Research ArticleOriginal ResearchA

Predictors of Adverse Outcomes in Uncomplicated Lower Respiratory Tract Infections

Michael Moore, Beth Stuart, Mark Lown, Ann Van den Bruel, Sue Smith, Kyle Knox, Matthew J. Thompson and Paul Little
The Annals of Family Medicine May 2019, 17 (3) 231-238; DOI: https://doi.org/10.1370/afm.2386

Article Figures & Data

Tables

  • Table 1

    Attribution of Diagnosis Reported on X-rays for All Reports and Those 8 to 30 Days

    All X-raysX-rays 8-30 Days
    Not pneumonia1,539 (86.4%)938 (88.3%)
    Unlikely pneumonia8 (0.5%)4 (0.4%)
    Definitely pneumonia184 (10.3%)95 (9.0%)
    Probable pneumonia28 (1.6%)12 (1.1%)
    Possible pneumonia18 (1.0%)9 (0.9%)
    Cancer4 (0.2%)3 (0.3%)
    Tuberculosis1 (0.1%)1 (0.1%)
  • Table 2

    Contribution of Categories to Total Adverse Event Tally

    TotalAlso Contributed to This Category
    X-ray Pneumonia >1 wkClinical (Notes Review) PneumoniaAdmission After Day 1DeathTotal Excluding Duplicates (Hierarchy: Death, Admission, Pneumonia)
    X-ray pneumonia >1 w120N/A523092
    Clinical (notes review) pneumonia345N/A6122
    Admission after day 1196236N/A14182
    Death290114N/A29
    Total: 325
  • Table 3

    Risk Factors at First Consultation for Severe Adverse Outcomes (Death or Hospitalization From Lower Respiratory Tract Infection Complications Within 30 Days or Late Onset or Pneumonia Confirmed by X-ray or Reconsultation 8 to 30 Days After First Consultation) (n = 325)

    Proportion of Patients Suffering Adverse OutcomeRisk RatioAdjusted Risk Ratioa
    Characteristic + No. (%)Characteristic – No. (%)Ratio (95% CI)P ValueRatio (95% CI)P Value
    Patient characteristics
    Age ≥65 years163/7,921 (2.1)162/20,925 (0.8)2.66 (2.14-3.30)<0.0012.15 (1.72-2.67)<0.001
    Male157/11,743 (1.3)16/17,098 (0.09)1.36 (1.10-1.70)0.0051.20 (0.96-1.50)0.112
    Influenza vaccine152/9,842 (1.5)173/19,004 (0.9)170 (1.37-2.11)<0.0010.80 (0.61-1.05)0.110
    Pneumovax <10 y95/5,294 (1.8)230/23,552 (1.0)1.84 (1.45-2.33)<0.0011.01 (0.79-1.29)0.939
    Ever smoked1,997/15,165 (13.1)120/13,212 (0.9)1.43 (1.14-1.79)0.0021.25 (0.99-1.57)0.060
    Any comorbidity210/13,100 (1.6)115/15,746 (0.7)2.19 (1.75-2.75)<0.0011.57 (1.24-1.99)<0.001
    Lung comorbidity112/7,461 (1.5)213/21,385 (1.0)1.51 (1.20-1.89)<0.0011.00 (0.75-1.32)0.976
    Steroids or bronchodilators93/6,537 (1.4)218/20,997 (1.0)1.37 (1.08-1.75)0.0100.87 (0.67-1.12)0.278
    Living in deprived areab69/5,750 (1.2)256/23,096 (1.1)1.08 (0.83-1.41)0.5561.10 (0.81-1.50)0.530
    Presenting symptoms
    Shortness of breath246/18,498 (1.3)77/10,229 (0.8)1.77 (1.37-2.28)<0.0011.55 (1.18-2.04)0.002
    Fever126/10,978 (1.1)198/17,800 (1.1)1.03 (0.83-1.29)0.7821.01 (0.80-1.29)0.919
    Chills128/9,146 (1.4)195/19,621 (1.0)1.41 (1.13-1.76)0.0021.41 (1.11-1.79)0.005
    Chest pain146/10,644 (1.4)178/18,130 (1.0)1.40 (1.12-1.74)0.0031.31 (1.03-1.67)0.028
    Confusion25/1,860 (1.3)300/26,968 (1.1)1.21 (0.81-1.81)0.3601.11 (0.75-1.65)0.605
    No coryza184/13,029 (1.4)139/15,718 (0.9)1.60 (1.28-1.99)<0.0011.60 (1.27-2.02)<0.001
    Headache131/13,254 (1.0)193/15,507 (1.2)0.79 (0.64-0.99)0.0410.73 (0.57-0.94)0.014
    Muscle aches112/10,497 (1.1)211/18,267 (1.2)0.92 (0.74-1.16)0.4950.82 (0.63-1.07)0.140
    Diarrhea27/2,508 (1.1)298/26,312 (1.1)0.95 (0.64-1.41)0.8000.89 (0.60-1.31)0.557
    Sputum: purulent186/18,221 (1.0)139/10,621 (1.3)0.78 (0.63-0.97)0.0260.70 (0.57-0.87)0.001
    Sputum bloody or rusty16/1,023 (1.6)309/27,819 (1.1)1.19 (0.92-1.52)0.1790.99 (0.76-1.27)0.917
    Clinical examination findings
    Severity assessment >5/10198/11,893 (1.7)126/16,943 (0.7)2.24 (1.79-2.80)<0.0011.48 (1.13-1.93)0.004
    Respiratory rate >24/min61/2,885 (2.1)263/25,844 (1.0)2.08 (1.58-2.74)<0.0011.42 (1.07-1.88)0.016
    Temperature >37.8°C40/1,656 (2.4)28/27,169 (0.1)2.31 (1.67-3.21)<0.0011.82 (1.28-2.58)0.001
    Pulse >100/min45/2,801 (1.6)280/26,033 (1.1)1.49 (1.09-2.04)0.0121.03 (0.75-1.40)0.858
    O2 sat <95%60/1,698 (3.5)205/22,047 (0.9)3.80 (2.86-5.05)<0.0012.76 (2.08-3.65)<0.001
    SBP<90 or DBP <60 mm Hg39/2,193 (1.8)286/26,653 (1.1)1.66 (1.19-2.31)0.0031.72 (1.20-2.46)0.003
    Crackles1,922/12,256 (15.7)133/16,582 (0.8)1.95 (1.57-2.43)<0.0011.39 (1.01-1.90)0.044
    Bronchial breathing37/2,166 (1.7)288/26,667 (1.1)1.58 (1.13-2.22)0.0081.07 (0.71-1.60)0.758
    Wheezing107/7,071 (1.5)218/21,765 (1.0)1.51 (1.20-1.90)<0.0010.89 (0.67-1.18)0.412

    • O2 sat = oxygen saturation; SBP = systolic blood pressure; DBP = diastolic blood pressure.

    • ↵a Adjusted by multivariable analysis for antibiotic prescribing, clustering, and other covariates in same category (ie, patient characteristics, presenting symptoms, or examinations findings, respectively).

    • ↵b Living in area of England with deprivation index in top 10%.

  • Table 4

    Independent Predictors of Adverse Outcome (P <.01)

    Risk Ratio (95% CI)P Value
    O2 sat <95%2.30 (1.74-3.04)<.001
    Age ≥65 years2.3 (1.65-2.75)<.001
    SBP <90 or DBP <60 mm Hg1.59 (1.13-2.25)0.008
    Temperature >37.8°C1.81 (1.32-2.47)<.001
    Any comorbiditya1.55 (1.17-2.05)0.002
    No coryza1.50 (1.17-1.92)0.001
    Severity assessment >5/101.45 (1.11-1.90)0.007
    Chest pain1.43 (1.11-1·86)0.006

    • SBP = systolic blood pressure; DBP = diastolic blood pressure.

    • ↵a Any comorbidity as defined in the notes review.

  • Table 5

    Distribution of the Score in the Presenting Population

    ScoreN (%) of Total Cohort With Each Score
    None1,982 (8.4)
    15,529 (23.4)
    26,946 (29.4)
    35,482 (23.2)
    42,672 (11.3)
    5783 (3.3)
    6201 (0.9
    718 (0.1)
    83 (0.01)

    • The denominator is 23,616, representing participants with complete data for all variables in the model.

  • Table 6

    Sensitivity, Specificity, and Predictive Values for Each Score and for Suggested Cutoff Points

    Cutoff Scoreto UseN (%) ofTotal CohortSensitivity,%Specificity,%NPV,%PPV,%LR+LR–
    ≥121,634 (91.6)99.28.599.91.21.080.09
    ≥216,105 (68.2)91.632.199.71.51.350.26
    ≥39,159 (38.8)69.861.699.52.01.820.49
    ≥43,677 (15.6)39.384.799.22.82.570.72
    ≥51,005 (4.3)21.895.999.15.75.360.82
    ≥6222 (0.9)8.499.199.09.99.810.92
    ≥721 (0.1)1.999.998.923.827.860.98
    ≥83 (0.01)0.8100.098.966.7178.270.99
    ≤214,457 (61.2)30.238.498.00.50.491.82
    3-48,154 (34.5)69.861.699.52.01.820.49
    ≥51,005 (4.3)21.895.999.15.75.360.82

    • NPV = negative predictive value; PPV = positive predictive value; LR+ = positive likelihood ratio; LR– = negative likelihood ratio.

    • The denominator is 23,616, representing participants with complete data for all variables in the model.

Additional Files

  • Supplemental Appendix

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  • The Article in Brief

    Predictors of Adverse Outcomes in Uncomplicated Lower Respiratory Tract Infections

    Michael Moore , and colleagues

    Background Although antibiotics provide little benefit for acute uncomplicated respiratory tract infections, patients and clinicians are often concerned about more severe or prolonged illness and complications. To help decision making in such cases, this study investigates the clinical features that predict future serious adverse outcomes of respiratory tract infections.

    What This Study Found In routine primary care practice, serious adverse outcomes occur in only 1% of adult patients with lower respiratory tract infection, but such outcomes may be predicted with moderate accuracy. In this study of 28,846 adult patients with lower respiratory tract infections, researchers recorded patient characteristics and clinical findings and identified adverse events (i.e., late onset pneumonia, hospital admission, or death) during a 30-day period following the patient visit. Serious adverse outcomes occurred in 325 of 28,846 patient visits. Three categories of factors independently predicted adverse outcomes from lower respiratory tract infection: severity of patient symptoms, patient vulnerability to serious illness, and the physiological impact of symptoms. These factors can be used to predict adverse outcomes by conversion to an eight-point score.

    Implications

    • An 8-item clinical prediction score for respiratory tract infections may help clinicians target prescribing based on predicted risk and identify a small group of high risk patients who may benefit from closer monitoring.

  • Annals Journal Club

    May/Jun 2019: Predicting Adverse Outcomes in Lower Respiratory Tract Infections


    Spencer S. Conte, DO, Grant Family Medicine; Michael E. Johansen, MD, MS, Associate Editor

    The Annals of Family Medicine encourages readers to develop a learning community to improve health care and health through enhanced primary care. Participate by conducting a RADICAL journal club. RADICAL stands for Read, Ask, Discuss, Inquire, Collaborate, Act, and Learn. We encourage diverse participants to think critically about important issues affecting primary care and act on those discussions.1

    HOW IT WORKS

    In each issue, the Annals selects an article and provides discussion tips and questions. Take a RADICAL approach to these materials and post a summary of your conversation in our online discussion. (Open the article and click on "TRACK Discussion/ Submit a comment.") Discussion questions and information are online at: http://www.AnnFamMed.org/site/AJC/.

    CURRENT SELECTION

    Article for Discussion

    Moore M, Stuart B, Little P, et al. Predictors of adverse outcomes in uncomplicated lower respiratory tract infections. Ann Fam Med. 2019;17(3):231-238.

    Discussion Tips

    Acute lower respiratory tract infections are common in primary care. This article uses a prospective cohort design to predict the likelihood that patients will have a serious adverse outcome (late-onset pneumonia, hospitalization, death). Consider how this study could influence the care you provide to patients with lower respiratory tract infections (eg, closer follow-up, antibiotics).

    Discussion Questions

    • What question is asked by this study and why does it matter?
    • What is a prospective cohort study? How does it differ from a retrospective cohort study? What advantages and disadvantages does this design allow?
    • What was the predicted outcome? Could the outcomes have been grouped together? How were variables selected for inclusion in the prediction model?
    • What variables were included in the final model to predict adverse outcomes? Could others have been included (eg, including unmeasured variables)?
    • What are discrimination and calibration? Why are these important for prediction models?
    • What are likelihood ratios and how can they be used clinically?
    • What are the main study findings? How often did late complications occur in the study population?
    • To what degree can the findings be accounted for by:
      • How patients were selected, excluded, or lost to follow-up?
      • How the main variables were measured?
      • Confounding (false attribution of causality because 2 variables discovered to be associated actually are associated with a 3rd factor)?
      • Chance?
      • Time of year?
      • Physicians' interpretation of overall assessment/"gut feeling"/severity?
    • How comparable is the study sample to your practice/patient population?
    • What contextual factors are important for interpreting the findings?
    • How actionable are the results of the prediction model? Would you use the risk score in your clinical practice? Would you consider changing treatment recommendations based on this study? Closer follow-up? Antibiotic prescribing?
    • Is this study applicable to areas with more limited medical resources?
    • What researchable questions remain?
    • Could (or should) this research be replicated? What barriers would prevent similar studies from achieving the same power?

    References

    1. Stange KC, Miller WL, McLellan LA, et al. Annals Journal Club: It's time to get RADICAL. Ann Fam Med. 2006;4(3):196-197.

  • Table 6-REVISED

    Data added to Table 6

    Files in this Data Supplement:
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