CCEDRRN COVID-19 Infection Score (CCIS): development and validation in a Canadian cohort of a clinical risk score to predict SARS-CoV-2 infection in patients presenting to the emergency department with suspected COVID-19

ObjectivesTo develop and validate a clinical risk score that can accurately quantify the probability of SARS-CoV-2 infection in patients presenting to an emergency department without the need for laboratory testing.DesignCohort study of participants in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) registry. Regression models were fitted to predict a positive SARS-CoV-2 test result using clinical and demographic predictors, as well as an indicator of local SARS-CoV-2 incidence.Setting32 emergency departments in eight Canadian provinces.Participants27 665 consecutively enrolled patients who were tested for SARS-CoV-2 in participating emergency departments between 1 March and 30 October 2020.Main outcome measuresPositive SARS-CoV-2 nucleic acid test result within 14 days of an index emergency department encounter for suspected COVID-19 disease.ResultsWe derived a 10-item CCEDRRN COVID-19 Infection Score using data from 21 743 patients. This score included variables from history and physical examination and an indicator of local disease incidence. The score had a c-statistic of 0.838 with excellent calibration. We externally validated the rule in 5295 patients. The score maintained excellent discrimination and calibration and had superior performance compared with another previously published risk score. Score cut-offs were identified that can rule-in or rule-out SARS-CoV-2 infection without the need for nucleic acid testing with 97.4% sensitivity (95% CI 96.4 to 98.3) and 95.9% specificity (95% CI 95.5 to 96.0).ConclusionsThe CCEDRRN COVID-19 Infection Score uses clinical characteristics and publicly available indicators of disease incidence to quantify a patient’s probability of SARS-CoV-2 infection. The score can identify patients at sufficiently high risk of SARS-CoV-2 infection to warrant isolation and empirical therapy prior to test confirmation while also identifying patients at sufficiently low risk of infection that they may not need testing.Trial registration numberNCT04702945.

60. Creation and Comparison of a Machine Learning Decision Tree and Traditional Risk Score to Predict Ceftriaxone Resistance in Cancer Patients with E. coli Bacteremia

Background There are several clinical tools that have been developed to predict the likelihood of extended-spectrum β-lactamase producing Enterobacterales; however, the creation of these tools included few patients with cancer or otherwise immunosuppressed. The objectives of this retrospective cohort study were to develop a decision tree and traditional risk score to predict ceftriaxone resistance in cancer patients with Escherichia coli (E. coli) bacteremia as well as to compare the predictive accuracy between the tools. Methods Adults age ≥ 18 years old with E. coli bacteremia at The University of Texas MD Anderson Cancer Center from 1/2018 to 12/2019 were included. Isolates recovered within 1 week from the same patient were excluded. The decision tree was constructed using classification and regression tree analysis, with a minimum node size of 10. The risk score was created using a multivariable logistic regression model derived by using stepwise variable selection with backward elimination at level 0.2. The decision tree and risk score statistical metrics were compared. Results A total of 629 E. coli isolates were screened, of which 580 isolates met criteria. Ceftriaxone-resistant (CRO-R) E. coli accounted for 36% of isolates. The machine learning-derived decision tree included 5 predictors whereas the logistic regression-derived risk score included 7 predictors. The risk score cutoff point of ≥ 5 points demonstrated the most optimized overall classification accuracy. The positive predictive value of the decision tree was higher than that of the risk score (88% vs 74%, respectively), but the area under the receiver operating characteristic curve and model accuracy of the risk score was higher than that of the decision tree (0.85 vs 0.73 and 82% vs 74%, respectively). Figure 1. Clinical Decision Tree Table 1. Regression Model and Assigned Points for Clinical Risk Score Table 2. Statistical Metrics of Clinical Decision Tree and Clinical Risk Score Conclusion The decision tree and risk score can be used to determine the likelihood of whether a cancer patient with E. coli bacteremia has a CRO-R infection. In both clinical tools, the strongest predictor was a history of CRO-R E. coli colonization or infection in the last 6 months. The decision tree was more user-friendly, has fewer variables, and has a better positive predictive value in comparison to the risk score. However, the risk score has a significantly better discrimination and model accuracy than that of the decision tree. Disclosures Samuel L. Aitken, PharmD, MPH, BCIDP, Melinta Therapeutoics (Individual(s) Involved: Self): Consultant, Grant/Research Support

Development and Validation of a Clinical Risk Score to Predict Hospitalization Within 30 Days of Coronavirus Disease 2019 Diagnosis

ABSTRACT Introduction Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis. Methods We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer–Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran’s Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1). Results The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts. Conclusions The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges.

Clinical risk score for individualized risk stratification of patients with clinically suspected myocardial inflammation

Objective To develop a clinical risk score for individualized risk stratification of patients with clinically suspected myocardial inflammation. Background Myocardial inflammation is a prominent cause of non-ischaemic dilated cardiomyopathy, heart failure (HF) and sudden cardiac death. Methods This is a prospective multicentre longitudinal study of consecutive patients referred to cardiac magnetic resonance (CMR) with clinically suspected myocardial inflammation between October 2011 and December 2019 as a part of standard diagnostic pathway. Patients were followed up from the date of CMR. The outcome endpoints included major adverse cardiovascular event (MACE, cardiovascular mortality, sudden cardiac death, appropriate device discharge); or death or hospitalisation due to HF). A prognostic model was developed using Cox proportional hazards analysis and validated internally and externally. Results The final dataset included 722 subjects (50 years (40–61); males 422 (58%)). During a follow-up period of median 19 (15–23) months, there were 64 (9%) MACE and 130 (18%) HF events. Ten predictor variables qualified for entry into the prognostic model: age, sex, hematocrit, C-reactive protein, high-sensitive troponin-T (TNT), left and right ventricular ejection fraction, native T1 and T2, and late gadolinium enhancement (LGE). The final multivariable Cox regression model included native T2 (Figure 1A), TNT and LGE (Figure 1B) for the primary (Chi-square: 102.0, p<0.001) and secondary endpoint (Chi-square: 166.9, p<0.001), respectively. Cross-validation as well as external validation of the secondary models revealed good performance and no healthcare system effect. Based on the MyoRISK Score, patients were classified into three risk groups with respective event rates for MACE of 0%, 6.3% and 25.1%, and HF endpoint of 1.8%, 17.3% and 44.2%. TNT≥7 pg/ml allowed to efficiently preselect patients prior to CMR. Conclusions This is the first systematic assessment of outcomes in patients with clinically suspected myocardial inflammation, providing a non-invasive estimation of the probability of adverse events based on a score using readily available clinical parameters. FUNDunding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): DZHK

Clinical Risk Score for the Prediction of Incident Atrial Fibrillation: Derivation in 7 220 654 Taiwan Patients With 438 930 Incident Atrial Fibrillations During a 16‐Year Follow‐Up

Background Although several risk schemes have been proposed to predict new‐onset atrial fibrillation (AF), clinical prediction models specific for Asian patients were limited. In the present study, we aimed to develop a clinical risk score (Taiwan AF score) for AF prediction using the whole Taiwan population database with a long‐term follow‐up. Methods and Results Among 7 220 654 individuals aged ≥40 years without a past history of cardiac arrhythmia identified from the Taiwan Health Insurance Research Database, 438 930 incident AFs occurred after a 16‐year follow‐up. Clinical risk factors of AF were identified using Cox regression analysis and then combined into a clinical risk score (Taiwan AF score). The Taiwan AF score included age, male sex, and important comorbidities (hypertension, heart failure, coronary artery disease, end‐stage renal disease, and alcoholism) and ranged from −2 to 15. The area under the receiver operating characteristic curve of the Taiwan AF scores in the predictions of AF are 0.857 for the 1‐year follow‐up, 0.825 for the 5‐year follow‐up, 0.797 for the 10‐year follow‐up, and 0.756 for the 16‐year follow‐up. The annual risks of incident AF were 0.21%/year, 1.31%/year, and 3.37%/year for the low‐risk (score −2 to 3), intermediate‐risk (score 4 to 9), and high‐risk (score ≥10) groups, respectively. Compared with low‐risk patients, the hazard ratios of incident AF were 5.78 (95% CI, 3.76–7.75) for the intermediate‐risk group and 8.94 (95% CI, 6.47–10.80) for the high‐risk group. Conclusions We developed a clinical AF prediction model, the Taiwan AF score, among a large‐scale Asian cohort. The new score could help physicians to identify Asian patients at high risk of AF in whom more aggressive and frequent detections and screenings may be considered.