Development and Validation of a Clinical Decision Rule for the Diagnosis of Influenza

Methods

We identified 2 prospective cohort studies^5,14 that reported the accuracy of signs and symptoms of influenza for adults in the outpatient setting and used an acceptable reference standard (polymerase chain reaction [PCR] or culture). Details of their study designs have been published previously.^5,14 The study populations had many similarities, including similar mean ages, age ranges, sex distributions, and percentages of patients with cough, sore throat, rhinitis, headache, chills, and fatigue (Table 1). Though the populations differed somewhat regarding the proportion with influenza and the proportion presenting with fever, we felt that the increased generalizability of creating a decision rule using patients from 2 different countries justified combining the datasets. Also, the prevalence of influenza in the final dataset is very similar to that seen during the typical peak influenza season in the United States, and there is no evidence of spectrum bias in previous studies. We used the random number function of an Excel spreadsheet (Microsoft Corp, Redmond, WA) to divide the dataset into derivation (70%; n = 326) and validation (30%; n = 133) subgroups.

The dependent, or outcome, variable was influenza as diagnosed by PCR of culture. Independent, or predictor, variables statistically significant at P < .05 in the univariate analysis (Table 2) were used to develop a multivariate model. A backward, stepwise logistic regression was performed using the derivation group and removing variables with P > .20. This model was simplified into a point score, flu score 1, based on the odds ratios (approximately twice the odds ratio was used to avoid half points). We then created 2 additional models that included interaction terms for fever, cough, and acute onset (flu score 2) and fever and cough (flu score 3).

We also proposed 2 point scores (flu scores 4 and 5) based on Monto et al's¹³ previously published multivariate model. These scores differed in the approach they took to assigning points to each clinical variable, with flu score 5 taking a more simplified approach. We adjudicated minor discrepancies in variable definitions as follows: (1) Monto et al¹³ defined acute onset as symptoms present for <36 hours, whereas our datasets defined it as presentation to a physician within 48 hours of the onset of symptoms; 2) “stuffy nose” and “rhinitis” in our datasets were considered equivalent to “nasal congestion” in the Monto et al¹³ study, (3) we considered “fatigue” equivalent to “weakness,” and (4) we did not have data regarding loss of appetite, so that variable was omitted from the point scores. In Monto et al's¹³ model, acute onset was associated with a decreased likelihood of influenza. Because this is different from all other studies in the literature,^10,11 we assigned acute onset positive points in each model (ie, predicting an increased risk of influenza). Two subjects were excluded from this validation because of missing information.

Cutpoints for flu scores 1 through 5 were determined by visual inspection of the model as applied to the derivation group of 326 patients. Our goal was to create low-, moderate-, and high-risk groups, with the low-risk group having a probability of influenza <10% and the high-risk group having a probability of influenza of at least 50%. These cutoffs were chosen because they corresponded to reasonable no-test/test and test/treat thresholds based on the opinion of the research team (all but one of whom are experience primary care physicians), an informal email poll of 20 academic generalists, and a previous decision threshold analysis.¹⁵ Patients in the low-risk group would not require further testing because disease had been ruled out, and those in the high-risk group would not require further testing because disease had been “ruled in.” We then evaluated these models and cutpoints using the validation group of 133 patients.

Finally, the combined adult dataset was used to validate 2 simple heuristics previously reported in the literature.¹² These heuristics were the “fever and cough rule” (influenza is diagnosed if both fever and cough are present) and the “fever, cough, and acute onset rule” (influenza is diagnosed if all 3 are present). The simple heuristics were evaluated using the entire combined dataset of 459 patients because their definitions of abnormal were known at the outset of the study.

The accuracy of clinical decision rules and simple heuristics was evaluated using standard measures such as sensitivity, specificity, LRs, and posttest probabilities. Unless otherwise noted, all statistical analyses were performed using Stata software (version 11.0, Stata Corp, College Station, TX).