal support. Results: Compared to Influenza-associated ARDS, COVID-19 patients had higher ventilatory system compliance (40.7 mL/mbar [31.8–46.7 mL/mbar] vs. 31.4 mL/mbar [13.7–42.8 mL/mbar], p = 0.198), higher ventilatory ratio (1.57 [1.31–1.84] vs. 0.91 [0.44–1.38], p = 0.006) and higher minute ventilation at the time of intubation (mean minute ventilation 10.7 l/min [7.2–12.2 l/min] for COVID-19 vs. 6.0 l/min [2.5–10.1 l/min] for Influenza, p = 0.013). There were no measurable differences in P/F ratio, positive end-expiratory pressure (PEEP) and driving pressures (ΔP). Respiratory system compliance deteriorated considerably in COVID-19 patients on ECMO during 2 weeks of mechanical ventilation (Crs, mean decrease over 2 weeks −23.87 mL/mbar ± 32.94 mL/mbar, p = 0.037) but not in ventilated Influenza patients on ECMO and less so in ventilated COVID-19 patients without ECMO. For COVID-19 patients, low driving pressures on ECMO were strongly correlated to a decline in compliance after 2 weeks (Pearson’s R 0.80, p = 0.058). Overall mortality was insignificantly lower for COVID-19 patients compared to Influenza patients (40% vs. 48%, p = 0.31). Outcome was insignificantly worse for patients requiring veno-venous ECMO in both groups (50% mortality for COVID-19 on ECMO vs. 27% without ECMO, p = 0.30/56% vs. 34% mortality for Influenza A/B with and without ECMO, p = 0.31). Conclusion: The pathophysiology of early COVID-19-associated ARDS differs from Influenza-associated acute lung failure by sustained respiratory mechanics during the early phase of ventilation. We question whether intubated COVID-19 patients on ECMO benefit from extremely low driving pressures, as this appears to accelerate derecruitment and consecutive loss of ventilatory system compliance.

Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study

Background: Owing to the shortage of ventilators, there is a crucial demand for an objective and accurate prognosis for 2019 coronavirus disease (COVID-19) critical patients, which may necessitate a mechanical ventilator (MV). This study aimed to construct a predictive model using machine learning (ML) algorithms for frontline clinicians to better triage endangered patients and priorities who would need MV.

Methods: In this retrospective single-center study, the data of 482 COVID-19 patients from February 9, 2020, to December 20, 2020, were analyzed by several ML algorithms including, multi-layer perception (MLP), logistic regression (LR), J-48 decision tree, and Naïve Bayes (NB). First, the most important clinical variables were identified using the Chi-square test at P < 0.01. Then, by comparing the ML algorithms' performance using some evaluation criteria, including TP-Rate, FP-Rate, precision, recall, F-Score, MCC, and Kappa, the best performing one was identified.

Results: Predictive models were trained using 15 validated features, including cough, contusion, oxygen therapy, dyspnea, loss of taste, rhinorrhea, blood pressure, absolute lymphocyte count, pleural fluid, activated partial thromboplastin time, blood glucose, white cell count, cardiac diseases, length of hospitalization, and other underline diseases. The results indicated the J-48 with F-score = 0.868 and AUC = 0.892 yielded the best performance for predicting intubation requirement.

Conclusion: ML algorithms are potentials to improve traditional clinical criteria to forecast the necessity for intubation in COVID-19 in-hospital patients. Such ML-based prediction models may help physicians with optimizing the timing of intubation, better sharing of MV resources and personnel, and increase patient clinical status.

Preexisting coronary artery disease among coronavirus disease 2019 patients: a systematic review and meta-analysis

AIMS

The prevalence and prognostic implications of coronary artery disease (CAD) in patients infected by the novel coronavirus 2019 (COVID-19) disease remain unclear.

METHODS

We conducted a systematic review and meta-analysis to investigate the prevalence and mortality risk in COVID-19 patients with preexisting CAD. We searched Medline and Scopus to locate all articles published up to December 8, 2021, reporting data of COVID-19 survivors and nonsurvivors with preexisting CAD. Data were pooled using the Mantel-Haenszel random effects models with odds ratio (OR) as the effect measure with the related 95% confidence interval (CI).

RESULTS

Thirty-eight studies including 27 435 patients (mean age 61.5 and 70.9 years) were analysed. The pooled prevalence of preexisting CAD was 12.6% (95% CI: 11.2-16.5%, I2 : 95.6%), and resulted as higher in intensive care unit patients (17.5%, 95% CI: 11.9-25.1, I2 : 88.4%) and in European cohorts (13.1%, 95% CI: 7.8-21.6%, P  < 0.001, I2 : 98.4%). COVID-19 patients with preexisting CAD had a two-fold risk of short-term mortality (OR 2.61, 95% CI 2.10-3.24, P  < 0.001, I2  = 73.6%); this risk was higher among Asian cohorts (OR: 2.66, 95% CI: 1.79-3.90, P  < 0.001, I2 : 77.3%) compared with European (OR: 2.44, 95% CI: 1.90-3.14, P  < 0.001, I2 : 56.9%) and American (OR: 1.86, 95% CI: 1.41-2.44, P  < 0.001, I2 : 0%) populations. The association between CAD and poor short-term prognosis was influenced by age, prevalence of hypertension (HT), DM and CKD.

CONCLUSIONS

Preexisting CAD is present in approximately 1 in 10 patients hospitalized for COVID-19 and significantly associated with an increased risk of short-term mortality, which is influenced by age, HT, DM and CKD.

Bicarbonate concentration as a predictor of prognosis in moderately severe COVID-19 patients: A multicenter retrospective study

Background

Coronavirus disease 2019 (COVID-19) patients reportedly have high bicarbonate concentration. However, its relationship to the disease progression are obscure.

Methods

In this two-center retrospective study, we included COVID-19 patients with moderate severity between March 2020 and May 2021. We classified patients into three groups according to bicarbonate concentrations: high (>27 mEq/L), normal (21 to 27 mEq/L), and low (<21 mEq/L). The primary outcome was the time to clinical worsening defined by the requirement of intubation or death during 90 days. We evaluated high or low bicarbonate concentration during the clinical course related to the primary outcome using multivariable Cox proportional hazard models.

Results

Of the 60 participants (median age 72 years), 60% were men. Participants were classified into high (13 patients), normal (30 patients), and low (17 patients) groups. Clinical worsening occurred in 54% of patients in the high group, 23% in the normal group, and 65% in the low group. Both high and low groups were associated with a higher clinical worsening rate: HR, 3.02 (95% CI, 1.05 to 8.63) in the high group; 3.49 (95% CI: 1.33 to 9.12) in the low group.

Conclusion

Monitoring of bicarbonate concentrations may be useful to predict the prognosis.

Evaluation of Procalcitonin's Utility to Predict Concomitant Bacterial Pneumonia in Critically Ill COVID-19 Patients

Background: Historically, procalcitonin(PCT) has been used as a predictor of bacterial infection and to guide antibiotic therapy in hospitalized patients. The purpose of this study was to determine PCT's diagnostic utility in predicting secondary bacterial pneumonia in critically ill patients with severe COVID-19 pneumonia. Methods: A retrospective cohort study was conducted in COVID-19 adults admitted to the ICU between March 2020, and March 2021. All included patients had a PCT level within 72 h of presentation and serum creatinine of <1.5mg/dL. A PCT threshold of 0.5ng/mL was used to compare patients with high( ≥ 0.5ng/mL) versus low(< 0.5ng/mL) PCT. Bacterial pneumonia was defined by positive respiratory culture. A receiver operating characteristics (ROC) curve was utilized to evaluate PCT as a diagnostic test for bacterial pneumonia, with an area under the curve(AUC) threshold of 0.7 to signify an accurate diagnostic test. A multivariable model was constructed to identify variables associated with in-hospital mortality. Results: There were 165 patients included: 127 low PCT versus 38 high PCT. There was no significant difference in baseline characteristics, vital signs, severity of disease, or outcomes among low versus high PCT groups (all p > 0.05). While there was no difference in bacterial pneumonia in low versus high groups (34(26.8%) versus 12(31.6%), p = 0.562), more patients in the high PCT group had bacteremia (19(15%) versus 11(28.9%), p = 0.050). Sensitivity was 26.1% and specificity was 78.2% for PCT to predict bacterial pneumonia coinfection in ICU patients with COVID-19 pneumonia. ROC yielded an AUC 0.54 (p = 0.415). After adjusting for LDH>350U/L and creatinine in multivariable regression, PCT did not enhance performance of the regression model. Conclusions: PCT offers little to no predictive utility in diagnosing concomitant bacterial pneumonia in critically ill patients with COVID-19 nor in predicting increased severity of disease or worse outcomes including mortality.

Prognostic Factors Associated With Mortality of Patients With COVID-19 Requiring Ventilator Management: A Retrospective Cohort Study

Aim

There are few reports on the prognostic factors associated with mortality in coronavirus disease (COVID-19) patients with critical disease. This study assessed prognostic factors associated with mortality of patients with critical COVID-19 who required ventilator management.

Methods

This single-center, retrospective cohort study used medical record data of COVID-19 patients admitted to an emergency ICU at a hospital in Japan between March 1, 2020 and September 30, 2021, and provided with ventilator management. Multivariable logistic regression was used to identify factors associated with mortality.

Results

Seventy patients were included, of whom 29 (41.4%) died. The patients who died were significantly older (median: 69 years) (interquartile range [IQR]: 47-82 years) than the patients who survived (62 years [38-84 years], p<0.007). In addition, patients who died were significantly less likely to have received steroid therapy than patients who survived (25 [86.2%] vs. 41 [100%], p=0.026). In the multivariable analysis, age was identified as a significant prognostic factor for mortality and the risk of death increased by 6% for every one-year increase in age (OR: 1.06; 95% CI: 1.00-1.13; p=0.048). Medical history was not a risk factor for death.

Conclusion

Age was a predictor of mortality in critically ill patients with COVID-19. Therefore, the indications for critical care in older patients with COVID-19 should be carefully considered.

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

Patterns of presentation, prevalence and associated factors of mortality in ICU among adult patients during the pandemic of COVID 19: A retrospective cross-sectional study

Background

There is concern that patients admitted to the intensive care unit (ICU) with Corona Virus Disease 2019 (COVID-19) have variable prevalence reports of mortality. The survival rates are also inconsistently reported due to varying follow-up periods. Even if data on outcomes and baseline characteristics of ICU patients with COVID-19 is essential for action planning to manage complications, it is still left undisclosed in our study setting.

Materials and method

This cross-sectional study was conducted on 402 samples using a retrospective chart review of patient's data who were admitted in the past 2 years of the adult ICUs. All the data were entered and analyzed with SPSS version 21. A multivariable Logistic regression analysis was used to identify the association between outcome variables with independent factors and a p-value of less than 0.05 was taken as statistically significant with a 95% confidence interval. We used text, tables, and figures for the result.

Result

The overall prevalence of mortality among adult patients admitted to ICU during COVID-19 pandemics was 67.4%. From the multivariable logistic regression analysis, factors that were shown to have an association with an increase in ICU patient mortality were; lack of Vasopressor support, patients who had confirmed COVID 19 infection, core body temperature at admission greater than 37.5 °c, SPO2 at admission less than 90%, patients who had diagnosed ischemic heart disease (IHD), patients with acute respiratory distress syndrome (ARDS), patients who were intubated and mechanically ventilated (MV), and patient's ICU length of stay longer than two weeks.

Conclusion

The prevalence of ICU mortality in adult patients was higher in Debre Tabor Comprehensive specialized hospital. Therefore, clinicians need to minimize factors that maximize patient mortalities like ARDS, hyperthermia, Desaturation, Covid infection, IHD, intubation and MV, lack of Vasopressor use, and prolonged ICU stay.

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The prevalence of mortality in ICU was 67.4%

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Hyperthermia increases mortality in ICU

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Use of vasopressor in ICU decreases mortality

Admission vital signs as predictors of COVID-19 mortality: a retrospective cross-sectional study

Background

COVID-19 remains a major healthcare concern. Vital signs are routinely measured on admission and may provide an early, cost-effective indicator of outcome – more so in developing countries where such data is scarce. We sought to describe the association between six routinely measured admission vital signs and COVID-19 mortality, and secondarily to derive potential applications for resource-limited settings.

Methods

Retrospective analysis of consecutive patients admitted to King Edward VIII Hospital, South Africa, with COVID-19 during June–September 2020 was undertaken. The sample was subdivided into survivors and non-survivors and comparisons made in terms of demographics and admission vital signs. Univariate and multivariate analysis of predictor variables identified associations with in-hospital mortality, with the resulting multivariate regression model evaluated for its predictive ability with receiver operating characteristic (ROC) curve analysis.

Results

The 236 participants enrolled comprised 153(77.54%) survivors and 53(22.46%) non-survivors. Most participants were Black African(87.71%) and female(59.75%) with a mean age of 53.08(16.96) years. The non-survivor group demonstrated a significantly lower median/mean for admission oxygen saturation (%) [87(78–95) vs. 96(90–98)] and diastolic BP (mmHg) [70.79(14.66) vs. 76.3(12.07)], and higher median for admission respiratory rate (breaths/minute) [24(20–28) vs. 20(20–23)] and glucose (mmol/l) [10.2(6.95–16.25) vs. 7.4(5.5–9.8)]. Age, oxygen saturation, respiratory rate, glucose and diastolic BP were found to be significantly associated with mortality on univariate analysis. A log rank test revealed significantly lower survival rates in patients with an admission oxygen saturation < 90% compared with ≥90% (p = 0.001). Multivariate logistic regression revealed a significant relationship between age and oxygen saturation with in-hospital mortality (OR 1.047; 95% CI 1.016–1.080; p = 0.003 and OR 0.922; 95% CI 0.880–0.965; p = 0.001 respectively). A ROC curve analysis generated an area under the curve (AUC) of 0.778 (p < 0.001) when evaluating the predictive ability of oxygen saturation, respiratory rate, glucose and diastolic BP for in-hospital death. This improved to an AUC of 0.832 (p < 0.001) with the inclusion of age.

Conclusion

A multivariate regression model comprising admission oxygen saturation, respiratory rate, glucose and diastolic BP (with/without age) demonstrated promising predictive capacity, and may provide a cost-effective means for early prognostication of patients admitted with COVID-19 in resource-limited settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-022-00631-7.

The clinical course, biochemical markers, and clinical outcomes of COVID-19 positive patients from the third wave in Pakistan: A retrospective cohort study

Background

Third wave of COVID-19 has affected several countries. Case fatality rates from first and second waves are expected to be surpassed by the current wave due to various variant transmissions. This study was aimed to compare and contrast the significant clinical markers between survivors and non-survivors during the third wave of COVID-19 to assess severity and prognosis.

Methods

It includes all the patients who were diagnosed with COVID-19 polymerase chain reaction (PCR) during the third wave, and were monitored for their disease course and outcomes. A total of 209 patients were included in the analysis via non-probability consecutive sampling method.

Results

The median age was higher in non-surviving patients (p = 0.010). Majority of deaths occurred in intensive care patients (p < 0.001) and those with diabetes (p = 0.032) and hypertension (p = 0.003). Fever was the most predominant symptom in all patients (78.9%), dyspnea was common among expired individuals (p = 0.043) while recovered patients were more likely to be asymptomatic (p = 0.044). Gastrointestinal symptoms were not found marked during this wave. Being on ventilator has higher mortality (p < 0.001). Predominant radiological findings were interstitial patches or infiltrate (43.7%). Multivariable analysis showed hypertension (p = 0.042), BiPAP/CPAP (p < 0.001), being on ventilator (p = 0.004), and ARDS (p < 0.001) was associated with poor survival while patchy interstitial infiltrates on X-ray had good survival probability (p = 0.032). On Kaplan-Meier survival analysis, hypertension (p = 0.003), BiPAP/CPAP (p = 0.008), ventilator (p = 0.025), ICU stay (p = 0.001), high-grade fever (p = 0.001), and ARDS (p < 0.001) had reduced cumulative survival.

Conclusion

Certain biochemical markers were more predictive of disease severity in the third-wave than the preceding waves.

Real-World Efficacy of Regdanvimab on Clinical Outcomes in Patients with Mild to Moderate COVID-19

Background: This study aims to evaluate the real-world effectiveness of regdanvimab on clinical outcomes in patients with mild to moderate coronavirus disease 2019 (COVID-19). Methods: This retrospective observational study included 152 patients (89 received regdanvimab and 63 did not) diagnosed with mild to moderate COVID-19 between August 2021 and October 2021 and admitted to Armed Forces Goyang Hospital. We collected information on the use of regdanvimab, remdesivir, dexamethasone, and supplemental oxygen; symptom severity score (SSS); and laboratory test results. A linear mixed-effects model was used to test the effectiveness of regdanvimab usage on SSS and the results of laboratory tests. A multivariate logistic regression model was used to calculate the odds ratio (OR) for additional therapeutic options, such as remdesivir, dexamethasone, and supplemental oxygen. Results: The patients who received regdanvimab were older, showed a higher rate of vaccination, and had a higher Charlson comorbidity index, initial body temperature, and percentages of pneumonia at admission. The use of regdanvimab showed no interactive effects on the SSS and laboratory findings. Older age, male sex, obesity, high initial body temperature, and the presence of pneumonia at admission were associated with increased ORs for the use of these additional treatments. The use of regdanvimab reduced the probability of requiring additional therapies such as remdesivir, dexamethasone, and oxygen supplementation by 90.3% (95% confidence interval (CI), 60.3–97.6), 85.8% (95% CI, 34.2–96.9), and 89.8% (95% CI, 48.3–98), respectively. Conclusions: Regdanvimab usage was well tolerated and was associated with a decreased probability of requiring remdesivir, dexamethasone, and oxygen therapy. However, changes in SSS were not significantly different by the drug usage.

Nursing care recommendation for pediatric COVID-19 patients in the hospital setting: A brief scoping review

BACKGROUND

The hospitalization of children during the COVID-19 pandemic has affected their physical and mental health. Pediatric nurses have faced challenges in providing high-quality nursing care for children and their families. However, the pediatric nursing care recommendations for COVID-19 patients in the hospital setting remain unclear. The current scoping review provides recommendations for nursing interventions for pediatric COVID-19 patients in the hospital setting.

METHODS AND FINDINGS

The selected articles containing management and nursing recommendations for COVID-19 that have occurred in pediatric patients ages 0-19 years old. A search strategy was developed and implemented in seven databases. We included peer-reviewed articles that reported observational or interventional studies, as well as policy papers, guides or guidelines, letters and editorials, and web articles. A total of 134 articles and other documents relevant to this review were included. We categorized the results based on The Nursing Intervention Classification (NIC) taxonomy which consists of six domains (e.g., Physiological: Basic); eleven classes (e.g., Nutrition Support); and eighteen intervention themes (e.g., Positioning, Family Presence Facilitation, Family Support, and Discharge Planning).

CONCLUSION

Apart from the intervention of physical problems, there is a need to promote patient- and family-centered care, play therapy, and discharge planning to help children and families cope with their new situation.

Coronavirus Disease 2019 Temperature Trajectories Correlate With Hyperinflammatory and Hypercoagulable Subphenotypes

OBJECTIVES

Body temperature trajectories of infected patients are associated with specific immune profiles and survival. We determined the association between temperature trajectories and distinct manifestations of coronavirus disease 2019.

DESIGN

Retrospective observational study.

SETTING

Four hospitals within an academic healthcare system from March 2020 to February 2021.

PATIENTS

All adult patients hospitalized with coronavirus disease 2019.

INTERVENTIONS

Using a validated group-based trajectory model, we classified patients into four previously defined temperature trajectory subphenotypes using oral temperature measurements from the first 72 hours of hospitalization. Clinical characteristics, biomarkers, and outcomes were compared between subphenotypes.

MEASUREMENTS AND MAIN RESULTS

The 5,903 hospitalized coronavirus disease 2019 patients were classified into four subphenotypes: hyperthermic slow resolvers (n = 1,452, 25%), hyperthermic fast resolvers (1,469, 25%), normothermics (2,126, 36%), and hypothermics (856, 15%). Hypothermics had abnormal coagulation markers, with the highest d-dimer and fibrin monomers (p < 0.001) and the highest prevalence of cerebrovascular accidents (10%, p = 0.001). The prevalence of venous thromboembolism was significantly different between subphenotypes (p = 0.005), with the highest rate in hypothermics (8.5%) and lowest in hyperthermic slow resolvers (5.1%). Hyperthermic slow resolvers had abnormal inflammatory markers, with the highest C-reactive protein, ferritin, and interleukin-6 (p < 0.001). Hyperthermic slow resolvers had increased odds of mechanical ventilation, vasopressors, and 30-day inpatient mortality (odds ratio, 1.58; 95% CI, 1.13-2.19) compared with hyperthermic fast resolvers. Over the course of the pandemic, we observed a drastic decrease in the prevalence of hyperthermic slow resolvers, from representing 53% of admissions in March 2020 to less than 15% by 2021. We found that dexamethasone use was associated with significant reduction in probability of hyperthermic slow resolvers membership (27% reduction; 95% CI, 23-31%; p < 0.001).

CONCLUSIONS

Hypothermics had abnormal coagulation markers, suggesting a hypercoagulable subphenotype. Hyperthermic slow resolvers had elevated inflammatory markers and the highest odds of mortality, suggesting a hyperinflammatory subphenotype. Future work should investigate whether temperature subphenotypes benefit from targeted antithrombotic and anti-inflammatory strategies.

Research and Development of a COVID-19 Tracking System in Order to Implement Analytical Tools to Reduce the Infection Risk

The whole world is currently focused on COVID-19, which causes considerable economic and social damage. The disease is spreading rapidly through the population, and the effort to stop the spread is entirely still failing. In our article, we want to contribute to the improvement of the situation. We propose a tracking system that would identify affected people with greater accuracy than medical staff can. The main goal was to design hardware and construct a device that would track anonymous risky contacts in areas with a highly concentrated population, such as schools, hospitals, large social events, and companies. We have chosen a 2.4 GHz proprietary protocol for contact monitoring and mutual communication of individual devices. The 2.4 GHz proprietary protocol has many advantages such as a low price and higher resistance to interference and thus offers benefits. We conducted a pilot experiment to catch bugs in the system. The device is in the form of a bracelet and captures signals from other bracelets worn at a particular location. In case of contact with an infected person, the alarm is activated. This article describes the concept of the tracking system, the design of the devices, initial tests, and plans for future use.

Predictors of COVID-19 Mortality in Critically Ill ICU Patients: A Multicenter Retrospective Observational Study

Introduction Coronavirus disease 2019 (COVID-19) is a multisystemic disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can lead to a broad spectrum of disease severity, from asymptomatic to severe respiratory disease. In addition, the mortality rate is exceedingly high among COVID-19 patients admitted to the ICU. The purpose of this study is to examine the differences between survivors and non-survivors of critically ill COVID-19 patients admitted to the ICU. Method This multicenter retrospective observational study was conducted at two hospitals in Southern California, USA. First, we compared the characteristics of the ICU patients that died and those that survived using the chi-square test for categorical variables and t-test for the continuous variables, with a p-value of 0.05 considered significant. Finally, we did a backward selection Cox multivariate regression analysis using mortality as a dependent variable. Result There were 1,116 patients admitted with COVID-19 during our study period. Of this number, 238 (21.3%) were admitted to the ICU. Among patients admitted to the ICU, 195 (81.9%) died and 43 (18.1%) survived. In the multivariate Cox regression analysis, C-reactive protein (CRP) (HR 1.03, 95% CI 1.003-1.059), tachycardia (HR 3.51, 95% CI 1.83-6.72), and age (HR 1.02, 95% CI 1.01-1.04) were independently associated with mortality. Patients' BMI and comorbidities such as hypertension, diabetes, chronic obstructive pulmonary disease, and chronic kidney disease did not predict mortality. Conclusion Age, elevated CRP, and tachycardia were independent risk factors for mortality in COVID-19 patients admitted to the ICU. It appears that several factors that predict severe diseases in COVID-19 patients, such as BMI and comorbidities, become less important once patients are admitted to the ICU.

Using decision tree algorithms for estimating ICU admission of COVID-19 patients

Introduction

Materials and methods

Results

Conclusions

Lower blood pH as a strong prognostic factor for fatal outcomes in critically ill COVID-19 patients at an intensive care unit: A multivariable analysis

BACKGROUND

Data of critically ill COVID-19 patients are being evaluated worldwide, not only to understand the various aspects of the disease and to refine treatment strategies but also to improve clinical decision-making. For clinical decision-making in particular, prognostic factors of a lethal course of the disease would be highly relevant.

METHODS

In this retrospective cohort study, we analyzed the first 59 adult critically ill Covid-19 patients treated in one of the intensive care units of the University Medical Center Regensburg, Germany. Using uni- and multivariable regression models, we extracted a set of parameters that allowed for prognosing in-hospital mortality.

RESULTS

Within the cohort, 19 patients died (mortality 32.2%). Blood pH value, mean arterial pressure, base excess, troponin, and procalcitonin were identified as highly significant prognostic factors of in-hospital mortality. However, no significant differences were found for other parameters expected to be relevant prognostic factors, like low arterial partial pressure of oxygen or high lactate levels. In the multivariable logistic regression analysis, the pH value and the mean arterial pressure turned out to be the most influential prognostic factors for a lethal course.

Gut Microbiota Dysbiosis Correlates with Abnormal Immune Response in Moderate COVID-19 Patients with Fever

Background

Most COVID-19 patients are moderate, and fever is the most common clinical manifestation and associated with poorer prognosis. Gut microbiota may also play important roles in COVID-19 pathogenesis. However, the association between gut microbiota and fever in individuals with moderate COVID-19 remains unclear.

Methods

We compared the clinical features and laboratory results of 187 moderate COVID-19 patients with fever and without fever and identified several inflammatory markers in patients with fever. Then, we performed gut metagenome-wide association study for 31 individuals to identify the microbes and their epitopes which have potential role in fever and hyperinflammation.

Results

Among 187 moderate COVID-19 patients, 127 (67.9%) patients presented with fever. Lymphocytes, CD3+ T cells, CD4+ T cells and the ratio of CD4+ T cells to CD8+ T cells were significantly reduced, while AST, LDH, CRP, IL-6 and IL-10 were significantly elevated in patients with fever. Gut microbiome composition was significantly altered in patients with fever compared with those with non-fever. Opportunistic pathogens such as Enterococcus faecalis and Saccharomyces cerevisiae were enriched in patients with fever. E. faecalis was positively correlated with LDH and D-dimer and negatively correlated with CD8+T cells and IL-4, while S. cerevisiae was positively correlated with diarrhea symptom. Furthermore, several species with anti-inflammatory and protective effects, such as Bacteroides fragilis and Eubacterium ramulus, were enriched in patients with non-fever. B. fragilis was positively correlated with lymphocytes, and E. ramulus was negatively correlated with LDH, AST and IL-6. Finally, we found that several bacterial epitopes of GroEL, a homolog of human HSP60, were enriched in patients with fever and positively correlated with IL-6, IL-10, WBC, neutrophils, D-dimer, LDH, CRP, and E. faecalis.

Conclusion

Gut microbiota dysbiosis correlates with abnormal immune response in moderate COVID-19 patients with fever.