A Clinical Prediction Rule for Diagnosing Severe Acute Respiratory Syndrome in the Emergency Department

Leung, Gabriel M.; Rainer, Timothy; Lau, FL; Wong, Irene; Tong, Anna; Wong, T. Y.; Kong, James; Hedley, Anthony J.; Lam, T.Y.D.

doi:10.7326/0003-4819-141-5-200409070-00106

Cited by 42 publications

(33 citation statements)

References 24 publications

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“…This higher incidence of virologic failure, coupled with the limited resources in clinical practice as compared with clinical trials, emphasizes the need for a simple prog-nostic tool to identify patients at increased risk of virologic failure. As is the case with diabetes, congestive heart failure, and other chronic diseases, a prediction rule to stratify HIV-positive patients' risk of future virologic failure could be used to target interventions and guide the allocation of resources [8][9][10][11][12].…”

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confidence: 99%

Predicting Virologic Failure in an HIV Clinic

Robbins

Johnson²,

Chang³

et al. 2010

Clinical Infectious Diseases

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Background We sought to use data captured in the electronic health record (EHR) to develop and validate a prediction rule for virologic failure in patients being treated for HIV infection. Methods We used EHRs at two Boston tertiary care hospitals, Massachusetts General Hospital and Brigham and Women's Hospital, to identify HIV-infected patients who were virologically suppressed (HIV RNA ≤400 copies/mL) on antiretroviral therapy between 1/1/05 and 12/31/06. We used a multivariable logistic model with data from Massachusetts General Hospital to derive a one-year virologic failure prediction rule. The model was validated using data from the Brigham and Women's Hospital. We then simplified the scoring scheme to develop a clinical prediction rule. Results The one-year virologic failure prediction model, using data from 712 Massachusetts General Hospital patients, demonstrated good discrimination (c-statistic 0.78) and calibration (χ2 =6.6, p =0.58). The validation model, based on 362 Brigham and Women's Hospital patients, also showed good discrimination (c-statistic 0.79) and calibration (χ2 =1.9, p =0.93). The clinical prediction rule included seven predictors, Sub-optimal Adherence, CD4 count <100/μL, Drug and/or Alcohol Abuse, Heavily ART Experienced, Missed ≥1Appointment, Prior Virologic Failure, and Suppressed ≤12 months, and appropriately stratified patients in the validation dataset into low, medium and high risk groups, with one-year virologic failure rates of 3.0%, 13.0% and 28.6%. Conclusions A risk score based on seven variables available in the EHR predicts HIV virologic failure at one year and could be used for targeted interventions to improve outcomes in HIV disease.

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confidence: 99%

Predicting Virologic Failure in an HIV Clinic

Robbins

Johnson²,

Chang³

et al. 2010

Clinical Infectious Diseases

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“…The scores were reported to have greater than 90% sensitivity and were found to be highly reliable when validated in a separate cohort [105,107]. A more recent study from Hong Kong, which included nearly one third of the SARS cases from 2003, reported similar results with key variables including exposure history, symptoms, and laboratory values [104]. Although determination of which prediction rule will be most effective is not known, these studies provide compelling support for integration of ED-based decision guidelines in future respiratory outbreaks.…”

Section: Secondary Prevention Measures For Sars Have Involved Methodsmentioning

confidence: 85%

“…The WHO definition of SARS cases, although useful for epidemiologic purposes, was found to be insufficiently sensitive for assessing patients in ED triage areas. Consequently, physicians had to develop clinical prediction rules that could more accurately identify patients with SARS during an acute outbreak [104][105][106][107]. One group from Taiwan derived a simple SARS decision rule that relied on combinations of symptoms and laboratory findings.…”

Section: Secondary Prevention Measures For Sars Have Involved Methodsmentioning

confidence: 99%

“…Evidence and support for ED-based primary prevention strategies such as influenza vaccination or prophylaxis exists [67], although practical challenges regarding such issues as education, counseling, and sustainable funding present ongoing challenges. Secondary preventive measures using clinical decision rules to rapidly identify and sequester patients with diseases have also been demonstrated to be effective in ED settings [31,104], but are by no means failsafe; recognized limitations of decision guidelines include difficulties with ensuring routine and consistent application, and need to establish generalizability.…”

Section: Public Health Interventionsmentioning

confidence: 99%

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Communicable Respiratory Threats in the ED: Tuberculosis, Influenza, SARS, and Other Aerosolized Infections

Rothman

Hsieh

Yang

2006

Emergency Medicine Clinics of North America

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Respiratory infections are the most common communicable infectious diseases. EDs are the front line for patients with respiratory infections because of their acute nature and because the ED is the principal site of health care for those at highest risk. These diseases include influenza, tuberculosis, and measles, together accounting for 25% of infectious causes of death worldwide. These are emerging and biothreat agents that follow the same route of transmission, such as pneumonic plague. We discuss epidemiology, pathogenesis, diagnosis, and treatment of each agent. Emphasis is on the ED's role as a public health prevention arena, with attention to education and disease prevention, early identification of disease in patients at risk, and reduction of illnesses.

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“…The overall risk score was calculated by adding each component together. 21,22 The adequacy of the model was assessed by the c-statistic and diagnostic test. C-statistic is identical to the area under the receiver operating characteristic (ROC) curve for dichotomous outcomes.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a predictive model for all-cause hospital readmissions in Winnipeg, Canada

Yang

Metge

et al. 2015

J Health Serv Res Policy

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A Clinical Prediction Rule for Diagnosing Severe Acute Respiratory Syndrome in the Emergency Department

Abstract: Our findings suggest that a simple model that uses clinical data at the time of presentation to an emergency department during an acute outbreak predicted the incidence of SARS and provided good diagnostic utility.

Cited by 42 publications

References 24 publications

Predicting Virologic Failure in an HIV Clinic

Predicting Virologic Failure in an HIV Clinic

Communicable Respiratory Threats in the ED: Tuberculosis, Influenza, SARS, and Other Aerosolized Infections

Development and validation of a predictive model for all-cause hospital readmissions in Winnipeg, Canada

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