Discriminant Accuracy of the SOFA Score for Determining the Probable Mortality of Patients With COVID-19 Pneumonia Requiring Mechanical Ventilation

COVID-19 critical care triage across Canada: a narrative synthesis and ethical analysis of early provincial triage protocols

PURPOSE

The COVID-19 pandemic created conditions of scarcity that led many provinces within Canada to develop triage protocols for critical care resources. In this study, we sought to undertake a narrative synthesis and ethical analysis of early provincial pandemic triage protocols.

METHODS

We collected provincial triage protocols through personal correspondence with academic and political stakeholders between June and August 2020. Protocol data were extracted independently by two researchers and compared for accuracy and agreement. We separated data into three categories for comparative content analysis: protocol development, ethical framework, and protocol content. Our ethical analysis was informed by a procedural justice framework.

RESULTS

We obtained a total of eight provincial triage protocols. Protocols were similar in content, although age, physiologic scores, and functional status were variably incorporated. Most protocols were developed through a multidisciplinary, expert-driven, consensus process, and many were informed by influenza pandemic guidelines previously developed in Ontario. All protocols employed tiered morality-focused exclusion criteria to determine scarce resource allocation at the level of regional health care systems. None included a public engagement phase, although targeted consultation with public advocacy groups and relevant stakeholders was undertaken in select provinces. Most protocols were not publicly available in 2020.

CONCLUSIONS

Early provincial COVID-19 triage protocols were developed by dedicated expert committees under challenging circumstances. Nonetheless, few were publicly available, and public consultation was limited. No protocols were ever implemented, including during periods of extreme critical care surge. A national approach to pandemic triage that incorporates additional aspects of procedural justice should be considered in preparation for future pandemics.

SCARLET (Supplemental Citicoline Administration to Reduce Lung injury Efficacy Trial): study protocol for a single-site, double-blinded, placebo-controlled, and randomized Phase 1/2 trial of i.v. citicoline (CDP-choline) in hospitalized SARS CoV-2-infected patients with hypoxemic acute respiratory failure

BACKGROUND

The SARS CoV-2 pandemic has resulted in more than 1.1 million deaths in the USA alone. Therapeutic options for critically ill patients with COVID-19 are limited. Prior studies showed that post-infection treatment of influenza A virus-infected mice with the liponucleotide CDP-choline, which is an essential precursor for de novo phosphatidylcholine synthesis, improved gas exchange and reduced pulmonary inflammation without altering viral replication. In unpublished studies, we found that treatment of SARS CoV-2-infected K18-hACE2-transgenic mice with CDP-choline prevented development of hypoxemia. We hypothesize that administration of citicoline (the pharmaceutical form of CDP-choline) will be safe in hospitalized SARS CoV-2-infected patients with hypoxemic acute respiratory failure (HARF) and that we will obtain preliminary evidence of clinical benefit to support a larger Phase 3 trial using one or more citicoline doses.

METHODS

We will conduct a single-site, double-blinded, placebo-controlled, and randomized Phase 1/2 dose-ranging and safety study of Somazina® citicoline solution for injection in consented adults of any sex, gender, age, or ethnicity hospitalized for SARS CoV-2-associated HARF. The trial is named "SCARLET" (Supplemental Citicoline Administration to Reduce Lung injury Efficacy Trial). We hypothesize that SCARLET will show that i.v. citicoline is safe at one or more of three doses (0.5, 2.5, or 5 mg/kg, every 12 h for 5 days) in hospitalized SARS CoV-2-infected patients with HARF (20 per dose) and provide preliminary evidence that i.v. citicoline improves pulmonary outcomes in this population. The primary efficacy outcome will be the SpO2:FiO2 ratio on study day 3. Exploratory outcomes include Sequential Organ Failure Assessment (SOFA) scores, dead space ventilation index, and lung compliance. Citicoline effects on a panel of COVID-relevant lung and blood biomarkers will also be determined.

DISCUSSION

Citicoline has many characteristics that would be advantageous to any candidate COVID-19 therapeutic, including safety, low-cost, favorable chemical characteristics, and potentially pathogen-agnostic efficacy. Successful demonstration that citicoline is beneficial in severely ill patients with SARS CoV-2-induced HARF could transform management of severely ill COVID patients.

TRIAL REGISTRATION

The trial was registered at www.

CLINICALTRIALS

gov on 5/31/2023 (NCT05881135).

TRIAL STATUS

Currently enrolling.

The COVIDTW2 study: Role of COVID-19 vaccination in intubated patients with COVID-19-related acute respiratory distress syndrome in Taiwan

BACKGROUND

COVID-19 vaccines have reduced the risk of disease progression to respiratory failure or death. However, in patients with breakthrough infections requiring invasive mechanical ventilation, the effect of prior COVID-19 vaccination on mortality remains inconclusive.

METHOD

We retrospectively analyzed data on patients intubated due to COVID-19 pneumonia between May 1, 2022 and October 31, 2022. Receipt of two or more doses of vaccine were considered as fully vaccinated. The primary outcome was the time from intubation to all-cause intensive care unit (ICU) mortality.

RESULT

A total of 84 patients were included (40 fully vaccinated versus 44 controls). The baseline characteristics, including age, comorbidities, and Sequential Organ Failure Assessment (SOFA) score on the day of intubation were similar between the two groups. The difference in ICU mortality rate between the fully vaccinated and control groups was not significant (35 % vs. 25 %, P = 0.317; hazard ratio with 95 % confidence interval = 1.246 (0.575-2.666), P = 0.571). The SOFA score (hazard ratio: 1.319, P = 0.001) and body mass index (BMI) (hazard ratio: 0.883, P = 0.022) were significantly associated with ICU mortality.

CONCLUSION

Being fully vaccinated was not associated with a mortality benefit in intubated patients with COVID-19. A higher SOFA score on the day of intubation and lower BMI were poor prognostic factors.

Effectiveness of Copeptin, MR-proADM and MR-proANP in Predicting Adverse Outcomes, Alone and in Combination with Traditional Severity Scores, a Secondary Analysis in COVID-19 Patients Requiring Intensive Care Admission

Objective: To investigate whether copeptin, MR-proADM and MR-proANP, alone or integrated with the SOFA, MuLBSTA and SAPS II scores, are capable of early recognition of COVID-19 ICU patients at increased risk of adverse outcomes. Methods: For this predefined secondary analysis of a larger cohort previously described, all consecutive COVID-19 adult patients admitted between March and December 2020 to the ICU of a referral, university hospital in Northern Italy were screened, and clinical severity scores were calculated upon admission. A blood sample for copeptin, MR-proADM and MR-proANP was collected within 48 h (T1), on day 3 (T3) and 7 (T7). Outcomes considered were ICU and in-hospital mortality, bacterial superinfection, recourse to renal replacement therapy (RRT) or veno-venous extracorporeal membrane oxygenation, need for invasive mechanical ventilation (IMV) and pronation. Results: Sixty-eight patients were enrolled, and in-hospital mortality was 69.1%. ICU mortality was predicted by MR-proANP measured at T1 (HR 1.005, 95% CI 1.001-1.010, p = 0.049), although significance was lost if the analysis was adjusted for procalcitonin and steroid treatment (p = 0.056). Non-survivors showed higher MR-proADM levels than survivors at all time points, and an increase in the ratio between values at baseline and at T7 > 4.9% resulted in a more than four-fold greater risk of in-hospital mortality (HR 4.417, p < 0.001). Finally, when considering patients with any reduction in glomerular filtration, an early copeptin level > 23.4 pmol/L correlated with a more than five-fold higher risk of requiring RRT during hospitalization (HR 5.305, p = 0.044). Conclusion: Timely evaluation of MR-proADM, MR-proANP and copeptin, as well as changes in the former over time, might predict mortality and other adverse outcomes in ICU patients suffering from severe COVID-19.

A modified renal angina index in critically ill patients with COVID-19

BACKGROUND

The renal angina index (RAI) is a tool that has been validated by several studies in the pediatric population to predict the development of severe acute kidney injury (AKI). The aims of this study were to evaluate the efficacy of the RAI in predicting severe AKI in critically ill patients with COVID-19 and to propose a modified RAI (mRAI) for this population.

METHODS

This was a prospective cohort analysis of all COVID-19 patients receiving invasive mechanical ventilation (IMV) who were admitted to the intensive care unit (ICU) of a third-level hospital in Mexico City from 03/2020 to 01/2021. AKI was defined according to KDIGO guidelines. The RAI score was calculated for all enrolled patients using the method of Matsuura. Since all patients had the highest score for the condition (due to receiving IMV), the score corresponded to the delta creatinine (ΔSCr) value. The main outcome was severe AKI (stage 2 or 3) at 24 and 72 h after ICU admission. A logistic regression analysis was applied to search for factors associated with the development of severe AKI, and the data were applied to develop a mRAI and compare it vis-à-vis the efficacy of both scores (RAI and mRAI).

RESULTS

Of the 452 patients studied, 30% developed severe AKI. The original RAI score was associated with AUCs of 0.67 and 0.73 at 24 h and 72 h, respectively, with a cutoff of 10 points to predict severe AKI. In the multivariate analysis adjusted for age and sex, a BMI ≥30 kg/m², a SOFA score ≥6, and Charlson score were identified as risk factors for the development of severe AKI. In the new proposed score (mRAI), the conditions were summed and multiplied by the ΔSCr value. With these modifications, the AUC improved to 0.72 and 0.75 at 24 h and 72 h, respectively, with a cutoff of 8 points.

CONCLUSIONS

The original RAI is a limited tool for patients with critical COVID-19 receiving IMV. The mRAI, with the parameters proposed in the present study, improves predictive performance and risk stratification in critically ill patients receiving IMV.

Comparing COVID-19 and influenza: Epidemiology, clinical characteristics, outcomes and mortality in the ICU

RATIONALE

Several authors have compared COVID-19 infection with influenza in the ICU.

OBJECTIVE

This study aimed to compare the baseline clinical profiles, care procedures, and mortality outcomes of patients admitted to the intensive care unit, categorized by infection status (Influenza vs. COVID-19).

METHODS

Retrospective observational study. Data were extracted from the Toulouse University Hospital from March 2014 to March 2021. To compare survival curves, we plotted the survival at Day-90 using the Kaplan-Meier curve and conducted a log-rank test. Additionally, we performed propensity score matching to adjust for confounding factors between the COVID-19 and influenza groups. Furthermore, we use the CART model for multivariate analysis.

RESULTS

The study included 363 patients admitted to the ICU due to severe viral pneumonia: 152 patients (41.9 %) with influenza and 211 patients (58.1 %) with COVID-19. COVID-19 patients exhibited a higher prevalence of cardiovascular risk factors, whereas influenza patients had significantly higher severity scores (SOFA: 10 [6-12] vs. 6 [3-9], p<0.01 and SAPS II: 51 [35-67] vs. 37 [29-50], p<0.001). Overall mortality rates were comparable between the two groups (27.6 % (n = 42) in the influenza group vs. 21.8 % (n = 46) in the COVID-19 group, p=NS). Mechanical ventilation was more commonly employed in the influenza group (76.3 % (n = 116) vs. 59.7 % (n = 126), p<0.001); however, COVID-19 patients required longer durations of mechanical ventilation (18 [9-29] days vs. 13 [5-24] days, p<0.006) and longer hospital stays (23 [13-34] days vs. 18.5 [9-34.5] days, p = 0.009). The CART analysis revealed that the use of extra renal replacement therapy was the most influential prognostic factor in the influenza group, while the PaO2/FiO2-PEEP ratio played a significant role in the COVID-19 group.

CONCLUSIONS

Despite differences in clinical presentation and prognostic factors, the mortality rates at 90 days, after adjusting for confounding factors, were similar between COVID-19 and influenza patients.

TGRA-P: Task-driven model predicts 90-day mortality from ICU clinical notes on mechanical ventilation

BACKGROUND

With the outbreak and spread of COVID-19 worldwide, limited ventilators fail to meet the surging demand for mechanical ventilation in the ICU. Clinical models based on structured data that have been proposed to rationalize ventilator allocation often suffer from poor ductility due to fixed fields and laborious normalization processes. The advent of pre-trained models and downstream fine-tuning methods allows for learning large amounts of unstructured clinical text for different tasks. But the hardware requirements of large-scale pre-trained models and purposeless networks downstream have led to a lack of promotion in the clinical domain.

OBJECTIVE

In this study, an innovative architecture of a task-driven predictive model is proposed and a Task-driven Gated Recurrent Attention Pool model (TGRA-P) is developed based on the architecture. TGRA-P predicts early mortality risk from patients' clinical notes on mechanical ventilation in the ICU, which is used to assist clinicians in diagnosis and decision-making.

METHODS

Specifically, a Task-Specific Embedding Module is proposed to fine-tune the embedding with task labels and save it as static files for downstream calls. It serves the task better and prevents GPU overload. The Gated Recurrent Attention Unit (GRA) is proposed to further enhance the dependency of the information preceding and following the text sequence with fewer parameters. In addition, we propose a Residual Max Pool (RMP) to avoid ignoring words in common text classification tasks by incorporating all word-level features of the notes for prediction. Finally, we use a fully connected decoding network as a classifier to predict the mortality risk.

RESULT

The proposed model shows very promising results with an AUROC of 0.8245±0.0096, an AUPRC of 0.7532±0.0115, an accuracy of 0.7422±0.0028 and F1-score of 0.6612±0.0059 for 90-day mortality prediction using clinical notes of ICU mechanically ventilated patients on the MIMIC-III dataset, all of which are better than previous studies. Moreover, the superiority of the proposed model in comparison with other baseline models is also statistically validated through the calculated Cohen's d effect sizes.

CONCLUSION

The experimental results show that TGRA-P based on the innovative task-driven prognostic architecture obtains state-of-the-art performance. In future work, we will build upon the provided code and investigate its applicability to different datasets. The model balances performance and efficiency, not only reducing the cost of early mortality risk prediction but also assisting physicians in making timely clinical interventions and decisions. By incorporating textual records that are challenging for clinicians to utilize, the model serves as a valuable complement to physicians' judgment, enhancing their decision-making process.

Serial laboratory biomarkers are associated with ICU outcomes in patients hospitalized with COVID-19

BACKGROUND

Clinical utility of routinely measured serial biomarkers in predicting escalation of inpatient care intensity and mortality among hospitalized patients with COVID-19 remains unknown.

METHODS

This retrospective cohort study included patients with COVID-19 who admitted to the Massachusetts General Hospital between March and June 2020 and January to March 2021. White blood cell (WBC) count, platelet count, C-reactive protein (CRP), and D-dimer values were measured on days 1, 3, and 7 of admission. Clinical outcomes include 30- and 60-day morality, ICU transfer, and overall survival (OS) over a follow-up period of 90 days. The association between serial biomarkers and outcomes were assessed using multivariable logistic regression and Cox proportional hazards models.

MEASUREMENTS AND MAIN RESULTS

Of the 456 patients hospitalized with COVID-19, 199 (43.6%) were ICU, 179 (39.3%) were medical floor, and 78 (17.1%) were initially admitted to the medical floor and then transferred to the ICU. In adjusted analyses, each unit increase in the slope of CRP was associated with a 42% higher odds of ICU transfer after controlling for the initial admission level (OR = 1.42, 95% CI: 1.25-1.65, P < 0.001). Including serial change in CRP levels from initial level on admission achieved the greatest predictive accuracy for ICU transfer (AUC = 0.72, 95% CI: 0.64-0.79).

CONCLUSIONS

Serial change in CRP levels from admission is associated with escalations of inpatient care intensity and mortality among hospitalized patients with COVID-19.

Increased carotid intima-media thickness is associated with higher odds of unfavorable outcomes in adults without advanced vascular diseases presenting with non-severe COVID-19 pneumonia: a nested case-control study

AIM

To evaluate the association between carotid intima-media thickness (CIMT) at hospital admission and unfavorable outcomes in adults without advanced vascular diseases presenting with non-severe COVID-19 pneumonia to assess the feasibility of evaluating CIMT as a risk stratification aid in this setting.

METHODS

This proof-of-concept nested case-control study enrolled consecutive non-vaccinated adults free of advanced vascular diseases presenting with verified non-severe COVID-19 pneumonia between December 2020 and June 2021. CIMT was measured at admission, and patients were managed in line with the national Ministry of Health guidelines. Those who died or required mechanical ventilation (MV) during the index hospital stay were considered cases and were matched (entropy balancing, exact matching) on a set of covariates to survivors not requiring MV (controls). Frequentist and Bayesian logistic models were fitted to the case status.

RESULTS

The study enrolled 207 patients: 27 (13%) cases and 180 controls. All were retained in the analysis after entropy balancing, while 27 cases were exactly matched to 99 controls. Higher CIMT at the proximal internal carotid artery (both left and right) was consistently associated with higher odds of being a case: all odds ratio point-estimates were ≥1.50 with lower limits of the 99% confidence intervals/credibility intervals ≥1.00 with two-sided probabilities of OR>1.00 greater than 99.5%. The susceptibility of the estimates to unmeasured confounding was low.

CONCLUSION

This study supports the feasibility of CIMT as a risk stratification aid in adults free of advanced vascular disease presenting with non-severe COVID-19 pneumonia.

Simulation of New York City's Ventilator Allocation Guideline During the Spring 2020 COVID-19 Surge

Importance

The spring 2020 surge of COVID-19 unprecedentedly strained ventilator supply in New York City, with many hospitals nearly exhausting available ventilators and subsequently seriously considering enacting crisis standards of care and implementing New York State Ventilator Allocation Guidelines (NYVAG). However, there is little evidence as to how NYVAG would perform if implemented.

Objectives

To evaluate the performance and potential improvement of NYVAG during a surge of patients with respect to the length of rationing, overall mortality, and worsening health disparities.

Design, Setting, and Participants

This cohort study included intubated patients in a single health system in New York City from March through July 2020. A total of 20 000 simulations were conducted of ventilator triage (10 000 following NYVAG and 10 000 following a proposed improved NYVAG) during a crisis period, defined as the point at which the prepandemic ventilator supply was 95% utilized.

Exposures

The NYVAG protocol for triage ventilators.

Main Outcomes and Measures

Comparison of observed survival rates with simulations of scenarios requiring NYVAG ventilator rationing.

Results

The total cohort included 1671 patients; of these, 674 intubated patients (mean [SD] age, 63.7 [13.8] years; 465 male [69.9%]) were included in the crisis period, with 571 (84.7%) testing positive for COVID-19. Simulated ventilator rationing occurred for 163.9 patients over 15.0 days, 44.4% (95% CI, 38.3%-50.0%) of whom would have survived if provided a ventilator while only 34.8% (95% CI, 28.5%-40.0%) of those newly intubated patients receiving a reallocated ventilator survived. While triage categorization at the time of intubation exhibited partial prognostic differentiation, 94.8% of all ventilator rationing occurred after a time trial. Within this subset, 43.1% were intubated for 7 or more days with a favorable SOFA score that had not improved. An estimated 60.6% of these patients would have survived if sustained on a ventilator. Revising triage subcategorization, proposed improved NYVAG, would have improved this alarming ventilator allocation inefficiency (25.3% [95% CI, 22.1%-28.4%] of those selected for ventilator rationing would have survived if provided a ventilator). NYVAG ventilator rationing did not exacerbate existing health disparities.

Conclusions and Relevance

In this cohort study of intubated patients experiencing simulated ventilator rationing during the apex of the New York City COVID-19 2020 surge, NYVAG diverted ventilators from patients with a higher chance of survival to those with a lower chance of survival. Future efforts should be focused on triage subcategorization, which improved this triage inefficiency, and ventilator rationing after a time trial, when most ventilator rationing occurred.

Predicting mortality among patients with severe COVID-19 pneumonia based on admission vital sign indices: a retrospective cohort study

BACKGROUND

Coronavirus disease 2019 (COVID-19) pneumonia remains a major public health concern. Vital sign indices-shock index (SI; heart rate [HR]/systolic blood pressure [SBP]), shock index age (SIA, SI × age), MinPulse (MP; maximum HR-HR), Pulse max index (PMI; HR/maximum HR), and blood pressure-age index (BPAI; SBP/age)-are better predictors of mortality in patients with trauma compared to traditional vital signs. We hypothesized that these vital sign indices may serve as predictors of mortality in patients with severe COVID-19 pneumonia. This study aimed to describe the association between vital sign indices at admission and COVID-19 pneumonia mortality and to modify the CURB-65 with the best performing vital sign index to establish a new mortality prediction tool.

METHODS

This retrospective study was conducted at a tertiary care center in southern Thailand. Adult patients diagnosed with COVID-19 pneumonia were enrolled in this study between January 2020 and July 2022. Patient demographic and clinical data on admission were collected from an electronic database. The area under the receiver operating characteristic (AUC) curve analysis was used to assess the predictive power of the resultant multivariable logistic regression model after univariate and multivariate analyses of variables with identified associations with in-hospital mortality.

RESULTS

In total, 251 patients with COVID-19 pneumonia were enrolled in this study. The in-hospital mortality rate was 27.9%. Non-survivors had significantly higher HR, respiratory rate, SIA, and PMI and lower MP and BPAI than survivors. A cutoff value of 51 for SIA (AUC, 0.663; specificity, 80%) was used to predict mortality. When SIA was introduced as a modifier for the CURB-65 score, the new score (the CURSIA score) showed a higher AUC than the Acute Physiology and Chronic Health Evaluation II and CURB-65 scores (AUCs: 0.785, 0.780, and 0.774, respectively) without statistical significance.

CONCLUSIONS

SIA and CURSIA scores were significantly associated with COVID-19 pneumonia mortality. These scores may contribute to better patient triage than traditional vital signs.

External validation of the ISARIC 4C Mortality Score to predict in-hospital mortality among patients with COVID-19 in a Canadian intensive care unit: a single-centre historical cohort study

PURPOSE

With uncertain prognostic utility of existing predictive scoring systems for COVID-19-related illness, the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) 4C Mortality Score was developed by the International Severe Acute Respiratory and Emerging Infection Consortium as a COVID-19 mortality prediction tool. We sought to externally validate this score among critically ill patients admitted to an intensive care unit (ICU) with COVID-19 and compare its discrimination characteristics to that of the Acute Physiology and Chronic Health Evaluation (APACHE) II and Sequential Organ Failure Assessment (SOFA) scores.

METHODS

We enrolled all consecutive patients admitted with COVID-19-associated respiratory failure between 5 March 2020 and 5 March 2022 to our university-affiliated and intensivist-staffed ICU (Jewish General Hospital, Montreal, QC, Canada). After data abstraction, our primary outcome of in-hospital mortality was evaluated with an objective of determining the discriminative properties of the ISARIC 4C Mortality Score, using the area under the curve of a logistic regression model.

RESULTS

A total of 429 patients were included, 102 (23.8%) of whom died in hospital. The receiver operator curve of the ISARIC 4C Mortality Score had an area under the curve of 0.762 (95% confidence interval [CI], 0.717 to 0.811), whereas those of the SOFA and APACHE II scores were 0.705 (95% CI, 0.648 to 0.761) and 0.722 (95% CI, 0.667 to 0.777), respectively.

CONCLUSIONS

The ISARIC 4C Mortality Score is a tool that had a good predictive performance for in-hospital mortality in a cohort of patients with COVID-19 admitted to an ICU for respiratory failure. Our results suggest a good external validity of the 4C score when applied to a more severely ill population.

Triage Procedures for Critical Care Resource Allocation During Scarcity

Importance

During the COVID-19 pandemic, many US states issued or revised pandemic preparedness plans guiding allocation of critical care resources during crises. State plans vary in the factors used to triage patients and have faced criticism from advocacy groups due to the potential for discrimination.

Objective

To analyze the role of comorbidities and long-term prognosis in state triage procedures.

Design, Setting, and Participants

This cross-sectional study used data gathered from parallel internet searches for state-endorsed pandemic preparedness plans for the 50 US states, District of Columbia, and Puerto Rico (hereafter referred to as states), which were conducted between November 25, 2021, and June 16, 2023. Plans available on June 16, 2023, that provided step-by-step instructions for triaging critically ill patients were categorized for use of comorbidities and prognostication.

Main Outcomes and Measures

Prevalence and contents of lists of comorbidities and their stated function in triage and instructions to predict duration of postdischarge survival.

Results

Overall, 32 state-promulgated pandemic preparedness plans included triage procedures specific enough to guide triage in clinical practice. Twenty of these (63%) included lists of comorbidities that excluded (11 of 20 [55%]) or deprioritized (8 of 20 [40%]) patients during triage; one state's list was formulated to resolve ties between patients with equal triage scores. Most states with triage procedures (21 of 32 [66%]) considered predicted survival beyond hospital discharge. These states proposed different prognostic time horizons; 15 of 21 (71%) were numeric (ranging from 6 months to 5 years after hospital discharge), with the remaining 6 (29%) using descriptive terms, such as long-term.

Conclusions and Relevance

In this cross-sectional study of state-promulgated critical care triage policies, most plans restricted access to scarce critical care resources for patients with listed comorbidities and/or for patients with less-than-average expected postdischarge survival. This analysis raises concerns about access to care during a public health crisis for populations with high burdens of chronic illness, such as individuals with disabilities and minoritized racial and ethnic groups.

Utility of Presepsin and Interferon-λ3 for Predicting Disease Severity and Clinical Outcomes in COVID-19 Patients

We explored the utility of novel biomarkers, presepsin and interferon-λ3 (IFN-λ3), for predicting disease severity and clinical outcomes in hospitalized Coronavirus (COVID-19) patients. In a total of 55 patients (non-critical, n = 16; critical, n = 39), presepsin and IFN-λ3 were compared with sequential organ failure assessment (SOFA) scores and age. Disease severity and clinical outcomes (in-hospital mortality, intensive care unit admission, ventilator use, and kidney replacement therapy) were analyzed using receiver operating characteristic (ROC) curves. In-hospital mortality was also analyzed using the Kaplan-Meier method with hazard ratios (HR). SOFA scores, age, presepsin, and IFN-λ3 predicted disease severity comparably (area under the curve [AUC], 0.67-0.73). SOFA score and IFN-λ3 predicted clinical outcomes comparably (AUC, 0.68-0.88 and 0.66-0.74, respectively). Presepsin predicted in-hospital mortality (AUC = 0.74). The combination of presepsin and IFN-λ3 showed a higher mortality risk than SOFA score or age (HR [95% confidence interval, CI], 6.7 [1.8-24.1]; 3.6 [1.1-12.1]; 2.8 [0.8-9.6], respectively) and mortality rate further increased when presepsin and IFN-λ3 were added to SOFA scores or age (8.5 [6.8-24.6], 4.2 [0.9-20.6], respectively). In the elderly (≥65 years), in-hospital mortality rate was significantly higher when both presepsin and IFN-λ3 levels increased than when either one or no biomarker level increased (88.9% vs. 14.3%, p < 0.001). Presepsin and IFN-λ3 predicted disease severity and clinical outcomes in hospitalized COVID-19 patients. Both biomarkers, whether alone or added to the clinical assessment, could be useful for managing COVID-19 patients, especially the elderly.

Metagenomic assessment of gut microbial communities and risk of severe COVID-19

BACKGROUND

The gut microbiome is a critical modulator of host immunity and is linked to the immune response to respiratory viral infections. However, few studies have gone beyond describing broad compositional alterations in severe COVID-19, defined as acute respiratory or other organ failure.

METHODS

We profiled 127 hospitalized patients with COVID-19 (n = 79 with severe COVID-19 and 48 with moderate) who collectively provided 241 stool samples from April 2020 to May 2021 to identify links between COVID-19 severity and gut microbial taxa, their biochemical pathways, and stool metabolites.

RESULTS

Forty-eight species were associated with severe disease after accounting for antibiotic use, age, sex, and various comorbidities. These included significant in-hospital depletions of Fusicatenibacter saccharivorans and Roseburia hominis, each previously linked to post-acute COVID syndrome or "long COVID," suggesting these microbes may serve as early biomarkers for the eventual development of long COVID. A random forest classifier achieved excellent performance when tasked with classifying whether stool was obtained from patients with severe vs. moderate COVID-19, a finding that was externally validated in an independent cohort. Dedicated network analyses demonstrated fragile microbial ecology in severe disease, characterized by fracturing of clusters and reduced negative selection. We also observed shifts in predicted stool metabolite pools, implicating perturbed bile acid metabolism in severe disease.

CONCLUSIONS

Here, we show that the gut microbiome differentiates individuals with a more severe disease course after infection with COVID-19 and offer several tractable and biologically plausible mechanisms through which gut microbial communities may influence COVID-19 disease course. Further studies are needed to expand upon these observations to better leverage the gut microbiome as a potential biomarker for disease severity and as a target for therapeutic intervention.

Intermittent body composition analysis as monitoring tool for muscle wasting in critically ill COVID-19 patients

OBJECTIVES

SARS-CoV-2 virus infection can lead to acute respiratory distress syndrome (ARDS), which can be complicated by severe muscle wasting. Until now, data on muscle loss of critically ill COVID-19 patients are limited, while computed tomography (CT) scans for clinical follow-up are available. We sought to investigate the parameters of muscle wasting in these patients by being the first to test the clinical application of body composition analysis (BCA) as an intermittent monitoring tool.

MATERIALS

BCA was conducted on 54 patients, with a minimum of three measurements taken during hospitalization, totaling 239 assessments. Changes in psoas- (PMA) and total abdominal muscle area (TAMA) were assessed by linear mixed model analysis. PMA was calculated as relative muscle loss per day for the entire monitoring period, as well as for the interval between each consecutive scan. Cox regression was applied to analyze associations with survival. Receiver operating characteristic (ROC) analysis and Youden index were used to define a decay cut-off.

RESULTS

Intermittent BCA revealed significantly higher long-term PMA loss rates of 2.62% (vs. 1.16%, p < 0.001) and maximum muscle decay of 5.48% (vs. 3.66%, p = 0.039) per day in non-survivors. The first available decay rate did not significantly differ between survival groups but showed significant associations with survival in Cox regression (p = 0.011). In ROC analysis, PMA loss averaged over the stay had the greatest discriminatory power (AUC = 0.777) for survival. A long-term PMA decline per day of 1.84% was defined as a threshold; muscle loss beyond this cut-off proved to be a significant BCA-derived predictor of mortality.

CONCLUSION

Muscle wasting in critically ill COVID-19 patients is severe and correlates with survival. Intermittent BCA derived from clinically indicated CT scans proved to be a valuable monitoring tool, which allows identification of individuals at risk for adverse outcomes and has great potential to support critical care decision-making.

Modeling mortality risk in patients with severe COVID-19 from Mexico

Background

Severe acute respiratory syndrome caused by a coronavirus (SARS-CoV-2) is responsible for the COVID-19 disease pandemic that began in Wuhan, China, in December 2019. Since then, nearly seven million deaths have occurred worldwide due to COVID-19. Mexicans are especially vulnerable to the COVID-19 pandemic as Mexico has nearly the worst observed case-fatality ratio (4.5%). As Mexican Latinos represent a vulnerable population, this study aimed to determine significant predictors of mortality in Mexicans with COVID-19 who were admitted to a large acute care hospital.

Methods

In this observational, cross-sectional study, 247 adult patients participated. These patients were consecutively admitted to a third-level referral center in Yucatan, Mexico, from March 1st, 2020, to August 31st, 2020, with COVID-19-related symptoms. Lasso logistic and binary logistic regression were used to identify clinical predictors of death.

Results

After a hospital stay of about eight days, 146 (60%) patients were discharged; however, 40% died by the twelfth day (on average) after hospital admission. Out of 22 possible predictors, five crucial predictors of death were found, ranked by the most to least important: (1) needing to be placed on a mechanical ventilator, (2) reduced platelet concentration at admission, (3) increased derived neutrophil to lymphocyte ratio, (4) increased age, and (5) reduced pulse oximetry saturation at admission. The model revealed that these five variables shared ~83% variance in outcome.

Conclusion

Of the 247 Mexican Latinos patients admitted with COVID-19, 40% died 12  days after admission. The patients' need for mechanical ventilation (due to severe illness) was the most important predictor of mortality, as it increased the odds of death by nearly 200-fold.

Serial SOFA-score trends in ICU-admitted COVID-19 patients as predictor of 28-day mortality: A prospective cohort study

Background and Aim

The efficacy of Sequential Organ Failure Assessment (SOFA) score as predictor of clinical outcomes among ICU-admitted COVID-19 patients is still controversial. We aimed to assess whether SOFA-score in different time intervals could predict 28-day mortality compared with other well-acknowledged risk factors of COVID-19 mortality.

Methods

This observational prospective cohort was conducted on 1057 patients from March 2020 to March 2022 at Masih Daneshvari Hospital, Iran. The univariate and multivariate Cox proportional analysis were performed to assess the hazards of SOFA-score models. Receiver operating characteristic (ROC) curves were designed to estimate the predictive values.

Results

Mean SOFA-score during first 96 h (HR: 3.82 [CI: 2.75-5.31]), highest SOFA-score (HR: 2.70 [CI: 1.93-3.78]), and initial SOFA-score (HR: 1.65 [CI: 1.30-2.11]) had strongest association with 28-day mortality (p < .0001). In contrast, SOFA scores at 48 and 96 h as well as Δ-SOFA: 48-0 h and Δ-SOFA: 96-0 h did not show significant correlations. Among them, merely mean SOFA-score (HR: 2.28 [CI: 2.21-3.51]; p < .001) remained as independent prognosticator on multivariate regression analysis; though having less odds of predicting value compared with age (HR: 3.81 [CI: 1.98-5.21]), hypertension (HR: 3.11 [CI: 1.26-3.81]), coronary artery disease [CAD] (HR: 2.82 [CI: 1.51-4.8]), and diabetes mellitus (HR: 2.45 [CI: 1.36-2.99]). The area under ROC (AUROC) for mean SOFA-score (0.77) and highest SOFA-score (0.71) were larger than other SOFA intervals. Calculating the first 96 h of SOFA trends, it was obtained that fatality rate was <12.3% if the score dropped, between 28.8% and 46.29% if the score remained unchanged, and >50.45% if the score increased.

Conclusion

To predict the 28-day mortality among ICU-admitted COVID-19 patients, mean SOFA upon first 96 h of ICU stay is reliable; while having inadequate accuracy comparing with well-acknowledged COVID-19 mortality predictors (age, diabetes mellitus, hypertension, CAD). Notably, increased SOFA levels in the course of first 96 h of ICU-admission, prognosticate at least 50% fatality regardless of initial SOFA score.

The positive impact of COVID-19 on critical care: from unprecedented challenges to transformative changes, from the perspective of young intensivists

Over the past 2 years, SARS-CoV-2 infection has resulted in numerous hospitalizations and deaths worldwide. As young intensivists, we have been at the forefront of the fight against the COVID-19 pandemic and it has been an intense learning experience affecting all aspects of our specialty. Critical care was put forward as a priority and managed to adapt to the influx of patients and the growing demand for beds, financial and material resources, thereby highlighting its flexibility and central role in the healthcare system. Intensivists assumed an essential and unprecedented role in public life, which was important when claiming for indispensable material and human investments. Physicians and researchers around the world worked hand-in-hand to advance research and better manage this disease by integrating a rapidly growing body of evidence into guidelines. Our daily ethical practices and communication with families were challenged by the massive influx of patients and restricted visitation policies, forcing us to improve our collaboration with other specialties and innovate with new communication channels. However, the picture was not all bright, and some of these achievements are already fading over time despite the ongoing pandemic and hospital crisis. In addition, the pandemic has demonstrated the need to improve the working conditions and well-being of critical care workers to cope with the current shortage of human resources. Despite the gloomy atmosphere, we remain optimistic. In this ten-key points review, we outline our vision on how to capitalize on the lasting impact of the pandemic to face future challenges and foster transformative changes of critical care for the better.

Blood Biomarkers from the Emergency Department Disclose Severe Omicron COVID-19-Associated Outcomes

Background: Since its outbreak, Coronavirus disease 2019 (COVID-19), a life-threatening respiratory illness, has rapidly become a public health emergency with a devastating social impact. Lately, the Omicron strain is considered the main variant of concern. Routine blood biomarkers are, indeed, essential for stratifying patients at risk of severe outcomes, and a huge amount of data is available in the literature, mainly for the previous variants. However, only a few studies are available on early routine biochemical blood biomarkers for Omicron-afflicted patients. Thus, the aim and novelty of this study were to identify routine blood biomarkers detected at the emergency room for the early prediction of severe morbidity and/or mortality. Methods: 449 COVID-19 patients from Sapienza University Hospital of Rome were divided into four groups: (1) the emergency group (patients with mild forms who were quickly discharged); (2) the hospital ward group (patients that after the admission in the emergency department were hospitalized in a COVID-19 ward); (3) the intensive care unit (ICU) group (patients that after the admission in the emergency department required intensive assistance); (4) the deceased group (patients that after the admission in the emergency department had a fatal outcome). Results: ANOVA and ROC data showed that high-sensitivity troponin-T (TnT), fibrinogen, glycemia, C-reactive protein, lactate dehydrogenase, albumin, D-dimer myoglobin, and ferritin for both men and women may predict lethal outcomes already at the level of the emergency department. Conclusions: Compared to previous Delta COVID-19 parallel emergency patterns of prediction, Omicron-induced changes in TnT may be considered other early predictors of severe outcomes.

Prognostic models in COVID-19 infection that predict severity: a systematic review

Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.

Hematological Parameters and Procalcitonin as Discriminants between Bacterial Pneumonia-Induced Sepsis and Viral Sepsis Secondary to COVID-19: A Retrospective Single-Center Analysis

Bacterial and viral sepsis induce alterations of all hematological parameters and procalcitonin is used as a biomarker of infection and disease severity. Our aim was to study the hematological patterns associated with pulmonary sepsis triggered by bacteria and Severe Acute Respiratory Syndrome-Coronavirus-type-2 (SARS-CoV-2) and to identify the discriminants between them. We performed a retrospective, observational study including 124 patients with bacterial sepsis and 138 patients with viral sepsis. Discriminative ability of hematological parameters and procalcitonin between sepsis types was tested using receiver operating characteristic (ROC) analysis. Sensitivity (Sn%), specificity (Sp%), positive and negative likelihood ratios were calculated for the identified cut-off values. Patients with bacterial sepsis were older than patients with viral sepsis (p < 0.001), with no differences regarding gender. Subsequently to ROC analysis, procalcitonin had excellent discriminative ability for bacterial sepsis diagnosis with an area under the curve (AUC) of 0.92 (cut-off value of >1.49 ng/mL; Sn = 76.6%, Sp = 94.2%), followed by RDW% with an AUC = 0.87 (cut-off value >14.8%; Sn = 80.7%, Sp = 85.5%). Leukocytes, monocytes and neutrophils had good discriminative ability with AUCs between 0.76-0.78 (p < 0.001), while other hematological parameters had fair or no discriminative ability. Lastly, procalcitonin value was strongly correlated with disease severity in both types of sepsis (p < 0.001). Procalcitonin and RDW% had the best discriminative ability between bacterial and viral sepsis, followed by leukocytes, monocytes and neutrophils. Procalcitonin is a marker of disease severity regardless of sepsis type.

A Novel COVID-19 Severity Score is Associated With Survival in Patients Undergoing Percutaneous Dilational Tracheostomy

INTRODUCTION

Tracheostomy in patients with COVID-19 is a controversial and difficult clinical decision. We hypothesized that a recently validated COVID-19 Severity Score (CSS) would be associated with survival in patients considered for tracheostomy.

METHODS

We reviewed 77 mechanically ventilated COVID-19 patients evaluated for decision for percutaneous dilational tracheostomy (PDT) from March to June 2020 at a public tertiary care center. Decision for PDT was based on clinical judgment of the screening surgeons. The CSS was retrospectively calculated using mean biomarker values from admission to time of PDT consult. Our primary outcome was survival to discharge, and all patient charts were reviewed through August 31, 2021. ROC curve and Youden index were used to estimate an optimal cut-point for survival.

RESULTS

The mean CSS for 42 survivors significantly differed from that of 35 nonsurvivors (CSS 52 versus 66, P = 0.003). The Youden index returned an optimal CSS of 55 (95% confidence interval 43-72), which was associated with a sensitivity of 0.8 and a specificity of 0.6. The median CSS was 40 (interquartile range 27, 49) in the lower CSS (<55) group and 72 (interquartile range 66, 93) in the high CSS (≥55 group). Eighty-seven percent of lower CSS patients underwent PDT, with 74% survival, whereas 61% of high CSS patients underwent PDT, with only 41% surviving. Patients with high CSS had 77% lower odds of survival (odds ratio = 0.2, 95% confidence interval 0.1-0.7).

CONCLUSIONS

Higher CSS was associated with decreased survival in patients evaluated for PDT, with a score ≥55 predictive of mortality. The novel CSS may be a useful adjunct in determining which COVID-19 patients will benefit from tracheostomy. Further prospective validation of this tool is warranted.

Safety and efficacy of methylprednisolone versus dexamethasone in critically ill patients with COVID-19 acute respiratory distress syndrome: a retrospective study

Background

Corticosteroids (CSs), specifically dexamethasone (DEX), are the treatment of choice for severe acute respiratory distress syndrome (ARDS) due to COVID-19 pneumonia (CARDS). However, data from both ARDS and relatively small CARDS clinical trials have suggested improved outcomes with methylprednisolone (MP) versus DEX. The objective of this retrospective cohort study was to compare the safety and effectiveness of MP and DEX in critically ill CARDS patients.

Methods

The study cohort included CARDS patients admitted to a tertiary referral intensive care unit (ICU) between April and September 2020 who received at least 5 days of CSs for CARDS.

Results

The cohort was notable for a high severity of illness (overall, 88.5% of patients required mechanical ventilation and 16% required vasopressors on admission). The DEX group (n = 62) was significantly older with a higher illness severity [Sequential Organ Failure Assessment (SOFA) 6 (4.75-8) versus 4.5 (3-7), p = 0.008], while the MP group (n = 51) received significantly more loading doses [19 (37.3%) versus 4 (6.5%), p < 0.0001]. MP was associated with a shorter time to intubation and more rapid progression to mortality [days to death: 18 (15-23) versus 27 (15-34), p = 0.026]. After correction for baseline imbalances in age and SOFA score, DEX was associated with improved mortality at 90 days compared with MP [hazard ratio (HR) = 0.43, 95% confidence interval (CI) = 0.23-0.80, p = 0.008]. However, there were no differences between rates of secondary infections during hospitalization or insulin requirements at 7 and 14 days.

Conclusion

In this cohort of critically ill CARDS, choice of CS was associated with mortality but not adverse event profile, and thus warrants further investigation.

Rationing scarce healthcare capacity: A study of the ventilator allocation guidelines during the COVID-19 pandemic

In the United States, even though national guidelines for allocating scarce healthcare resources are lacking, 26 states have specific ventilator allocation guidelines to be invoked in case of a shortage. While several states developed their guidelines in response to the recent COVID-19 pandemic, New York State developed these guidelines in 2015 as "pandemic influenza is a foreseeable threat, one that we cannot ignore." The primary objective of this study is to assess the existing procedures and priority rules in place for allocating/rationing scarce ventilator capacity and propose alternative (and improved) priority schemes. We first build machine learning models using inpatient records of COVID-19 patients admitted to New York-Presbyterian/Columbia University Irving Medical Center and an affiliated community health center to predict survival probabilities as well as ventilator length-of-use. Then, we use the resulting point estimators and their uncertainties as inputs for a multiclass priority queueing model with abandonments to assess three priority schemes: (i) SOFA-P (Sequential Organ Failure Assessment based prioritization), which most closely mimics the existing practice by prioritizing patients with sufficiently low SOFA scores; (ii) ISP (incremental survival probability), which assigns priority based on patient-level survival predictions; and (iii) ISP-LU (incremental survival probability per length-of-use), which takes into account survival predictions and resource use duration. Our findings highlight that our proposed priority scheme, ISP-LU, achieves a demonstrable improvement over the other two alternatives. Specifically, the expected number of survivals increases and death risk while waiting for ventilator use decreases. We also show that ISP-LU is a robust priority scheme whose implementation yields a Pareto-improvement over both SOFA-P and ISP in terms of maximizing saved lives after mechanical ventilation while limiting racial disparity in access to the priority queue.

Coagulation Disorders in Sepsis and COVID-19-Two Sides of the Same Coin? A Review of Inflammation-Coagulation Crosstalk in Bacterial Sepsis and COVID-19

Sepsis is a major cause of morbidity and mortality worldwide. Sepsis-associated coagulation disorders are involved in the pathogenesis of multiorgan failure and lead to a subsequently worsening prognosis. Alongside the global impact of the COVID-19 pandemic, a great number of research papers have focused on SARS-CoV-2 pathogenesis and treatment. Significant progress has been made in this regard and coagulation disturbances were once again found to underlie some of the most serious adverse outcomes of SARS-CoV-2 infection, such as acute lung injury and multiorgan dysfunction. In the attempt of untangling the mechanisms behind COVID-19-associated coagulopathy (CAC), a series of similarities with sepsis-induced coagulopathy (SIC) became apparent. Whether they are, in fact, the same disease has not been established yet. The clinical picture of CAC shows the unique feature of an initial phase of intravascular coagulation confined to the respiratory system. Only later on, patients can develop a clinically significant form of systemic coagulopathy, possibly with a consumptive pattern, but, unlike SIC, it is not a key feature. Deepening our understanding of CAC pathogenesis has to remain a major goal for the research community, in order to design and validate accurate definitions and classification criteria.

Triage Policies at U.S. Hospitals with Pediatric Intensive Care Units

OBJECTIVES

To characterize the prevalence and content of pediatric triage policies.

METHODS

We surveyed and solicited policies from U.S. hospitals with pediatric intensive care units. Policies were analyzed using qualitative methods and coded by 2 investigators.

RESULTS

Thirty-four of 120 institutions (28%) responded. Twenty-five (74%) were freestanding children's hospitals and 9 (26%) were hospitals within a hospital. Nine (26%) had approved policies, 9 (26%) had draft policies, 5 (14%) were developing policies, and 7 (20%) did not have policies. Nineteen (68%) institutions shared their approved or draft policy. Eight (42%) of those policies included neonates. The polices identified 0 to 5 (median 2) factors to prioritize patients. The most common factors were short- (17, 90%) and long- (14, 74%) term predicted mortality. Pediatric scoring systems included Pediatric Logistic Organ Dysfunction-2 (12, 63%) and Score for Neonatal Acute Physiology and Perinatal Extensions-II (4, 21%). Thirteen (68%) policies described a formal algorithm. The most common tiebreakers were random/lottery (10, 71%) and life cycles (9, 64%). The majority (15, 79%) of policies specified the roles of triage team members and 13 (68%) precluded those participating in patient care from making triage decisions.

CONCLUSIONS

While many institutions still do not have pediatric triage policies, there appears to be a trend among those with policies to utilize a formal algorithm that focuses on short- and long-term predicted mortality and that incorporates age-appropriate scoring systems. Additional work is needed to expand access to pediatric-specific policies, to validate scoring systems, and to address health disparities.

The draw of the few: the challenge of crisis guidelines for extremely scarce resources

The COVID-19 pandemic has focused considerable attention on crisis standards of care (CSCs). Most public CSCs at present are effective tools for allocating scarce but not uncommon resources (like ventilators and dialysis machines). However, a different set of challenges arise with regard to extremely scarce resources (ESRs), where the number of patients in need may exceed the availability of the intervention by magnitudes of hundreds or thousands. Using the allocation of extracorporeal membrane oxygenation machines as a case study, this paper argues for a different set of CSCs specifically for ESRs and explores four principles (transparency, uniformity, equity and impact) that should shape such guidelines.

Preparing for the Worst-Case Scenario in a Pandemic: Intensivists Simulate Prioritization and Triage of Scarce ICU Resources

OBJECTIVES

Simulation and evaluation of a prioritization protocol at a German university hospital using a convergent parallel mixed methods design.

DESIGN

Prospective single-center cohort study with a quantitative analysis of ICU patients and qualitative content analysis of two focus groups with intensivists.

SETTING

Five ICUs of internal medicine and anesthesiology at a German university hospital.

PATIENTS

Adult critically ill ICU patients ( n = 53).

INTERVENTIONS

After training the attending senior ICU physicians ( n = 13) in rationing, an impending ICU congestion was simulated. All ICU patients were rated according to their likelihood to survive their acute illness (good-moderate-unfavorable). From each ICU, the two patients with the most unfavorable prognosis ( n = 10) were evaluated by five prioritization teams for triage.

MEASUREMENTS AND MAIN RESULTS

Patients nominated for prioritization visit ( n = 10) had higher Sequential Organ Failure Assessment scores and already a longer stay at the hospital and on the ICU compared with the other patients. The order within this worst prognosis group was not congruent between the five teams. However, an in-hospital mortality of 80% confirmed the reasonable match with the lowest predicted probability of survival. Qualitative data highlighted the tremendous burden of triage and the need for a team-based consensus-oriented decision-making approach to ensure best possible care and to support professionals. Transparent communication within the teams, the hospital, and to the public was seen as essential for prioritization implementation.

CONCLUSIONS

To mitigate potential bias and to reduce the emotional burden of triage, a consensus-oriented, interdisciplinary, and collaborative approach should be implemented. Prognostic comparative assessment by intensivists is feasible. The combination of long-term ICU stay and consistently high Sequential Organ Failure Assessment scores resulted in a greater risk for triage in patients. It remains challenging to reliably differentiate between patients with very low chances to survive and requires further conceptual and empirical research.

COVID-19 machine learning model predicts outcomes in older patients from various European countries, between pandemic waves, and in a cohort of Asian, African, and American patients

BACKGROUND

COVID-19 remains a complex disease in terms of its trajectory and the diversity of outcomes rendering disease management and clinical resource allocation challenging. Varying symptomatology in older patients as well as limitation of clinical scoring systems have created the need for more objective and consistent methods to aid clinical decision making. In this regard, machine learning methods have been shown to enhance prognostication, while improving consistency. However, current machine learning approaches have been limited by lack of generalisation to diverse patient populations, between patients admitted at different waves and small sample sizes.

OBJECTIVES

We sought to investigate whether machine learning models, derived on routinely collected clinical data, can generalise well i) between European countries, ii) between European patients admitted at different COVID-19 waves, and iii) between geographically diverse patients, namely whether a model derived on the European patient cohort can be used to predict outcomes of patients admitted to Asian, African and American ICUs.

METHODS

We compare Logistic Regression, Feed Forward Neural Network and XGBoost algorithms to analyse data from 3,933 older patients with a confirmed COVID-19 diagnosis in predicting three outcomes, namely: ICU mortality, 30-day mortality and patients at low risk of deterioration. The patients were admitted to ICUs located in 37 countries, between January 11, 2020, and April 27, 2021.

RESULTS

The XGBoost model derived on the European cohort and externally validated in cohorts of Asian, African, and American patients, achieved AUC of 0.89 (95% CI 0.89-0.89) in predicting ICU mortality, AUC of 0.86 (95% CI 0.86-0.86) for 30-day mortality prediction and AUC of 0.86 (95% CI 0.86-0.86) in predicting low-risk patients. Similar AUC performance was achieved also when predicting outcomes between European countries and between pandemic waves, while the models showed high calibration quality. Furthermore, saliency analysis showed that FiO2 values of up to 40% do not appear to increase the predicted risk of ICU and 30-day mortality, while PaO2 values of 75 mmHg or lower are associated with a sharp increase in the predicted risk of ICU and 30-day mortality. Lastly, increase in SOFA scores also increase the predicted risk, but only up to a value of 8. Beyond these scores the predicted risk remains consistently high.

CONCLUSION

The models captured both the dynamic course of the disease as well as similarities and differences between the diverse patient cohorts, enabling prediction of disease severity, identification of low-risk patients and potentially supporting effective planning of essential clinical resources.

TRIAL REGISTRATION NUMBER

NCT04321265.

Machine learning-based derivation and external validation of a tool to predict death and development of organ failure in hospitalized patients with COVID-19

COVID-19 mortality risk stratification tools could improve care, inform accurate and rapid triage decisions, and guide family discussions regarding goals of care. A minority of COVID-19 prognostic tools have been tested in external cohorts. Our objective was to compare machine learning algorithms and develop a tool for predicting subsequent clinical outcomes in COVID-19. We conducted a retrospective cohort study that included hospitalized patients with COVID-19 from March 2020 to March 2021. Seven Hundred Twelve consecutive patients from University of Washington and 345 patients from Tongji Hospital in China were included. We applied three different machine learning algorithms to clinical and laboratory data collected within the initial 24 h of hospital admission to determine the risk of in-hospital mortality, transfer to the intensive care unit, shock requiring vasopressors, and receipt of renal replacement therapy. Mortality risk models were derived, internally validated in UW and externally validated in Tongji Hospital. The risk models for ICU transfer, shock and RRT were derived and internally validated in the UW dataset but were unable to be externally validated due to a lack of data on these outcomes. Among the UW dataset, 122 patients died (17%) during hospitalization and the mean days to hospital mortality was 15.7 +/- 21.5 (mean +/- SD). Elastic net logistic regression resulted in a C-statistic for in-hospital mortality of 0.72 (95% CI, 0.64 to 0.81) in the internal validation and 0.85 (95% CI, 0.81 to 0.89) in the external validation set. Age, platelet count, and white blood cell count were the most important predictors of mortality. In the sub-group of patients > 50 years of age, the mortality prediction model continued to perform with a C-statistic of 0.82 (95% CI:0.76,0.87). Prediction models also performed well for shock and RRT in the UW dataset but functioned with lower accuracy for ICU transfer. We trained, internally and externally validated a prediction model using data collected within 24 h of hospital admission to predict in-hospital mortality on average two weeks prior to death. We also developed models to predict RRT and shock with high accuracy. These models could be used to improve triage decisions, resource allocation, and support clinical trial enrichment.

The Clinical Implementation of NEWS, SOFA, and CALL Scores in Predicting the In-Hospital Outcome of Severe or Critical COVID-19 Patients

OBJECTIVE

To date, there is no specific validated coronavirus disease 2019 score to assess the disease severity. This study aimed to evaluate the performance of the National Early Warning Score, Sequential Organ Failure Assessment, and Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase scores in predicting the in-hospital outcome of critical or severe coronavirus disease 2019 patients.

MATERIALS AND METHODS

Single-centered analytical study was carried out in the coronavirus disease 2019 high dependency unit from April to August 2020. National Early Warning Score, Sequential Organ Failure Assessment, and Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase scores were calculated for each critical to severely ill coronavirus disease 2019 patient. The diagnostic accuracy of these 3 scores in determining the in-hospital outcome of coronavirus disease 2019 patients was assessed by area under the receiver operating characteristic curve. The cut-off value of each score along with sensitivity, specificity, and positive and negative likelihood ratio were calculated by Youden index. Predictors of outcome in coronavirus disease 2019 patients were analyzed by Cox-regression analysis.

RESULTS

The area under the curve was highest for the Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase score (area under the curve=0.85) while the Sequential Organ Failure Assessment score had an area under the curve of 0.72. The cut-off values for National Early Warning Score score was 8 (sensitivity=72.34%, specificity=76.10%), Sequential Organ Failure Assessment score was 3 (sensitivity=68.97%, specificity=67.42%), and Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase score was 8 (sensitivity=88.89%, specificity=66.67%). The pairwise comparison showed that the difference between the area under the curve of these 3 scores was statistically insignificant (P > .05). The rate of mortality and invasive ventilation was significantly high in groups with high National Early Warning Score, Sequential Organ Failure Assessment, and Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase scores (P > .0001). These 3 scores, age, low platelets, and high troponin-T levels were found to be statistically significant predictors of outcome Conclusion:Comorbidity-Age-Lymphocyte count-Lactate dehydrogenase score had a good area under the curve, the highest sensitivity of its cut-off value, required only 4 parameters, and is easy to calculate so it may be a better tool among the 3 scores in outcome prediction for coronavirus disease 2019 patients.

Contributing Factors to Increased Left Ventricular End-Diastolic Volume in COVID-19 ICU Patients in Sanglah Hospital: A Study on Galectin-3

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a respiratory disease that has become the largest pandemic and also could put the heart at risk of dysfunction. Galectin-3 is involved in the inflammatory process that continues with remodeling and eventually fibrosis. Using galectin-3 examination, we could predict the possible worsening of heart function and evaluate data on influencing factors for increased left ventricular end-diastolic volume (LVEDV) which could later progress to heart failure. METHODS: This is an observational prospective analytic study in the COVID-19 ICU of Sanglah Hospital, Bali, Indonesia. The study was conducted from June to October 2021. All research subjects had their blood samples taken for galectin-3 levels examination using enzyme-linked immunosorbent assay (ELISA). Subjects were also evaluated for left ventricular end-diastolic volume (LVEDV) with echocardiography, SOFA scores, and troponin I levels. Subjects were treated with COVID-19 standard protocol established by the Ministry of Health. After 72 h post-admission, subjects were re-examined for galectin-3 levels and LVEDV. Data were analyzed using STATA™. RESULTS: A total of 45 research subjects were analyzed. Bivariate analysis of the difference of galectin-3 and LVEDV was shown to be insignificant (r = 0.08), no correlation was found between galectin-3 level and LVEDV on ICU admission (r = 0.191), and no correlation found between galectin-3 level and LVEDV after 72 h of hospitalization (r=0.197). Multivariate analysis also showed that none of the variables, namely, difference of galectin-3 level, age, gender, troponin I, SOFA, and Charlson scores had statistically significant correlation with LVEDV (p < 0.05). CONCLUSION: No significant correlation was found between galectin-3 level and an increase in LVEDV.

Development and Internal Validation of a New Prognostic Model Powered to Predict 28-Day All-Cause Mortality in ICU COVID-19 Patients-The COVID-SOFA Score

Background: The sequential organ failure assessment (SOFA) score has poor discriminative ability for death in severely or critically ill patients with Coronavirus disease 2019 (COVID-19) requiring intensive care unit (ICU) admission. Our aim was to create a new score powered to predict 28-day mortality. Methods: Retrospective, observational, bicentric cohort study including 425 patients with COVID-19 pneumonia, acute respiratory failure and SOFA score ≥ 2 requiring ICU admission for ≥72 h. Factors with independent predictive value for 28-day mortality were identified after stepwise Cox proportional hazards (PH) regression. Based on the regression coefficients, an equation was computed representing the COVID-SOFA score. Discriminative ability was tested using receiver operating characteristic (ROC) analysis, concordance statistics and precision-recall curves. This score was internally validated. Results: Median (Q1−Q3) age for the whole sample was 64 [55−72], with 290 (68.2%) of patients being male. The 28-day mortality was 54.58%. After stepwise Cox PH regression, age, neutrophil-to-lymphocyte ratio (NLR) and SOFA score remained in the final model. The following equation was computed: COVID-SOFA score = 10 × [0.037 × Age + 0.347 × ln(NLR) + 0.16 × SOFA]. Harrell’s C-index for the COVID-SOFA score was higher than the SOFA score alone for 28-day mortality (0.697 [95% CI; 0.662−0.731] versus 0.639 [95% CI: 0.605−0.672]). Subsequently, the prediction error rate was improved up to 16.06%. Area under the ROC (AUROC) was significantly higher for the COVID-SOFA score compared with the SOFA score for 28-day mortality: 0.796 [95% CI: 0.755−0.833] versus 0.699 [95% CI: 0.653−0.742, p < 0.001]. Better predictive value was observed with repeated measurement at 48 h after ICU admission. Conclusions: The COVID-SOFA score is better than the SOFA score alone for 28-day mortality prediction. Improvement in predictive value seen with measurements at 48 h after ICU admission suggests that the COVID-SOFA score can be used in a repetitive manner. External validation is required to support these results.

COVID-19 Time of Intubation Mortality Evaluation (C-TIME): A system for predicting mortality of patients with COVID-19 pneumonia at the time they require mechanical ventilation

Background

An accurate system to predict mortality in patients requiring intubation for COVID-19 could help to inform consent, frame family expectations and assist end-of-life decisions.

Research objective

To develop and validate a mortality prediction system called C-TIME (COVID-19 Time of Intubation Mortality Evaluation) using variables available before intubation, determine its discriminant accuracy, and compare it to acute physiology and chronic health evaluation (APACHE IVa) and sequential organ failure assessment (SOFA).

Methods

A retrospective cohort was set in 18 medical-surgical ICUs, enrolling consecutive adults, positive by SARS-CoV 2 RNA by reverse transcriptase polymerase chain reaction or positive rapid antigen test, and undergoing endotracheal intubation. All were followed until hospital discharge or death. The combined outcome was hospital mortality or terminal extubation with hospice discharge. Twenty-five clinical and laboratory variables available 48 hours prior to intubation were entered into multiple logistic regression (MLR) and the resulting model was used to predict mortality of validation cohort patients. Area under the receiver operating curve (AUROC) was calculated for C-TIME, APACHE IVa and SOFA.

Results

The median age of the 2,440 study patients was 66 years; 61.6 percent were men, and 50.5 percent were Hispanic, Native American or African American. Age, gender, COPD, minimum mean arterial pressure, Glasgow Coma scale score, and PaO₂/FiO₂ ratio, maximum creatinine and bilirubin, receiving factor Xa inhibitors, days receiving non-invasive respiratory support and days receiving corticosteroids prior to intubation were significantly associated with the outcome variable. The validation cohort comprised 1,179 patients. C-TIME had the highest AUROC of 0.75 (95%CI 0.72–0.79), vs 0.67 (0.64–0.71) and 0.59 (0.55–0.62) for APACHE and SOFA, respectively (Chi² P<0.0001).

Conclusions

C-TIME is the only mortality prediction score specifically developed and validated for COVID-19 patients who require mechanical ventilation. It has acceptable discriminant accuracy and goodness-of-fit to assist decision-making just prior to intubation. The C-TIME mortality prediction calculator can be freely accessed on-line at https://phoenixmed.arizona.edu/ctime.

The role of a noninvasive index 'Spo2/ Fio2' in predicting mortality among patients with COVID-19 pneumonia

INTRODUCTION

Noninvasive risk assessment is crucial in patients with COVID-19 in emergency department. Since limited data is known about the role of noninvasive parameters, we aimed to evaluate the role of a noninvasive parameter 'SpO2/FiO2' in independently predicting 30-day mortality in patients with COVID-19 and its prognostic utility in combination with a noninvasive score 'CRB-65'.

METHODS

A retrospective study was performed in a tertiary training and research hospital, which included 272 patients with COVID-19 pneumonia diagnosed with polymerase chain reaction in emergency department. Data on characteristics, vital signs, and laboratory parameters were recorded from electronic medical records. The primary outcome of the study was 30-day mortality, and we assessed the discriminative ability of SpO2/FiO2 in predicting mortality in patients with COVID-19 pneumonia and its prognostic utility in combination with conventional pneumonia risk assessment scores.

RESULTS

Multivariate analysis revealed that only SpO2/FiO2 level was found to be an independent parameter associated with 30-day mortality (OR:0.98, 95% CI: 0.98-0.99, p = 0.003). PSI and CURB-65 were found to be better scores than CRB-65 in predicting 30-day mortality (AUC: 0.79 vs 0.72, p = 0.04; AUC: 0.76 vs 0.72, p = 0.01 respectively). Both SpO2/FiO2 combined with CRB-65 and SpO2/FiO2 combined with CURB-65 have good discriminative ability and seemed to be more favorable than PSI in predicting 30-days mortality (AUC: 0.83 vs 0.75; AUC: 0.84 vs 0.75), however no significant difference was found (p = 0.21 and p = 0.06, respectively).

CONCLUSION

SpO2/FiO2 is a promising index in predicting mortality. Addition of SpO2/FiO2 to CRB-65 improved the role of CRB-65 alone, however it performed similar to PSI. The combined noninvasive model of SpO2/FiO2 and CRB-65 may help physicians quickly stratify COVID-19 patients on admission, which is expected to be particularly important in hospitals still stressed by pandemic volumes.

Prognostic Utility of Procalcitonin, Presepsin, and the VACO Index for Predicting 30-day Mortality in Hospitalized COVID-19 Patients

Background

Biomarkers and clinical indices have been investigated for predicting mortality in patients with coronavirus disease (COVID-19). We explored the prognostic utility of procalcitonin (PCT), presepsin, and the Veterans Health Administration COVID-19 (VACO) index for predicting 30-day-mortality in COVID-19 patients.

Methods

In total, 54 hospitalized COVID-19 patients were enrolled. PCT and presepsin levels were measured using the Elecsys BRAHMS PCT assay (Roche Diagnostics GmbH, Mannheim, Germany) and HISCL Presepsin assay (Sysmex, Kobe, Japan), respectively. The VACO index was calculated based on age, sex, and comorbidities. PCT and presepsin levels and the VACO index were compared using ROC curve, Kaplan-Meier method, and reclassification analysis for the 30-day mortality.

Results

ROC curve analysis was used to measure PCT and presepsin levels and the VACO index to predict 30-day mortality; the optimal cut-off values were 0.138 ng/mL for PCT, 717 pg/mL for presepsin, and 12.1% for the VACO index. On Kaplan-Meier survival analysis, hazard ratios (95% confidence interval) were 15.9 (4.1-61.3) for PCT, 26.3 (6.4-108.0) for presepsin, and 6.0 (1.7-21.1) for the VACO index. On reclassification analysis, PCT and presepsin in addition to the VACO index significantly improved the prognostic value of the index.

Conclusions

This study demonstrated the prognostic utility of measuring PCT and presepsin levels and the VACO index in COVID-19 patients. The biomarkers in addition to the clinical index were more useful than the index alone for predicting clinical outcomes in COVID-19 patients.

Quantifying Utilitarian Outcomes to Inform Triage Ethics: Simulated Performance of a Ventilator Triage Protocol under Sars-CoV-2 Pandemic Surge Conditions

BACKGROUND

Equitable protocols to triage life-saving resources must be specified prior to shortages in order to promote transparency, trust and consistency. How well proposed utilitarian protocols perform to maximize lives saved is unknown. We aimed to estimate the survival rates that would be associated with implementation of the New York State 2015 guidelines for ventilator triage, and to compare them to a first-come-first-served triage method.

METHODS

We constructed a simulation model based on a modified version of the New York State 2015 guidelines compared to a first-come-first-served method under various hypothetical ventilator shortages. We included patients with SARs-CoV-2 infection admitted with respiratory failure requiring mechanical ventilation to three acute care hospitals in New York from 3/01/2020 and 5/27/2020. We estimated (1) survival rates, (2) number of excess deaths, (3) number of patients extubated early or not allocated a ventilator due to capacity constraints, (4) survival rates among patients not allocated a ventilator at triage or extubated early due to capacity constraints.

RESULTS

807 patients were included in the study. The simulation model based on a modified New York State policy did not decrease mortality, excess death or exclusion from ventilators compared to the first-come-first-served policy at every ventilator capacity we tested using COVID-19 surge cohort patients. Survival rates were similar at all the survival probabilities estimated. At the lowest ventilator capacity, the modified New York State policy has an estimated survival of 28.5% (CI: 28.4-28.6), compared to 28.1% (CI: 27.7-28.5) for the first-come-first-served policy.

CONCLUSIONS

This simulation of a modified New York State guideline-based triage protocol revealed limitations in achieving the utilitarian goals these protocols are designed to fulfill. Quantifying these outcomes can inform a better balance among competing moral aims.

Epidemiology of Organ Failure Before and During COVID-19 Pandemic Surge Conditions

BACKGROUND

Understanding the distribution of organ failure before and during the COVID-19 pandemic surge can provide a deeper understanding of how the pandemic strained health care systems and affected outcomes.

OBJECTIVE

To assess the distribution of organ failure in 3 New York City hospitals during the COVID-19 pandemic.

METHODS

A retrospective cohort study of adult admissions across hospitals from February 1, 2020, through May 31, 2020, was conducted. The cohort was stratified into those admitted before March 17, 2020 (prepandemic) and those admitted on or after that date (SARS-CoV-2-positive and non-SARS-CoV-2). Sequential Organ Failure Assessment scores were computed every 2 hours for each admission.

RESULTS

A total of 1 794 975 scores were computed for 20 704 admissions. Before and during the pandemic, renal failure was the most common type of organ failure at admission and respiratory failure was the most common type of hospital-onset organ failure. The SARS-CoV-2-positive group showed a 231% increase in respiratory failure compared with the prepandemic group. More than 65% of hospital-onset organ failure in the prepandemic group and 83% of hospital-onset respiratory failure in the SARS-CoV-2-positive group occurred outside intensive care units. The SARS-CoV-2-positive group showed a 341% increase in multiorgan failure compared with the prepandemic group. Compared with the prepandemic and non-SARS-CoV-2 patients, SARS-CoV-2-positive patients had significantly higher mortality for the same admission and maximum organ failure score.

CONCLUSION

Most hospital-onset organ failure began outside intensive care units, with a marked increase in multiorgan failure during pandemic surge conditions and greater hospital mortality for the severity of organ failure.

How Common SOFA and Ventilator Time Trial Criteria Would Have Performed During the COVID-19 Pandemic: An Observational Simulated Cohort Study

OBJECTIVES

To evaluate how key aspects of New York State Ventilator Allocation Guidelines (NYSVAG)-Sequential Organ Failure Assessment score criteria and ventilator time trials -might perform with respect to the frequency of ventilator reallocation and survival to hospital discharge in a simulated cohort of coronavirus disease (COVID-19) patients.

METHODS

Single center retrospective observational and simulation cohort study of 884 critically ill COVID-19 patients undergoing ventilator allocation per NYSVAG.

RESULTS

In total, 742 patients (83.9%) would have had their ventilator reallocated during the 11-day observation period, 280 (37.7%) of whom would have otherwise survived to hospital discharge if provided with a ventilator. Only 65 (18.1%) of the observed surviving patients would have survived by NYSVAG. Extending ventilator time trials from 2 to 5 days resulted in a 49.2% increase in simulated survival to discharge.

CONCLUSIONS

In the setting of a protracted respiratory pandemic, implementation of NYSVAG or similar protocols could lead to a high degree of ventilator reallocation, including withdrawal from patients who might otherwise survive. Longer ventilator time trials might lead to improved survival for COVID-19 patients given their protracted respiratory failure. Further studies are needed to understand the survival of patients receiving reallocated ventilators to determine whether implementation of NYSVAG would improve overall survival.

Metagenomic assessment of gut microbial communities and risk of severe COVID-19

The gut microbiome is a critical modulator of host immunity and is linked to the immune response to respiratory viral infections. However, few studies have gone beyond describing broad compositional alterations in severe COVID-19, defined as acute respiratory or other organ failure. We profiled 127 hospitalized patients with COVID-19 (n=79 with severe COVID-19 and 48 with moderate) who collectively provided 241 stool samples from April 2020 to May 2021 to identify links between COVID-19 severity and gut microbial taxa, their biochemical pathways, and stool metabolites. 48 species were associated with severe disease after accounting for antibiotic use, age, sex, and various comorbidities. These included significant in-hospital depletions of Fusicatenibacter saccharivorans and Roseburia hominis, each previously linked to post-acute COVID syndrome or “long COVID”, suggesting these microbes may serve as early biomarkers for the eventual development of long COVID. A random forest classifier achieved excellent performance when tasked with predicting whether stool was obtained from patients with severe vs. moderate COVID-19. Dedicated network analyses demonstrated fragile microbial ecology in severe disease, characterized by fracturing of clusters and reduced negative selection. We also observed shifts in predicted stool metabolite pools, implicating perturbed bile acid metabolism in severe disease. Here, we show that the gut microbiome differentiates individuals with a more severe disease course after infection with COVID-19 and offer several tractable and biologically plausible mechanisms through which gut microbial communities may influence COVID-19 disease course. Further studies are needed to validate these observations to better leverage the gut microbiome as a potential biomarker for disease severity and as a target for therapeutic intervention.

Pathogen Species Is Associated With Mortality in Nosocomial Bloodstream Infection in Patients With COVID-19

Background

The epidemiology of nosocomial bloodstream infections (NBSIs) in patients with coronavirus disease 2019 (COVID-19) is poorly understood, due in part to substantial disease heterogeneity resulting from multiple potential pathogens.

Methods

We identified risk factors for NBSIs and examined the association between NBSIs and mortality in a retrospective cohort of patients hospitalized with COVID-19 in 2 New York City hospitals during the height of the pandemic. We adjusted for the potential effects of factors likely to confound that association, including age, race, illness severity upon admission, and underlying health status.

Results

Between January 1 and October 1, 2020, 1403 patients had a positive blood culture, and 79 and 101 met the stringent criteria for NBSI among non-COVID-19 and COVID-19 patients, respectively. NBSIs occurred almost exclusively among patients who were severely ill with COVID-19 at hospital admission. NBSIs were associated with elevated mortality, even after adjusting for baseline differences in COVID-19 illness (55% cases vs 45% controls; P = .13). Mortality was concentrated in patients with early-onset pneumonia caused by S. aureus and gram-negative bacteria. Less virulent Candida (49%) and Enterococcus (12%) species were the predominant cause of NBSI in the latter stages of hospitalization, after antibiotic treatment and COVID-19 treatments that attenuate immune response. Most Enterococcus and Candida infections did not have an identifiable source and were not associated with common risk factors for infection by these organisms.

Conclusions

Pathogen species and mortality exhibited temporal differences. Early recognition of risk factors among COVID-19 patients could potentially decrease NBSI-associated mortality through early COVID-19 and antimicrobial treatment.

Risk of Death in Comorbidity Subgroups of Hospitalized COVID-19 Patients Inferred by Routine Laboratory Markers of Systemic Inflammation on Admission: A Retrospective Study

Our study objective was to construct models using 20 routine laboratory parameters on admission to predict disease severity and mortality risk in a group of 254 hospitalized COVID-19 patients. Considering the influence of confounding factors in this single-center study, we also retrospectively assessed the correlations between the risk of death and the routine laboratory parameters within individual comorbidity subgroups. In multivariate regression models and by ROC curve analysis, a model of three routine laboratory parameters (AUC 0.85; 95% CI: 0.79–0.91) and a model of six laboratory factors (AUC 0.86; 95% CI: 0.81–0.91) were able to predict severity and mortality of COVID-19, respectively, compared with any other individual parameter. Hierarchical cluster analysis showed that inflammatory laboratory markers grouped together in three distinct clusters including positive correlations: WBC with NEU, NEU with neutrophil-to-lymphocyte ratio (NLR), NEU with systemic immune-inflammation index (SII), NLR with SII and platelet-to-lymphocyte ratio (PLR) with SII. When analyzing the routine laboratory parameters in the subgroups of comorbidities, the risk of death was associated with a common set of laboratory markers of systemic inflammation. Our results have shown that a panel of several routine laboratory parameters recorded on admission could be helpful for early evaluation of the risk of disease severity and mortality in COVID-19 patients. Inflammatory markers for mortality risk were similar in the subgroups of comorbidities, suggesting the limited effect of confounding factors in predicting COVID-19 mortality at admission.

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.

Development and Structure of an Accurate Machine Learning Algorithm to Predict Inpatient Mortality and Hospice Outcomes in the Coronavirus Disease 2019 Era

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has challenged the accuracy and racial biases present in traditional mortality scores. An accurate prognostic model that can be applied to hospitalized patients irrespective of race or COVID-19 status may benefit patient care.

RESEARCH DESIGN

This cohort study utilized historical and ongoing electronic health record features to develop and validate a deep-learning model applied on the second day of admission predicting a composite outcome of in-hospital mortality, discharge to hospice, or death within 30 days of admission. Model features included patient demographics, diagnoses, procedures, inpatient medications, laboratory values, vital signs, and substance use history. Conventional performance metrics were assessed, and subgroup analysis was performed based on race, COVID-19 status, and intensive care unit admission.

SUBJECTS

A total of 35,521 patients hospitalized between April 2020 and October 2020 at a single health care system including a tertiary academic referral center and 9 community hospitals.

RESULTS

Of 35,521 patients, including 9831 non-White patients and 2020 COVID-19 patients, 2838 (8.0%) met the composite outcome. Patients who experienced the composite outcome were older (73 vs. 61 y old) with similar sex and race distributions between groups. The model achieved an area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.88, 0.91) and an average positive predictive value of 0.46 (0.40, 0.52). Model performance did not differ significantly in White (0.89) and non-White (0.90) subgroups or when grouping by COVID-19 status and intensive care unit admission.

CONCLUSION

A deep-learning model using large-volume, structured electronic health record data can effectively predict short-term mortality or hospice outcomes on the second day of admission in the general inpatient population without significant racial bias.