A Clinical Prediction Rule for Thrombosis in Critically Ill COVID-19 Patients: Step 1 Results of the Thromcco Study.

The incidence of thrombosis in COVID-19 patients is exceptionally high among intensive care unit (ICU)-admitted individuals. We aimed to develop a clinical prediction rule for thrombosis in hospitalized COVID-19 patients. Data were taken from the Thromcco study (TS) database, which contains information on consecutive adults (aged ≥ 18) admitted to eight Spanish ICUs between March 2020 and October 2021. Diverse logistic regression model analysis, including demographic data, pre-existing conditions, and blood tests collected during the first 24 h of hospitalization, was performed to build a model that predicted thrombosis. Once obtained, the numeric and categorical variables considered were converted to factor variables giving them a score. Out of 2055 patients included in the TS database, 299 subjects with a median age of 62.4 years (IQR 51.5-70) (79% men) were considered in the final model (SE = 83%, SP = 62%, accuracy = 77%). Seven variables with assigned scores were delineated as age 25-40 and ≥70 = 12, age 41-70 = 13, male = 1, D-dimer ≥ 500 ng/mL = 13, leukocytes ≥ 10 × 103/µL = 1, interleukin-6 ≥ 10 pg/mL = 1, and C-reactive protein (CRP) ≥ 50 mg/L = 1. Score values ≥28 had a sensitivity of 88% and specificity of 29% for thrombosis. This score could be helpful in recognizing patients at higher risk for thrombosis, but further research is needed.

Development and Validation of Simple Risk Scores to Predict Hospitalization in Outpatients with COVID-19 Including the Omicron Variant.

INTRODUCTION: Outpatient physicians need guidance to support decisions regarding hospitalization of COVID-19 patients and how closely to follow outpatients. Thus, we sought to develop and validate simple risk scores to predict hospitalization for outpatients with COVID-19 that do not require laboratory testing or imaging. METHODS: We identified outpatients 12 years and older who had a positive polymerase chain reaction test for SARS-CoV-2. Logistic regression was used to derive a risk score in patients presenting before March, 2021, and it was validated in a cohort presenting from March to September 2021 and an Omicron cohort from December, 2021 to January, 2022. RESULTS: Overall, 4.0% of 5843 outpatients in the early derivation cohort (before 3/1/21), 4.2% of 3806 outpatients in the late validation cohort, and 1.2% in an Omicron cohort were hospitalized. The base risk score included age, dyspnea, and any comorbidity. Other scores added fever, respiratory rate and/or oxygen saturation. All had very good overall accuracy (AUC 0.85-0.87) and classified about half of patients into a low-risk group with < 1% hospitalization risk. Hospitalization rates in the Omicron cohort were 0.22%, 1.3% and 8.7% for the base score. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization than our novel risk scores. CONCLUSIONS: A simple risk score suitable for outpatient and telehealth settings can classify over half of COVID-19 outpatients into a very low risk group with a 0.22% hospitalization risk in the Omicron cohort. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/.

External Validation of the COVID-NoLab and COVID-SimpleLab Prognostic Tools.

Our objective was to externally validate 2 simple risk scores for mortality among a mostly inpatient population with COVID-19 in Canada (588 patients for COVID-NoLab and 479 patients for COVID-SimpleLab). The mortality rates in the low-, moderate-, and high-risk groups for COVID-NoLab were 1.1%, 9.6%, and 21.2%, respectively. The mortality rates for COVID-SimpleLab were 0.0%, 9.8%, and 20.0%, respectively. These values were similar to those in the original derivation cohort. The 2 simple risk scores, now successfully externally validated, offer clinicians a reliable way to quickly identify low-risk inpatients who could potentially be managed as outpatients in the event of a bed shortage. Both are available online (https://ebell-projects.shinyapps.io/covid_nolab/ and https://ebell-projects.shinyapps.io/COVID-SimpleLab/).

Development and Validation of Simple Risk Scores to Predict Hospitalization in Outpatients with COVID-19 Including the Omicron Variant.

INTRODUCTION: Outpatient physicians need guidance to support decisions regarding hospitalization of COVID-19 patients and how closely to follow outpatients. Thus, we sought to develop and validate simple risk scores to predict hospitalization for outpatients with COVID-19 that do not require laboratory testing or imaging. METHODS: We identified outpatients 12 years and older who had a positive polymerase chain reaction test for SARS-CoV-2. Logistic regression was used to derive a risk score in patients presenting before March, 2021, and it was validated in a cohort presenting from March to September 2021 and an Omicron cohort from December, 2021 to January, 2022. RESULTS: Overall, 4.0% of 5843 outpatients in the early derivation cohort (before 3/1/21), 4.2% of 3806 outpatients in the late validation cohort, and 1.2% in an Omicron cohort were hospitalized. The base risk score included age, dyspnea, and any comorbidity. Other scores added fever, respiratory rate and/or oxygen saturation. All had very good overall accuracy (AUC 0.85-0.87) and classified about half of patients into a low-risk group with < 1% hospitalization risk. Hospitalization rates in the Omicron cohort were 0.22%, 1.3% and 8.7% for the base score. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization than our novel risk scores. CONCLUSIONS: A simple risk score suitable for outpatient and telehealth settings can classify over half of COVID-19 outpatients into a very low risk group with a 0.22% hospitalization risk in the Omicron cohort. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/.

Predictive scores for the diagnosis of Pulmonary Embolism in COVID-19: A systematic review.

OBJECTIVES: During the COVID-19 pandemic, several studies described an increased chance of developing pulmonary embolism (PE). Several scores have been used to predict the occurrence of PE. This systematic review summarizes the literature on predicting rules for PE in hospitalized COVID-19 patients (HCPs). METHODS: PUBMED and EMBASE databases were searched to identify articles (1 January 2020-28 April 2021) presenting data pertaining to the use of a prediction rule to assess the risk for PE in adult HCPs. The investigated outcome was the diagnosis of PE. Studies presenting data using a single laboratory assay for PE prediction were excluded. Included studies were appraised for methodological quality using the Newcastle - Ottawa Quality Assessment Scale for Cohort Studies (NOS). RESULTS: We obtained a refined pool of twelve studies for five scoring systems (Wells score, Geneva score, CHADS2/CHA2DS2VASc/M-CHA2DS2VASc, CHOD score, Padua Prediction Score), and 4,526 patients. Only one score was designed explicitly for HCPs. Three and nine included studies were prospective and retrospective cohort studies, respectively. Among the examined scores, the CHOD score seems promising for predictive ability. CONCLUSION: New prediction rules, specifically developed and validated for estimating the risk of PE in HCP, differentiating ICU from non-ICU patients, and taking into account anticoagulation prophylaxis, comorbidities, and the time from COVID-19 diagnosis are needed.

The Effects of Using a Clinical Prediction Rule to Prioritize Diagnostic Testing on Transmission and Hospital Burden: A Modeling Example of Early Severe Acute Respiratory Syndrome Coronavirus 2.

BACKGROUND: Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. METHODS: Using early severe acute respiratory syndrome coronavirus disease 2 (SARS-CoV-2) as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2. RESULTS: We found that applying this CPR (area under the curve, 0.69; 95% confidence interval, .68-.70) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (ie, "flattens the curve"), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. In addition, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit burden. CONCLUSION: We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.SummaryWhen the demand for diagnostic tests exceeds capacity, the use of a clinical prediction rule to prioritize diagnostic testing can have meaningful impact on population-level outcomes, including delaying and lowering the infection peak, and reducing healthcare burden.

An observational cohort study of the performance of the REDS score compared to the SIRS criteria, NEWS2, CURB65, SOFA, MEDS and PIRO scores to risk-stratify emergency department suspected sepsis.

OBJECTIVE: To compare the performance of the Risk-stratification of Emergency Department suspected Sepsis (REDS) score to the SIRS criteria, NEWS2, CURB65, SOFA, MEDS and PIRO scores, to risk-stratify Emergency Department (ED) suspected sepsis patients for mortality. METHOD: A retrospective observational cohort study of prospectively collected data. Adult patients admitted from the ED after receiving intravenous antibiotics for suspected sepsis in the year 2020, were studied. Patients with COVID-19 were excluded. The scores stated above were calculated for each patient. Receiver operator characteristics (ROC) curves were constructed for each score for the primary outcome measure, all-cause in-hospital mortality. The area under the ROC (AUROC) curves and cut-off points were identified by the statistical software. Scores above the cut-off point were deemed high-risk. The test characteristics of the high-risk groups were calculated. Comparisons were based on the AUROC curve and sensitivity for mortality of the high-risk groups. Previously published cut-off points were also studied. Calibration was also studied. RESULTS: Of the 2594 patients studied, 332 (12.8%) died. The AUROC curve for the REDS score 0.73 (95% confidence interval [CI] 0.72-0.75) was significantly greater than the AUROC curve for the SIRS criteria 0.51 (95% CI 0.49-0.53), p < .0001 and the NEWS2 score 0.69 (95% CI 0.67-0.70), p = .005, and similar to all other scores studied. Sensitivity for mortality at the respective cut-off points identified (REDS ≥3, NEWS2 ≥ 8, CURB65 ≥ 3, SOFA ≥3, MEDS ≥10 and PIRO ≥10) was greatest for the REDS score at 80.1% (95% CI 75.4-84.3) and significantly greater than the other scores. The sensitivity for mortality for an increase of two points from baseline in the SOFA score was 63% (95% CI 57.5-68.2). CONCLUSIONS: In this single centre study, the REDS score had either a greater AUROC curve or sensitivity for mortality compared to the comparator scores, at the respective cut-off points identified.KEY MESSAGESThe REDS score is a simple and objective scoring system to risk-stratify for mortality in emergency department (MED) patients with suspected sepsis.The REDS score is better or equivalent to existing scoring systems in its discrimination for mortality.

Age-Adjusted Endothelial Activation and Stress Index for Coronavirus Disease 2019 at Admission Is a Reliable Predictor for 28-Day Mortality in Hospitalized Patients With Coronavirus Disease 2019.

Background: Endothelial Activation and Stress Index (EASIX) predict death in patients undergoing allogeneic hematopoietic stem cell transplantation who develop endothelial complications. Because coronavirus disease 2019 (COVID-19) patients also have coagulopathy and endotheliitis, we aimed to assess whether EASIX predicts death within 28 days in hospitalized COVID-19 patients. Methods: We performed a retrospective study on COVID-19 patients from two different cohorts [derivation (n = 1,200 patients) and validation (n = 1,830 patients)]. The endpoint was death within 28 days. The main factors were EASIX [(lactate dehydrogenase * creatinine)/thrombocytes] and aEASIX-COVID (EASIX * age), which were log2-transformed for analysis. Results: Log2-EASIX and log2-aEASIX-COVID were independently associated with an increased risk of death in both cohorts (p < 0.001). Log2-aEASIX-COVID showed a good predictive performance for 28-day mortality both in the derivation cohort (area under the receiver-operating characteristic = 0.827) and in the validation cohort (area under the receiver-operating characteristic = 0.820), with better predictive performance than log2-EASIX (p < 0.001). For log2 aEASIX-COVID, patients with low/moderate risk (<6) had a 28-day mortality probability of 5.3% [95% confidence interval (95% CI) = 4-6.5%], high (6-7) of 17.2% (95% CI = 14.7-19.6%), and very high (>7) of 47.6% (95% CI = 44.2-50.9%). The cutoff of log2 aEASIX-COVID = 6 showed a positive predictive value of 31.7% and negative predictive value of 94.7%, and log2 aEASIX-COVID = 7 showed a positive predictive value of 47.6% and negative predictive value of 89.8%. Conclusion: Both EASIX and aEASIX-COVID were associated with death within 28 days in hospitalized COVID-19 patients. However, aEASIX-COVID had significantly better predictive performance than EASIX, particularly for discarding death. Thus, aEASIX-COVID could be a reliable predictor of death that could help to manage COVID-19 patients.

The value of computed tomography in assessing the risk of death in COVID-19 patients presenting to the emergency room.

OBJECTIVE: The aims of this study were to develop a multiparametric prognostic model for death in COVID-19 patients and to assess the incremental value of CT disease extension over clinical parameters. METHODS: Consecutive patients who presented to all five of the emergency rooms of the Reggio Emilia province between February 27 and March 23, 2020, for suspected COVID-19, underwent chest CT, and had a positive swab within 10 days were included in this retrospective study. Age, sex, comorbidities, days from symptom onset, and laboratory data were retrieved from institutional information systems. CT disease extension was visually graded as < 20%, 20-39%, 40-59%, or ≥ 60%. The association between clinical and CT variables with death was estimated with univariable and multivariable Cox proportional hazards models; model performance was assessed using k-fold cross-validation for the area under the ROC curve (cvAUC). RESULTS: Of the 866 included patients (median age 59.8, women 39.2%), 93 (10.74%) died. Clinical variables significantly associated with death in multivariable model were age, male sex, HDL cholesterol, dementia, heart failure, vascular diseases, time from symptom onset, neutrophils, LDH, and oxygen saturation level. CT disease extension was also independently associated with death (HR = 7.56, 95% CI = 3.49; 16.38 for ≥ 60% extension). cvAUCs were 0.927 (bootstrap bias-corrected 95% CI = 0.899-0.947) for the clinical model and 0.936 (bootstrap bias-corrected 95% CI = 0.912-0.953) when adding CT extension. CONCLUSIONS: A prognostic model based on clinical variables is highly accurate in predicting death in COVID-19 patients. Adding CT disease extension to the model scarcely improves its accuracy. KEY POINTS: • Early identification of COVID-19 patients at higher risk of disease progression and death is crucial; the role of CT scan in defining prognosis is unclear. • A clinical model based on age, sex, comorbidities, days from symptom onset, and laboratory results was highly accurate in predicting death in COVID-19 patients presenting to the emergency room. • Disease extension assessed with CT was independently associated with death when added to the model but did not produce a valuable increase in accuracy.

Development and Validation of the COVID-NoLab and COVID-SimpleLab Risk Scores for Prognosis in 6 US Health Systems.

PURPOSE: Develop and validate simple risk scores based on initial clinical data and no or minimal laboratory testing to predict mortality in hospitalized adults with COVID-19. METHODS: We gathered clinical and initial laboratory variables on consecutive inpatients with COVID-19 who had either died or been discharged alive at 6 US health centers. Logistic regression was used to develop a predictive model using no laboratory values (COVID-NoLab) and one adding tests available in many outpatient settings (COVID-SimpleLab). The models were converted to point scores and their accuracy evaluated in an internal validation group. RESULTS: We identified 1340 adult inpatients with complete data for nonlaboratory parameters and 741 with complete data for white blood cell (WBC) count, differential, c-reactive protein (CRP), and serum creatinine. The COVID-NoLab risk score includes age, respiratory rate, and oxygen saturation and identified risk groups with 0.8%, 11.4%, and 40.4% mortality in the validation group (AUROCC = 0.803). The COVID-SimpleLab score includes age, respiratory rate, oxygen saturation, WBC, CRP, serum creatinine, and comorbid asthma and identified risk groups with 1.0%, 9.1%, and 29.3% mortality in the validation group (AUROCC = 0.833). CONCLUSIONS: Because they use simple, readily available predictors, developed risk scores have potential applicability in the outpatient setting but require prospective validation before use.

Primary Care Relevant Risk Factors for Adverse Outcomes in Patients With COVID-19 Infection: A Systematic Review.

BACKGROUND: The aim of this systematic review is to summarize the best available evidence regarding individual risk factors, simple risk scores, and multivariate models that use patient characteristics, vital signs, comorbidities, and laboratory tests relevant to outpatient and primary care settings. METHODS: Medline, WHO COVID-19, and MedRxIV databases were searched; studies meeting inclusion criteria were reviewed in parallel, and variables describing study characteristics, study quality, and risk factor data were abstracted. Study quality was assessed using the Quality in Prognostic Studies tool. Random effects meta-analysis of relative risks (categorical variables) and unstandardized mean differences (continuous variables) was performed; multivariate models and clinical prediction rules were summarized qualitatively. RESULTS: A total of 551 studies were identified and 22 studies were included. The median or mean age ranged from 38 to 68 years. All studies included only inpatients, and mortality rates ranged from 3.2% to 50.5%. Individual risk factors most strongly associated with mortality included increased age, c-reactive protein (CRP), d-dimer, heart rate, respiratory rate, lactate dehydrogenase, and procalcitonin as well as decreased oxygen saturation, the presence of dyspnea, and comorbid coronary heart and chronic kidney disease. Independent predictors of adverse outcomes reported most frequently by multivariate models include increasing age, increased CRP, decreased lymphocyte count, increased lactate dehydrogenase, elevated temperature, and the presence of any comorbidity. Simple risk scores and multivariate models have been proposed but are often complex, and most have not been validated. CONCLUSIONS: Our systematic review identifies several risk factors for adverse outcomes in COVID-19-infected inpatients that are often available in the outpatient and primary care settings: increasing age, increased CRP or procalcitonin, decreased lymphocyte count, decreased oxygen saturation, dyspnea on presentation, and the presence of comorbidities. Future research to develop clinical prediction models and rules should include these predictors as part of their core data set to develop and validate pragmatic outpatient risk scores.

A Symptom-Based Rule for Diagnosis of COVID-19.

SARS-CoV-19 PCR testing has a turn-around time that makes it impractical for real-time decision-making, and current point-of-care tests have limited sensitivity, with frequent false negatives. The study objective was to develop a clinical prediction rule to use with a point-of-care test to diagnose COVID-19 in symptomatic outpatients. A standardized clinical questionnaire was administered prior to SARS-CoV-2 PCR testing. Data was extracted by a physician blinded to the result status. Individual symptoms were combined into 326 unique clinical phenotypes. Multivariable logistic regression was used to identify independent predictors of COVID-19, from which a weighted clinical prediction rule was developed, to yield stratified likelihood ratios for varying scores. A retrospective cohort of 120 SARS-CoV-2-positive cases and 120 SARS-CoV-2-negative matched controls among symptomatic outpatients in a Connecticut HMO was used for rule development. A temporally distinct cohort of 40 cases was identified for validation of the rule. Clinical phenotypes independently associated with COVID-19 by multivariable logistic regression include loss of taste or smell (olfactory phenotype, 2 points) and fever and cough (febrile respiratory phenotype, 1 point). Wheeze or chest tightness (reactive airways phenotype, - 1 point) predicted non-COVID-19 respiratory viral infection. The AUC of the model was 0.736 (0.674-0.798). Application of a weighted C19 rule yielded likelihood ratios for COVID-19 diagnosis for varying scores ranging from LR 15.0 for 3 points to LR 0.1 for - 1 point. Using a Bayesian diagnostic approach, combining community prevalence with the evidence-based C19 rule to adjust pretest probability, clinicians can apply a point of care test with limited sensitivity across a range of clinical scenarios to differentiate COVID-19 infection from influenza and respiratory viral infection.