Comparison of CRB-65 and quick sepsis-related organ failure assessment for predicting the need for intensive respiratory or vasopressor support in patients with COVID-19

Low CRB-65 Scores Effectively Rule out Adverse Clinical Outcomes in COVID-19 Irrespective of Chest Radiographic Abnormalities

Background: CRB-65 (Confusion; Respiratory rate ≥ 30/min; Blood pressure ≤ 90/60 mmHg; age ≥ 65 years) is a risk score for prognosticating patients with COVID-19 pneumonia. However, a significant proportion of COVID-19 patients have normal chest X-rays (CXRs). The influence of CXR abnormalities on the prognostic value of CRB-65 is unknown, limiting its wider applicability. Methods: We assessed the influence of CXR abnormalities on the prognostic value of CRB-65 in COVID-19. Results: In 589 study patients (71 years (IQR: 57-83); 57% males), 186 (32%) had normal CXRs. On ROC analysis, CRB-65 performed similarly in patients with normal vs. abnormal CXRs for predicting inpatient mortality (AUC 0.67 ± 0.05 vs. 0.69 ± 0.03). In patients with normal CXRs, a CRB-65 of 0 ruled out mortality, NIV requirement and critical illness (intubation and/or ICU admission) with negative predictive values (NPVs) of 94%, 98% and 99%, respectively. In patients with abnormal CXRs, a CRB-65 of 0 ruled out the same endpoints with NPVs of 91%, 83% and 86%, respectively. Patients with low CRB-65 scores had better inpatient survival than patients with high CRB-65 scores, irrespective of CXR abnormalities (all p < 0.05). Conclusions: CRB-65, CXR and CRP are independent predictors of mortality in COVID-19. Adding CXR findings (dichotomised to either normal or abnormal) to CRB-65 does not improve its prognostic accuracy. A low CRB-65 score of 0 may be a good rule-out test for adverse clinical outcomes in COVID-19 patients with normal or abnormal CXRs, which deserves prospective validation.

Disparities in Level of Care and Outcomes Among Patients with COVID-19: Associations Between Race/Ethnicity, Social Determinants of Health and Virtual Hospitalization, Inpatient Hospitalization, Intensive Care, and Mortality

OBJECTIVE

To examine the role of race/ethnicity and social determinants of health on COVID-19 care and outcomes for patients within a healthcare system that provided virtual hospital care.

METHODS

This retrospective cohort study included 12,956 adults who received care for COVID-19 within an integrated healthcare system between 3/1/2020 and 8/31/2020. Multinomial models were used to examine associations between race/ethnicity, insurance, neighborhood deprivation measured by Area Deprivation Index (ADI), and outcomes of interest. Outcomes included (1) highest level of care: virtual observation (VOU), virtual hospitalization (VACU), or inpatient hospitalization; (2) intensive care (ICU); and (3) all-cause 30-day mortality.

RESULTS

Patients were 41.8% White, 27.2% Black, and 31.0% Hispanic. Compared to White patients, Black patients had 1.86 higher odds of VACU admission and 1.43 higher odds of inpatient hospitalization (vs. VOU). Hispanic patients had 1.24 higher odds of inpatient hospitalization (vs. VOU). In models stratified by race/ethnicity, Hispanic and Black patients had higher odds of inpatient hospitalization (vs. VOU) if Medicaid insured compared to commercially insured. Hispanic patients living in the most deprived neighborhood had higher odds of inpatient hospitalization, compared to those in the least deprived neighborhood. Black and Hispanic patients had higher odds of ICU admission and 30-day mortality after adjustment for other social determinants.

CONCLUSIONS

Insurance and ADI were associated with COVID-19 outcomes; however, associations varied by race/ethnicity. Racial/ethnic disparities in outcomes are not fully explained by measured social determinants of health, highlighting the need for further investigation into systemic causes of inequities in COVID-19 outcomes.

The relevance of chest X-ray radiologic severity index and CURB-65 score with the death event in hospitalized patient with COVID-19 pneumonia

Background

The global pandemic respiratory infection COVID-19 has had a high mortality rate since early 2020 with a broad spectrum of symptoms and giving a high burden. This study used the chest X-ray radiologic severity index method to radiologically assess the degree of lung lesions and the CURB-65 score to clinically assess COVID-19 pneumonia patients associated with the incidence of death in hospitalized patients.

Results

The research data were carried out from March 2020 to April 2021 based on patient medical records and chest X-rays at Doctor Kariadi General Hospital Semarang Indonesia. One hundred and five subjects were collected that fulfilled the inclusion and exclusion criteria. The CURB-65 score ≥ 2 had a significant relationship to the death event with a prevalence interval of 2.98 (95% CI, p = 0.000). The radiologic severity index ≥ 22.5 in initial chest X-ray signified a prevalence ratio of 2.24 (CI 95%, p = 0.004) and the radiologic severity index ≥ 29.5 in the second chest X-ray signified a prevalence ratio of 4.53 for the incidence of death (95% CI, p = 0.000). The combination of CURB-65 and the first chest X-ray radiologic severity index resulted in a prevalence ratio of 27.44, and the combination of CURB-65 and the second chest X-ray radiologic severity index resulted in a prevalence ratio of 60.2 which were significant for the mortality of hospitalized COVID-19 pneumonia patients.

Conclusions

Chest X-ray radiologic severity index and CURB-65 score have a significant relevance with the death event in hospitalized patients with COVID-19 pneumonia and can thus be used as a predictor of mortality.

Shock Index in COVID Era

The health care burden and risks to health care workers imposed by novel coronavirus disease 2019 (COVID-19) mandated the need for a simple, noninvasive, objective, and parsimonious risk stratification system predicting the level of care, need for definitive airway, and titration of the ongoing patient care. Shock index (SI = heart rate/systolic blood pressure) has been evaluated in emergency triage, sepsis, and trauma settings including different age group of patients. The ever accumulating girth of evidences demonstrated a superior predictive value of SI over other hemodynamic parameters. Inclusion of respiratory and/or neurological parameters and adjustment of the cutoffs appropriate to patient age increase the predictability in the trauma and sepsis scenario. Being reproducible, dynamic, and simple, SI can be a valuable patient risk stratification tool in this ongoing era of COVID-19 pandemic.

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies. Objectives: Early prediction of mortality using machine learning based on typical laboratory results and clinical data registered on the day of ICU admission. Methods: We retrospectively studied 797 patients diagnosed with COVID-19 in Iran and the United Kingdom (U.K.). To find parameters with the highest predictive values, Kolmogorov-Smirnov and Pearson chi-squared tests were used. Several machine learning algorithms, including Random Forest (RF), logistic regression, gradient boosting classifier, support vector machine classifier, and artificial neural network algorithms were utilized to build classification models. The impact of each marker on the RF model predictions was studied by implementing the local interpretable model-agnostic explanation technique (LIME-SP). Results: Among 66 documented parameters, 15 factors with the highest predictive values were identified as follows: gender, age, blood urea nitrogen (BUN), creatinine, international normalized ratio (INR), albumin, mean corpuscular volume (MCV), white blood cell count, segmented neutrophil count, lymphocyte count, red cell distribution width (RDW), and mean cell hemoglobin (MCH) along with a history of neurological, cardiovascular, and respiratory disorders. Our RF model can predict patient outcomes with a sensitivity of 70% and a specificity of 75%. The performance of the models was confirmed by blindly testing the models in an external dataset. Conclusions: Using two independent patient datasets, we designed a machine-learning-based model that could predict the risk of mortality from severe COVID-19 with high accuracy. The most decisive variables in our model were increased levels of BUN, lowered albumin levels, increased creatinine, INR, and RDW, along with gender and age. Considering the importance of early triage decisions, this model can be a useful tool in COVID-19 ICU decision-making.

CURB-65 as a predictor of 30-day mortality in patients hospitalized with COVID-19 in Ecuador: COVID-EC study

Objective

This article aims to assess the utility of CURB-65 in predicting 30-day mortality in adult patients hospitalized with COVID-19.

Methods

This work is a cohort study conducted between March 1 and April 30, 2020 in Ecuador.

Results

A total of 247 patients were included (mean age 60 ± 14 years, 70% men, overall mortality 41.3%). Patients with CURB-65 ≥ 2 had a higher mortality rate (57 vs. 17%, p < .001) that was associated with other markers of risk: advanced age, hypertension, overweight/obesity, kidney failure, hypoxemia, requirement for mechanical ventilation, or onset of respiratory distress.

Conclusions

CURB-65  ≥ 2 was associated with higher 30-day mortality on the univariate (Kaplan–Meier estimator) and multivariate (Cox regression) analysis.

Clinical features and risk factors for mortality among long-term care facility residents hospitalized due to COVID-19 in Spain

BACKGROUND

COVID-19 severely impacted older adults and long-term care facility (LTCF) residents. Our primary aim was to describe differences in clinical and epidemiological variables, in-hospital management, and outcomes between LTCF residents and community-dwelling older adults hospitalized with COVID-19. The secondary aim was to identify risk factors for mortality due to COVID-19 in hospitalized LTCF residents.

METHODS

This is a cross-sectional analysis within a retrospective cohort of hospitalized patients≥75 years with confirmed COVID-19 admitted to 160 Spanish hospitals. Differences between groups and factors associated with mortality among LTCF residents were assessed through comparisons and logistic regression analysis.

RESULTS

Of 6,189 patients≥75 years, 1,185 (19.1%) were LTCF residents and 4,548 (73.5%) were community-dwelling. LTCF residents were older (median: 87.4 vs. 82.1 years), mostly female (61.6% vs. 43.2%), had more severe functional dependence (47.0% vs 7.8%), more comorbidities (Charlson Comorbidity Index: 6 vs 5), had dementia more often (59.1% vs. 14.4%), and had shorter duration of symptoms (median: 3 vs 6 days) than community-dwelling patients (all, p<.001). Mortality risk factors in LTCF residents were severe functional dependence (aOR:1.79;95%CI:1.13-2.83;p=.012), dyspnea (1.66;1.16-2.39;p=.004), SatO2<94% (1.73;1.27-2.37;p=.001), temperature≥37.8ºC (1.62;1.11-2.38; p=.013); qSOFA index≥2 (1.62;1.11-2.38;p=.013), bilateral infiltrates (1.98;1.24-2.98;p<.001), and high C-reactive protein (1.005;1.003-1.007;p<.001). In-hospital mortality was initially higher among LTCF residents (43.3% vs 39.7%), but lower after adjusting for sex, age, functional dependence, and comorbidities (aOR:0.74,95%CI:0.62-0.87;p<.001).

CONCLUSION

Basal functional status and COVID-19 severity are risk factors of mortality in LTCF residents. The lower adjusted mortality rate in LTCF residents may be explained by earlier identification, treatment, and hospitalization for COVID-19.

Performance of the CURB-65, ISARIC-4C and COVID-GRAM scores in terms of severity for COVID-19 patients

BACKGROUND

In the COVID-19 pandemic, difficulties have been experienced in the provision of healthcare services because of excessive patient admissions to hospitals and emergency departments. It has become important to use clear and objective criteria for the early diagnosis of patients with high-risk classification and clinical worsening risk.

OBJECTIVE

The aim of this study was to assess the prognostic accuracy of CURB-65, ISARIC-4C and COVID-GRAM scores in patients hospitalised for COVID-19 and to compare the scoring systems in terms of predicting in-hospital mortality and intensive care unit requirement.

METHODS

The files of all COVID-19 patients over the age of 18 who were admitted to the emergency department and hospitalised between September 1, 2020 and December 1, 2020 were retrospectively scanned. The area under the receiver operating characteristic curve and Youden J Index were used to compare scoring systems for predicting in-hospital mortality and intensive care requirement.

RESULTS

There were 481 patients included in this study. The median age of the patients was 67 (52-79). In terms of in-hospital mortality, the AUC of CURB-65, ISARIC-4C and COVID-GRAM were 0.846, 0.784 and 0.701 respectively. In terms of intensive care requirement, the AUC of CURB-65, ISARIC-4C and COVID-GRAM were 0.898, 0.797 and 0.684 respectively. In our study, Youden's J indexes of CURB-65, ISARIC-4C and COVID-GRAM scores were found to be 0.59, 0.27 and 0.01 respectively, for mortality prediction of COVID-19 patients. Whereas Youden's J indexes were found to be 0.63, 0.26 and 0.01 respectively for determining intensive care requirement.

CONCLUSIONS

Among the scoring systems assessed, CURB-65 score had better performance in predicting in-hospital mortality and ICU requirement in COVID-19 patients. ISARIC-4C has been found successful in identifying low-risk patients and the use of the ISARIC-4C score with CURB-65 increases the accuracy of risk assessment.

Management of COVID-19 Patients in the Emergency Department

COVID-19 is an emerging disease of global public health concern. As the pandemic overwhelmed emergency departments (EDs), a restructuring of emergency care delivery became necessary in many hospitals. Furthermore, with more than 2000 papers being published each week, keeping up with ever-changing information has proven to be difficult for emergency physicians. The aim of the present review is to provide emergency physician with a summary of the current literature regarding the management of COVID-19 patients in the emergency department.

Cancer and COVID-19: analysis of patient outcomes

Background: We sought to investigate the outcomes associated with COVID-19 disease in cancer patients. Methods: We conducted a retrospective cohort study of laboratory-confirmed COVID-19 patients. Results: Of the 206 patients included, 57 had at least one preexisting malignancy. Cancer patients were older than noncancer patients. Of the 185 discharged cases, cancer patients had a significantly higher frequency of unplanned reintubation (7.1% vs 0.9%, p < 0.049), and required longer hospital stay (8.58 ± 6.50 days versus 12.83 ± 11.44 days, p < 0.002). Regression analysis revealed that obesity and active smoking were associated with an increased risk of mortality. Conclusion: Outcomes in COVID-19 appear to be driven by obesity as well as active smoking, with no difference in mortality between cancer and noncancer patients.

In this study, we aimed to investigate how COVID-19 affected cancer patients and whether this altered their survival outcomes. To do this, we examined data from a database of patients who have passed through our institution – a retrospective cohort analysis. Of the 206 patients we included in the study from this database, 57 had at least one preexisting cancer. Cancer patients tended to be older than noncancer patients. Of the 185 discharged patients, cancer patients required longer hospital stays, but there was no difference in mortality. Disease complications and intensive care unit admission with obesity and active smoking put patients in our cohort at increased risk of death. To conclude, outcomes in COVID-19 patients appear to be driven by obesity as well as active smoking, with no difference in mortality between cancer and noncancer patients.

Stratifying Deterioration Risk by Acuity at Admission Offers Triage Insights for Coronavirus Disease 2019 Patients

OBJECTIVES

Triaging patients at admission to determine subsequent deterioration risk can be difficult. This is especially true of coronavirus disease 2019 patients, some of whom experience significant physiologic deterioration due to dysregulated immune response following admission. A well-established acuity measure, the Rothman Index, is evaluated for stratification of patients at admission into high or low risk of subsequent deterioration.

DESIGN

Multicenter retrospective study.

SETTING

One academic medical center in Connecticut, and three community hospitals in Connecticut and Maryland.

PATIENTS

Three thousand four hundred ninety-nine coronavirus disease 2019 and 14,658 noncoronavirus disease 2019 adult patients admitted to a medical service between January 1, 2020, and September 15, 2020.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Performance of the Rothman Index at admission to predict in-hospital mortality or ICU utilization for both general medical and coronavirus disease 2019 populations was evaluated using the area under the curve. Precision and recall for mortality prediction were calculated, high- and low-risk thresholds were determined, and patients meeting threshold criteria were characterized. The Rothman Index at admission has good to excellent discriminatory performance for in-hospital mortality in the coronavirus disease 2019 (area under the curve, 0.81-0.84) and noncoronavirus disease 2019 (area under the curve, 0.90-0.92) populations. We show that for a given admission acuity, the risk of deterioration for coronavirus disease 2019 patients is significantly higher than for noncoronavirus disease 2019 patients. At admission, Rothman Index-based thresholds segregate the majority of patients into either high- or low-risk groups; high-risk groups have mortality rates of 34-45% (coronavirus disease 2019) and 17-25% (noncoronavirus disease 2019), whereas low-risk groups have mortality rates of 2-5% (coronavirus disease 2019) and 0.2-0.4% (noncoronavirus disease 2019). Similarly large differences in ICU utilization are also found.

CONCLUSIONS

Acuity level at admission may support rapid and effective risk triage. Notably, in-hospital mortality risk associated with a given acuity at admission is significantly higher for coronavirus disease 2019 patients than for noncoronavirus disease 2019 patients. This insight may help physicians more effectively triage coronavirus disease 2019 patients, guiding level of care decisions and resource allocation.

Clinical Characteristics and Risk Factors for Mortality in Very Old Patients Hospitalized With COVID-19 in Spain

Background

Advanced age is a well-known risk factor for poor prognosis in COVID-19. However, few studies have specifically focused on very old inpatients with COVID-19. This study aims to describe the clinical characteristics of very old inpatients with COVID-19 and identify risk factors for in-hospital mortality at admission.

Methods

We conducted a nationwide, multicenter, retrospective, observational study in patients ≥ 80 years hospitalized with COVID-19 in 150 Spanish hospitals (SEMI-COVID-19) Registry (March 1–May 29, 2020). The primary outcome was in-hospital mortality. A uni- and multivariate logistic regression was performed to assess predictors of mortality at admission.

Results

A total of 2772 consecutive patients (49.4% men, median age 86.3 years) were analyzed. Rates of atherosclerotic cardiovascular disease, diabetes mellitus, dementia, and Barthel Index < 60 were 30.8%, 25.6%, 30.5%, and 21.0%, respectively. The overall case-fatality rate was 46.9% (n: 1301) and increased with age (80–84 years: 41.6%; 85–90 years: 47.3%; 90–94 years: 52.7%; ≥95 years: 54.2%). After analysis, male sex and moderate-to-severe dependence were independently associated with in-hospital mortality; comorbidities were not predictive. At admission, independent risk factors for death were: oxygen saturation < 90%; temperature ≥ 37.8°C; quick sequential organ failure assessment (qSOFA) score ≥ 2; and unilateral–bilateral infiltrates on chest x-rays. Some analytical findings were independent risk factors for death, including estimated glomerular filtration rate < 45 mL/min/1.73 m²; lactate dehydrogenase ≥ 500 U/L; C-reactive protein ≥ 80 mg/L; neutrophils ≥ 7.5 × 10³/μL; lymphocytes < 0.8 × 10³/μL; and monocytes < 0.5 × 10³/μL.

Conclusions

This first large, multicenter cohort of very old inpatients with COVID-19 shows that age, male sex, and poor preadmission functional status—not comorbidities—are independently associated with in-hospital mortality. Severe COVID-19 at admission is related to poor prognosis.

Performance of the Pandemic Medical Early Warning Score (PMEWS), Simple Triage Scoring System (STSS) and Confusion, Uremia, Respiratory rate, Blood pressure and age ≥ 65 (CURB-65) score among patients with COVID-19 pneumonia in an emergency department triage setting: a retrospective study

BACKGROUND

Healthcare institutions are confronted with large numbers of patient admissions during large-scale or long-term public health emergencies like pandemics. Appropriate and effective triage is needed for effective resource use.

OBJECTIVES

To evaluate the effectiveness of the Pandemic Medical Early Warning Score (PMEWS), Simple Triage Scoring System (STSS) and Confusion, Uremia, Respiratory rate, Blood pressure and age ≥ 65 years (CURB-65) score in an emergency department (ED) triage setting.

DESIGN AND SETTING

Retrospective study in the ED of a tertiary-care university hospital in Düzce, Turkey.

METHODS

PMEWS, STSS and CURB-65 scores of patients diagnosed with COVID-19 pneumonia were calculated. Thirty-day mortality, intensive care unit (ICU) admission, mechanical ventilation (MV) need and outcomes were recorded. The predictive accuracy of the scores was assessed using receiver operating characteristic curve analysis.

RESULTS

One hundred patients with COVID-19 pneumonia were included. The 30-day mortality was 6%. PMEWS, STSS and CURB-65 showed high performance for predicting 30-day mortality (area under the curve: 0.968, 0.962 and 0.942, respectively). Age > 65 years, respiratory rate > 20/minute, oxygen saturation (SpO2) < 90% and ED length of stay > 4 hours showed associations with 30-day mortality (P < 0.05).

CONCLUSIONS

CURB-65, STSS and PMEWS scores are useful for predicting mortality, ICU admission and MV need among patients diagnosed with COVID-19 pneumonia. Advanced age, increased respiratory rate, low SpO2 and prolonged ED length of stay may increase mortality. Further studies are needed for developing the triage scoring systems, to ensure effective long-term use of healthcare service capacity during pandemics.

Decreased serial scores of severe organ failure assessments are associated with survival in mechanically ventilated patients; the prospective Maastricht Intensive Care COVID cohort

Background

The majority of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are admitted to the Intensive Care Unit (ICU) for mechanical ventilation. The role of multi-organ failure during ICU admission as driver for outcome remains to be investigated yet.

Design and setting

Prospective cohort of mechanically ventilated critically ill with SARS-CoV-2 infection.

Participants and methods

94 participants of the MaastrICCht cohort (21% women) had a median length of stay of 16 days (maximum of 77). After division into survivors (n = 59) and non-survivors (n = 35), we analysed 1555 serial SOFA scores using linear mixed-effects models.

Results

Survivors improved one SOFA score point more per 5 days (95% CI: 4–8) than non-survivors. Adjustment for age, sex, and chronic lung, renal and liver disease, body-mass index, diabetes mellitus, cardiovascular risk factors, and Acute Physiology and Chronic Health Evaluation II score did not change this result. This association was stronger for women than men (P-interaction = 0.043).

Conclusions

The decrease in SOFA score associated with survival suggests multi-organ failure involvement during mechanical ventilation in patients with SARS-CoV-2. Surviving women appeared to improve faster than surviving men. Serial SOFA scores may unravel an unfavourable trajectory and guide decisions in mechanically ventilated patients with SARS-CoV-2.

[CURB-65 as a predictor of 30-day mortality in patients hospitalized with COVID-19 in Ecuador: COVID-EC StudyAbstract]

Objetivo

Valorar la utilidad del CURB-65 para predecir la mortalidad a 30 días en pacientes adultos hospitalizados con COVID-19.

Métodos

Cohorte realizada entre el 1 de marzo y el 30 de abril de 2020 en Ecuador.

Resultados

Se incluyeron 247 pacientes (edad media 60 ± 14 años, 70% varones, mortalidad global 41,3%). Los pacientes con CURB-65 ≥ 2 presentaron mayor mortalidad (57 vs. 17%, p < 0,001), en asociación con otros marcadores de riesgo: edad avanzada, hipertensión arterial, sobrepeso/obesidad, fracaso renal, hipoxemia, requerimiento de ventilación mecánica o desarrollo de distrés respiratorio.

Conclusiones

En el análisis univariado (Kaplan-Meier) y multivariado (regresión de Cox) el CURB-65 ≥ 2 se relacionó con una mayor mortalidad a 30 días.

Predicting severe COVID-19 in the Emergency Department

Background

COVID-19 may lead to severe disease, requiring intensive care treatment and challenging the capacity of health care systems. The aim of this study was to compare the ability of commonly used scoring systems for sepsis and pneumonia to predict severe COVID-19 in the emergency department.

Methods

Prospective, observational, single centre study in a secondary/tertiary care hospital in Oslo, Norway. Patients were assessed upon hospital admission using the following scoring systems; quick Sequential Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome criteria (SIRS), National Early Warning Score 2 (NEWS2), CURB-65 and Pneumonia Severity index (PSI). The ratio of arterial oxygen tension to inspiratory oxygen fraction (P/F-ratio) was also calculated. The area under the receiver operating characteristics curve (AUROC) for each scoring system was calculated, along with sensitivity and specificity for the most commonly used cut-offs. Severe disease was defined as death or treatment in ICU within 14 days.

Results

38 of 175 study participants developed severe disease, 13 (7%) died and 29 (17%) had a stay at an intensive care unit (ICU). NEWS2 displayed an AUROC of 0.80 (95% confidence interval 0.72-0.88), CURB-65 0.75 (0.65-0.84), PSI 0.75 (0.65-0.84), SIRS 0.70 (0.61-0.80) and qSOFA 0.70 (0.61-0.79). NEWS2 was significantly better than SIRS and qSOFA in predicating severe disease, and with a cut-off of5 points, had a sensitivity and specificity of 82% and 60%, respectively.

Conclusion

NEWS2 predicted severe COVID-19 disease more accurately than SIRS and qSOFA, but not significantly better than CURB65 and PSI. NEWS2 may be a useful screening tool in evaluating COVID-19 patients during hospital admission.

Trial registration

: ClinicalTrials.gov Identifier: NCT04345536. (https://clinicaltrials.gov/ct2/show/NCT04345536).

Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial

Background

Currently, physicians are limited in their ability to provide an accurate prognosis for COVID-19 positive patients. Existing scoring systems have been ineffective for identifying patient decompensation. Machine learning (ML) may offer an alternative strategy. A prospectively validated method to predict the need for ventilation in COVID-19 patients is essential to help triage patients, allocate resources, and prevent emergency intubations and their associated risks.

Methods

In a multicenter clinical trial, we evaluated the performance of a machine learning algorithm for prediction of invasive mechanical ventilation of COVID-19 patients within 24 h of an initial encounter. We enrolled patients with a COVID-19 diagnosis who were admitted to five United States health systems between March 24 and May 4, 2020.

Results

197 patients were enrolled in the REspirAtory Decompensation and model for the triage of covid-19 patients: a prospective studY (READY) clinical trial. The algorithm had a higher diagnostic odds ratio (DOR, 12.58) for predicting ventilation than a comparator early warning system, the Modified Early Warning Score (MEWS). The algorithm also achieved significantly higher sensitivity (0.90) than MEWS, which achieved a sensitivity of 0.78, while maintaining a higher specificity (p < 0.05).

Conclusions

In the first clinical trial of a machine learning algorithm for ventilation needs among COVID-19 patients, the algorithm demonstrated accurate prediction of the need for mechanical ventilation within 24 h. This algorithm may help care teams effectively triage patients and allocate resources. Further, the algorithm is capable of accurately identifying 16% more patients than a widely used scoring system while minimizing false positive results.

•

Validation of prediction algorithm for ventilation requirements in COVID-19 patients.

•

Algorithm achieved significantly higher sensitivity than the common scoring system MEWS.

•

Algorithm detected 16% more patients who will require invasive ventilation than MEWS.

•

Advance warning of ventilation needs can help improve COVID-19 patient outcomes.

Acute kidney injury and kidney replacement therapy in COVID-19: a systematic review and meta-analysis

BACKGROUND

Acute kidney injury (AKI) can affect hospitalized patients with coronavirus disease 2019 (COVID-19), with estimates ranging between 0.5% and 40%. We performed a systematic review and meta-analysis of studies reporting incidence, mortality and risk factors for AKI in hospitalized COVID-19 patients.

METHODS

We systematically searched 11 electronic databases until 29 May 2020 for studies in English reporting original data on AKI and kidney replacement therapy (KRT) in hospitalized COVID-19 patients. Incidences of AKI and KRT and risk ratios for mortality associated with AKI were pooled using generalized linear mixed and random-effects models. Potential risk factors for AKI were assessed using meta-regression. Incidences were stratified by geographic location and disease severity.

RESULTS

A total of 3042 articles were identified, of which 142 studies were included, with 49 048 hospitalized COVID-19 patients including 5152 AKI events. The risk of bias of included studies was generally low. The pooled incidence of AKI was 28.6% [95% confidence interval (CI) 19.8-39.5] among hospitalized COVID-19 patients from the USA and Europe (20 studies) and 5.5% (95% CI 4.1-7.4) among patients from China (62 studies), whereas the pooled incidence of KRT was 7.7% (95% CI 5.1-11.4; 18 studies) and 2.2% (95% CI 1.5-3.3; 52 studies), respectively. Among patients admitted to the intensive care unit, the incidence of KRT was 20.6% (95% CI 15.7-26.7; 38 studies). Meta-regression analyses showed that age, male sex, cardiovascular disease, diabetes mellitus, hypertension and chronic kidney disease were associated with the occurrence of AKI; in itself, AKI was associated with an increased risk of mortality, with a pooled risk ratio of 4.6 (95% CI 3.3-6.5).

CONCLUSIONS

AKI and KRT are common events in hospitalized COVID-19 patients, with estimates varying across geographic locations. Additional studies are needed to better understand the underlying mechanisms and optimal treatment of AKI in these patients.

The association of lung ultrasound images with COVID-19 infection in an emergency room cohort

Lung ultrasound could facilitate the triage of patients with suspected COVID‐19 infection admitted to the emergency room. We developed a predictive model for COVID‐19 diagnosis based on lung ultrasound and clinical features. We used ultrasound to image the lung bilaterally at two anterior sites, one and two hands below each clavicle, and a posterolateral site that was the posterior transverse continuation from the lower anterior site. We studied 100 patients, 31 of whom had a COVID‐19 positive reverse transcriptase polymerase chain reaction. A positive test was independently associated with: quick sequential organ failure assessment score ≥1; ≥3 B‐lines at the upper site; consolidation and thickened pleura at the lower site; and thickened pleura line at the posterolateral site. The model discrimination was an area (95%CI) under the receiver operating characteristic curve of 0.82 (0.75–0.90). The characteristics (95%CI) of the model’s diagnostic threshold, applied to the population from which it was derived, were: sensitivity, 97% (83–100%); specificity, 62% (50–74%); positive predictive value, 54% (41–98%); and negative predictive value, 98% (88–99%). This model may facilitate triage of patients with suspected COVID‐19 infection admitted to the emergency room.