Simple risk scores to predict hospitalization or death in outpatients with COVID-19

Importance Outpatient physicians need guidance to support their clinical decisions regarding management of patients with COVID-19, in particular whether to hospitalize a patient and if managed as an outpatient, how closely to follow them. Objective To develop and prospectively validate a clinical prediction rule to predict the likelihood of hospitalization for outpatients with COVID-19 that does not require laboratory testing or imaging. Design Derivation and temporal validation of a clinical prediction rule, and prospective validation of two externally derived clinical prediction rules. Setting Primary and Express care clinics in a Pennsylvania health system. Participants Patients 12 years and older presenting to outpatient clinics who had a positive polymerase chain reaction test for COVID-19. Main outcomes and measures Classification accuracy (percentage in each risk group hospitalized) and area under the receiver operating characteristic curve (AUC). Results Overall, 7.4% of outpatients in the early derivation cohort (5843 patients presenting before 3/1/21) and 5.5% in the late validation cohort (3806 patients presenting 3/1/21 or later) were ultimately hospitalized. We developed and temporally validated three risk scores that all included age, dyspnea, and the presence of comorbidities, adding respiratory rate for the second score and oxygen saturation for the third. All had very good overall accuracy (AUC 0.77 to 0.78) and classified over half of patients in the validation cohort as very low risk with a 1.7% or lower likelihood of hospitalization. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization (2.8%). Conclusions and relevance Simple risk scores applicable to outpatient and telehealth settings can identify patients with very low (1.6% to 1.7%), low (5.2% to 5.9%), moderate (14.7% to 15.6%), and high risk (32.0% to 34.2%) of hospitalization. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/.

Clinical performance of a new prediction rule of systemic loxoscellism. case report

Systemic or cutaneous-visceral loxoscelism is the most severe clinical presentation of loxoscelism. Currently there is no validated laboratory diagnostic method that allows us to confirm the presence of this disease in the face of arachneism. However, there is a clinical prediction rule (CPR), which allows us to predict the evolution of a bite towards a systemic condition and which would be useful in emergency rooms. We present the case of a woman who developed the systemic picture of loxoscelism to whom a new CPR was applied for the early detection of systemic loxoscelism, presenting adequate performance for early evaluation in emergency services.

Appendicitis Inflammatory Response Score in Comparison to Alvarado Score in Acute Appendicitis

Appendicitis is a common differential diagnosis of right lower quadrant pain. Clinical evaluation alone results in high negative appendicectomy rates. Alvarado scoring is the most commonly used clinical prediction rule. The study aimed to compare the recently developed appendicitis inflammatory response (AIR) score with the Alvarado score. This cross-sectional observational study included patients who underwent appendicectomy for clinical suspicion of appendicitis. The clinical and laboratory parameters required for obtaining Alvarado score and AIRS were gathered. Area under ROC curve was calculated for both Alvarado score and AIRS. The study included 130 patients (77 males and 53 females). The negative appendicectomy rate was 10.7%. The perforation rate was 10.3%. The area under ROC for Alvarado score was 0.821 and for AIR score was 0.901. The Alvarado score had a sensitivity of 72% and a specificity of 79% at score ≥6. The appendicitis inflammatory response score had a sensitivity of 98% for scores ≥5 and a specificity of 97% for score ≥6. The C-reactive protein (CRP) value was the best performing individual parameter with an area under ROC of 0.789, followed by WBC count with an area under ROC of 0.762. Appendicitis inflammatory response score is a recently developed score that outperforms the Alvarado score. AIR score has a higher specificity. The sound construction, gradation of parameters, the inclusion of CRP, and avoidance of subjective parameters make the AIR score an attractive clinical prediction rule which can decrease the rate of negative appendicectomy.

Development and validation of a clinical prediction rule for development of diabetic foot ulceration: an analysis of data from five cohort studies

IntroductionThe aim of the study was to develop and validate a clinical prediction rule (CPR) for foot ulceration in people with diabetes.Research design and methodsDevelopment of a CPR using individual participant data from four international cohort studies identified by systematic review, with validation in a fifth study. Development cohorts were from primary and secondary care foot clinics in Europe and the USA (n=8255, adults over 18 years old, with diabetes, ulcer free at recruitment). Using data from monofilament testing, presence/absence of pulses, and participant history of previous ulcer and/or amputation, we developed a simple CPR to predict who will develop a foot ulcer within 2 years of initial assessment and validated it in a fifth study (n=3324). The CPR’s performance was assessed with C-statistics, calibration slopes, calibration-in-the-large, and a net benefit analysis.ResultsCPR scores of 0, 1, 2, 3, and 4 had a risk of ulcer within 2 years of 2.4% (95% CI 1.5% to 3.9%), 6.0% (95% CI 3.5% to 9.5%), 14.0% (95% CI 8.5% to 21.3%), 29.2% (95% CI 19.2% to 41.0%), and 51.1% (95% CI 37.9% to 64.1%), respectively. In the validation dataset, calibration-in-the-large was −0.374 (95% CI −0.561 to −0.187) and calibration slope 1.139 (95% CI 0.994 to 1.283). The C-statistic was 0.829 (95% CI 0.790 to 0.868). The net benefit analysis suggested that people with a CPR score of 1 or more (risk of ulceration 6.0% or more) should be referred for treatment.ConclusionThe clinical prediction rule is simple, using routinely obtained data, and could help prevent foot ulcers by redirecting care to patients with scores of 1 or above. It has been validated in a community setting, and requires further validation in secondary care settings.

Defining a Clinical Prediction Rule to Diagnose Bacterial Gastroenteritis Requiring Empirical Antibiotics in an Emergency Department Setting: a Retrosepctive Review

BACKGROUNDGastroenteritis (GE) is a nonspecific term for various pathologic states of the gastrointestinal tract. Infectious agents usually cause acute gastroenteritis. At present, there are no robust decision-making rules that predict bacterial GE and hence dictate when to start antibiotics in patients presenting with acute GE to the ED. We aim to define a clinical prediction rule to diagnose bacterial gastroenteritis requiring empirical antibiotics in an emergency department setting. METHODSA 2-year retrospective case review was performed on all cases from July 2015 to June 2017 that presented acutely with infectious GE symptoms to the Emergency Department and then had stool cultures performed. The clinical parameters analysed included patient co-morbid conditions, physical examination findings, historical markers, point of care tests and other laboratory work. We then used multivariate logistic regression analysis on each group (Bacterial culture-positive GE and Bacterial culture-negative GE) to elucidate clinical criteria with the highest yield for predicting BGE. RESULTS756 patients with a mean age of 52 years, 52% of whom were female, and 48% male, were recruited into the study. Based on the data from these patients, we suggest using a scoring system to delineate the need for empirical antibiotics in patients with suspected bacterial GE based on six clinical and laboratory variables. A score 0-3 points on the suggests low risk (5.8%) of bacterial GE. A score of 4-5 points confers an intermediate risk of 28.5% and a score of 6-8 points confers a high risk of 66.7%. A cut-off of >5 points may be used to predict culture positive BGE with a 75% sensitivity and 75% specificity. The AUROC for the scoring system (range 0-8) is 0.812+0.016 (95% CI: 0.780-0.843) p-value <0.001. CONCLUSIONWhile this is a pilot study which will require further validation with a larger sample size, our proposed decision-making rule will potentially serve to improve diagnosis of BGE, reduce unnecessary prescribing of antibiotics which will in turn reduce antibiotic associated adverse events and save costs worldwide.

O08 Developing a DNA methylation signature for predicting rheumatoid arthritis using a machine learning pipeline

Background/Aims Early diagnosis and intervention improves outcomes of immune mediated rheumatic and musculoskeletal diseases (RMDs) but may be hampered by diagnostic uncertainty. The extent to which rationally selected molecular parameters add value to clinical characteristics for diagnostic prediction in undifferentiated disease states warrants investigation. B lymphocytes play an increasingly recognised role in rheumatoid arthritis (RA) pathogenesis, and cell-specific methylation patterns link environmental exposures to genetic risk. We derived and tested the practical utility of a B lymphocyte-derived DNA methylation signature for predicting RA in an early arthritis clinic cohort. Methods CD19+ B cell and peripheral blood mononuclear cell (PBMC) whole genome DNA methylation array data were available, respectively, from 109 inflammatory arthritis patients naïve to immunomodulatory drugs (Newcastle, UK; 38% confirmed to have a diagnosis of RA within 1 year) and 50 untreated undifferentiated arthritis (UA) patients (Leiden, The Netherlands; 68% classifiable RA within 1 year by 1987 ACR criteria versus alternate diagnoses). A bespoke machine learning pipeline employed a sequential model-based optimisation (SMBO) procedure for selecting, tuning and applying methods amongst ten feature-selection, six data-sampling and two classification algorithms in the Newcastle “training cohort.” The predictive performance of the resultant optimised molecular classifier was assessed in the independent Leiden “test cohort” alongside a previously described clinical prediction rule, using comparative area under receiver operating characteristic (AUROC) curves. A modification to the clinical prediction rule that incorporated a single parameter to reflect molecular classification was also assessed. The pipeline was implemented using the R machine learning package mlr. Results Using the SMBO approach, 27 CpGs maximally discriminatory for RA were selected from B lymphocyte DNA methylome training data, and a molecular classifier was derived using the random forest algorithm. Applied to the independent PBMC methylome in UA patients, the classifier and the validated Leiden prediction rule performed similarly in predicting RA (AUROC [95% CI] = 0.8 [0.66-0.94] versus 0.78 [0.64-0.92]). Interestingly, incorporating a molecular risk score based on the 27-CpG signature into the validated Leiden clinical prediction rule significantly improved its performance (AUROC [95% CI] = 0.89 [0.79-0.98] versus 0.78 [0.64-0.92]; p = 0.048). When applied to the sub-cohort of 25 patients in the Leiden cohort who were negative for anti-citrullinated peptide autoantibodies (ACPA), enhanced performance of the modified over the un-modified clinical prediction rule was maintained (AUROC [95% CI] = 0.82 [0.65-1] versus 0.70 [0.45-0.95], respectively), although the difference did not reach statistical significance in this smaller cohort. Conclusion We provide a proof of principle for the application of a B lymphocyte-derived epigenetic signature to enhance prediction of RA in UA patients using stored PBMCs. Further refinement of our pipeline represents a plausible means to expedite the diagnosis in undifferentiated RMDs and could offer pathophysiological insight. Disclosure N. Naamane: None. E. Niemantsverdriet: None. N. Thalayasingam: None. N. Nair: None. A.D. Clark: None. K. Murray: None. B. Hargreaves: None. L.N. Reynard: None. S. Eyre: None. A. Barton: None. A.H.M. van der Helm-van Mil: None. A.G. Pratt: None.