Tweet
BMC Infect Dis. 2016 Sep 22;16(1):503.
Classification and Regression Tree (CART) analysis to predict influenza in primary care patients.
Zimmerman RK1,2, Balasubramani GK3, Nowalk MP3, Eng H3, Urbanski L4, Jackson ML5, Jackson LA5, McLean HQ6, Belongia EA6, Monto AS7, Malosh RE7, Gaglani M8, Clipper L8, Flannery B9, Wisniewski SR3.
Author information
Abstract
BACKGROUND:
The use of neuraminidase-inhibiting anti-viral medication to treat influenza is relatively infrequent. Rapid, cost-effective methods for diagnosing influenza are needed to enable appropriate prescribing. Multi-viral respiratory panels using reverse transcription polymerase chain reaction (PCR) assays to diagnose influenza are accurate but expensive and more time-consuming than low sensitivity rapid influenza tests. Influenza clinical decision algorithms are both rapid and inexpensive, but most are based on regression analyses that do not account for higher order interactions. This study used classification and regression trees (CART) modeling to estimate probabilities of influenza.
METHODS:
Eligible enrollees ≥ 5 years old (n = 4,173) who presented at ambulatory centers for treatment of acute respiratory illness (≤7 days) with cough or fever in 2011-2012, provided nasal and pharyngeal swabs for PCR testing for influenza, information on demographics, symptoms, personal characteristics and self-reported influenza vaccination status.
RESULTS:
Antiviral medication was prescribed for just 15 % of those with PCR-confirmed influenza. An algorithm that included fever, cough, and fatigue had sensitivity of 84 %, specificity of 48 %, positive predictive value (PPV) of 23 % and negative predictive value (NPV) of 94 % for the development sample.
CONCLUSIONS:
The CART algorithm has good sensitivity and high NPV, but low PPV for identifying influenza among outpatients ≥5 years. Thus, it is good at identifying a group who do not need testing or antivirals and had fair to good predictive performance for influenza. Further testing of the algorithm in other influenza seasons would help to optimize decisions for lab testing or treatment.
KEYWORDS:
Clinical decision tools; Influenza; Recursive partitioning
PMID: 27659721 DOI: 10.1186/s12879-016-1839-x
[PubMed - as supplied by publisher]
Classification and Regression Tree (CART) analysis to predict influenza in primary care patients.
Zimmerman RK1,2, Balasubramani GK3, Nowalk MP3, Eng H3, Urbanski L4, Jackson ML5, Jackson LA5, McLean HQ6, Belongia EA6, Monto AS7, Malosh RE7, Gaglani M8, Clipper L8, Flannery B9, Wisniewski SR3.
Author information
Abstract
BACKGROUND:
The use of neuraminidase-inhibiting anti-viral medication to treat influenza is relatively infrequent. Rapid, cost-effective methods for diagnosing influenza are needed to enable appropriate prescribing. Multi-viral respiratory panels using reverse transcription polymerase chain reaction (PCR) assays to diagnose influenza are accurate but expensive and more time-consuming than low sensitivity rapid influenza tests. Influenza clinical decision algorithms are both rapid and inexpensive, but most are based on regression analyses that do not account for higher order interactions. This study used classification and regression trees (CART) modeling to estimate probabilities of influenza.
METHODS:
Eligible enrollees ≥ 5 years old (n = 4,173) who presented at ambulatory centers for treatment of acute respiratory illness (≤7 days) with cough or fever in 2011-2012, provided nasal and pharyngeal swabs for PCR testing for influenza, information on demographics, symptoms, personal characteristics and self-reported influenza vaccination status.
RESULTS:
Antiviral medication was prescribed for just 15 % of those with PCR-confirmed influenza. An algorithm that included fever, cough, and fatigue had sensitivity of 84 %, specificity of 48 %, positive predictive value (PPV) of 23 % and negative predictive value (NPV) of 94 % for the development sample.
CONCLUSIONS:
The CART algorithm has good sensitivity and high NPV, but low PPV for identifying influenza among outpatients ≥5 years. Thus, it is good at identifying a group who do not need testing or antivirals and had fair to good predictive performance for influenza. Further testing of the algorithm in other influenza seasons would help to optimize decisions for lab testing or treatment.
KEYWORDS:
Clinical decision tools; Influenza; Recursive partitioning
PMID: 27659721 DOI: 10.1186/s12879-016-1839-x
[PubMed - as supplied by publisher]