Table 3.

Prediction of extrapulmonary complications.

	OR (95% CI)
Age: 80+	2·27 (0·49 - 9·92)
PMH: Heart	2·13 (0·63 - 7·41)
PMH: HTN	1·74 (0·81 - 3·69)
PMH: Dementia	3·60 (0·58 - 25·33)
PMH: CKD	4·56 (1·36 – 17·27)
Smoker	1·88 (0·80 - 4·42)
Obese	2·64 (1·23 - 5·84)
ED: RR high	1·63 (0·79 - 3·35)
ED: SpO2 low	1·34 (0·60 - 2·93)
RNAaemic	2·81 (1·26 - 6·36)
N	191
AIC	222·25
AUROC	0·73

Potential predictors of extrapulmonary complications (EPCs) included: demographic features (age 60+ or 80+, sex), past medical history features (lung disease, cancer, diabetes, immunosuppression, heart disease, hypertension, angiotensin converting enzyme inhibitor or angiotensin receptor blocker use, stroke, dementia, deep venous thrombosis or pulmonary embolus, chronic kidney disease, tobacco smoking, obesity), binary indicators of abnormal ED vital signs (low or high mean arterial pressure, low or high heart rate, low or high respiratory rate, low oxygen saturation, low or high temperature), pneumonia on initial chest X-ray or CT, and patient-reported symptoms on enrollment excluding those constitutive of extrapulmonary diagnosis (fever, chills, cough, sore throat, congestion, shortness of breath, chest pain, myalgias). Laboratory values were not included as many were constitutive of extrapulmonary diagnoses.

To prevent over-fitting, predictors were selected via elastic net regression of EPC (1 if a patient had one or more EPC, 0 if none) on these features with 10-fold cross-validation, selecting the regularization parameter λ minimising mean cross-validated error, and yielding the features in the table above. In a logistic model regressing EPC on these features, significant predictors of EPC included: chronic kidney disease, obesity (BMI>30), and RNAaemia. RNAaemia was associated with 2·8 times the odds of EPC, comparable in magnitude to the association between obesity and development of EPC. Mean cross-validated area under the receiver-operating characteristic curve (AUROC) of the model in predicting EPC was 0·73.