A prediction rule for clinical diagnosis of severe acute respiratory syndrome

Abstract

A prospective study was undertaken to identify clinical, radiographical, haematological and biochemical profiles of severe acute respiratory syndrome (SARS) patients.A prediction rule, which demarcates low from high risk patients for SARS in an outbreak situation was developed. A total of 295 patients with unexplained respiratory illnesses, admitted to Queen Mary Hospital, Hong Kong SAR, China, in March to July 2003, were evaluated for clinical, radiological, haematological and alanine transaminase (ALT) data daily for 3 days after hospitalisation.In total, 44 cases were subsequently confirmed to have SARS by RT-PCR (68.2%) and serology (100%). The scoring system of attributing 11, 10, 3, 3 and 3 points to the presence of independent risk factors, namely: epidemiological link, radiographical deterioration, myalgia, lymphopenia and elevated ALT respectively, generated high and low-risk (total score 11-30 and 0-10, respectively) groups for SARS. The sensitivity and specificity of this prediction rule in positively identifying a SARS patient were 97.7 and 81.3%, respectively. The positive and negative predictive values were 47.8 and 99.5%, respectively.The prediction rule appears to be helpful in assessing suspected patients with severe acute respiratory syndrome at the bedside, and should be further validated in other severe acute respiratory syndrome cohorts.

Role of laboratory variables in differentiating SARS-coronavirus from other causes of community-acquired pneumonia within the first 72 h of hospitalization

“…low prevalence rate) [1], and it is therefore important to interpret such findings in the context of patients considered to be at high risk of infection with SARS-CoV. Further evaluation of the role of monitoring routine laboratory changes in other cohorts and in combination with epidemiological, clinical, and radiological variables using the "clinical prediction rule" approach for risk stratification may be warranted [7,9]. It is worth pointing out again that the key component of epidemiological linkage can be elusive during the interepidemic period.…”

“…CAP was diagnosed by physicians in the medical department if symptoms were suggestive of acute lower respiratory infection (e.g. fever, cough with or without sputum production, dyspnea) and were accompanied by acute lung infiltrates evident on chest radiographs, and if the patient had been neither hospitalized within the previous 14 days nor residing in a long-term-care facility [3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Chest radiographs were reviewed retrospectively by a radiologist blinded to all clinical information except for a provisional diagnosis of CAP.…”

“…Previous studies have shown that certain hematological/biochemical changes (e.g. lymphopenia, thrombocytopenia, elevated liver transaminases) may be useful in the diagnosis of SARS [7][8][9][10][11][12].…”

“…By day 3 of hospitalization, age (p<0.0001), change in ANC (p=0.0003), and change in bilirubin (p=0.0065) were discriminating variables. A model combining age <65 years, a change in ANC of >−3×10 9 /l, and a change in bilirubin of ≥0 mmol/l had a sensitivity of 43% and a specificity of 95% for SARS (AUC 0.90). There are only a few laboratory features (including lymphopenia) that clearly discriminate SARS from other causes of CAP.…”

Possible case scenarios and logistic issues in H5N1 pandemic

“…Rapid diagnosis of H5N1 requires vigilance, which needs to be balanced against over‐investigation of low‐risk patients, thereby wasting limited resources and disrupting daily health‐care logistics. Prediction rules, available for SARS, which embraces epidemiological history, clinical features and basic investigation should be constructed as soon as outbreaks occur using a pre‐established electronic centralized database 13 . Laboratory confirmation should be carried out by using real‐time reverse transcription‐polymerase chain reaction assays of nasopharyngeal aspirate, and later done serologically.…”