Assessment of Pneumonia Severity Indices as Mortality Predictors

BACKGROUND The leading cause of infectious disease death in the United States is community-acquired pneumonia (CAP). Several pneumonia severity indices exist and are widely used as tools to assist physicians regarding site of care based on risk of death. However, limited data exists that discerns which of the most commonly used severity scores is the best predictor of mortality across multiple time points. The objective of this study is to determine the best mortality predictor at different time points between four of the most commonly used pneumonia severity scores. METHODS This was a secondary analysis of a prospective, multicenter, population-based, observational study of patients hospitalized with CAP in the city of Louisville, KY. The severity indices used were the American Thoracic Society (ATS) criteria, the Pneumonia Severity Index (PSI), the British Thoracic Society criteria (CURB-65), Quick Sepsis-Related Organ Failure Assessment (QSOFA), and direct ICU admission to represent physician discretion. The accuracy, kappa statistic, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the ability to predict in-hospital, 30-day, 6-month, and 1-year mortality. 95% confidence intervals for each variable were generated by bootstrapping with random sampling and resampling of the subjects 1000 times. In addition, the area under the curve (AUC) was calculated for each severity score and mortality time point. RESULTS There were 6013 eligible patients included in this analysis with data collected between the years 2014 and 2016. At each time point, the QSOFA had the highest sensitivity and NPV, while the PSI had the highest specificity and PPV. QSOFA had the highest accuracy for in-hospital mortality, 30-day mortality, and 6-month mortality, and the CURB-65 had highest mortality for 1-year mortality. The QSOFA had the highest kappa statistic for in-hospital mortality, the CURB-65 had the highest kappa statistic for 30-day mortality, and the PSI had the highest kappa statistic for 6-month and 1-year mortality. The AUC was highest for the ATS criteria for in-hospital mortality, and was highest for the PSI at the remaining time points. CONCLUSIONS The results of this study show that QSOFA and the PSI are the most reliable severity indices for mortality predictions based on these measures. QSOFA was found, on average, to have the highest accuracy, sensitivity, and NPV. Additionally, PSI was found, on average, to have the highest kappa statistic, specificity, and PPV. The AUC, on average, was best with PSI as the predictor. QSOFA is most capable of making true negative predictions and the PSI is the most capable of making true positive predictions across the four time points

Impact of Temperature Relative Humidity and Absolute Humidity on the Incidence of Hospitalizations for Lower Respiratory Tract Infections Due to Influenza, Rhinovirus, and Respiratory Syncytial Virus: Results from Community-Acquired Pneumonia Organization (CAPO) International Cohort Study

Abstract Background: Transmissibility of several etiologies of lower respiratory tract infections (LRTI) may vary based on outdoor climate factors. The objective of this study was to evaluate the impact of outdoor temperature, relative humidity, and absolute humidity on the incidence of hospitalizations for lower respiratory tract infections due to influenza, rhinovirus, and respiratory syncytial virus (RSV). Methods: This was a secondary analysis of an ancillary study of the Community Acquired Pneumonia Organization (CAPO) database. Respiratory viruses were detected using the Luminex xTAG respiratory viral panel. Climate factors were obtained from the National Weather Service. Adjusted Poisson regression models with robust error variance were used to model the incidence of hospitalization with a LRTI due to: 1) influenza, 2) rhinovirus, and 3) RSV (A and/or B), separately. Results: A total of 467 hospitalized patients with LRTI were included in the study; 135 (29%) with influenza, 41 (9%) with rhinovirus, and 27 (6%) with RSV (20 RSV A, 7 RSV B). The average, minimum, and maximum absolute humidity and temperatur e variables were associated with hospitalization due to influenza LRTI, while the relative humidity variables were not. None of the climate variables were associated with hospitalization due to rhinovirus or RSV. Conclusions: This study suggests that outdoor absolute humidity and temperature are associated with hospitalizations due to influenza LRTIs, but not with LRTIs due to rhinovirus or RSV. Understanding factors contributing to the transmission of respiratory viruses may assist in the prediction of future outbreaks and facilitate the development of transmission prevention interventions

Predicting 30-Day Mortality in Hospitalized Patients with Community-Acquired Pneumonia Using Statistical and Machine Learning Approaches

Background: Predicting if a hospitalized patient with community-acquired pneumonia (CAP) will or will not survive after admission to the hospital is important for research purposes as well as for institution of early patient management interventions. Although population-level mortality prediction scores for these patients have been around for many years, novel patient-level algorithms are needed. The objective of this study was to assess several statistical and machine learning models for their ability to predict 30-day mortality in hospitalized patients with CAP. Methods: This was a secondary analysis of the University of Louisville (UofL) Pneumonia Study database. Six different statistical and/or machine learning methods were used to develop patientlevel prediction models for hospitalized patients with CAP. For each model, nine different statistics were calculated to provide measures of the overall performance of the models. Results: A total of 3249 unique hospitalized patients with CAP were enrolled in the study, 2743 were included in the model building (training) dataset, while the remaining 686 were included in the testing dataset. From the full population, death at 30-days post discharge was documented in 458 (13.4%) patients. All models resulted in high variation in the ability to predict survivors and non-survivors at 30 days. Conclusions: In conclusion, this study suggests that accurate patient-level prediction of 30-day mortality in hospitalized patients with CAP is difficult with statistical and machine learning approaches. It will be important to evaluate novel variables and other modeling approaches to better predict poor clinical outcomes in these patients to ensure early and appropriate interventions are instituted

Real-time enrollment dashboard for multisite clinical trials

Objective: Achieving patient recruitment goals is critical for the successful completion of a clinical trial. We designed and developed a web-based dashboard for assisting in the management of clinical trial screening and enrollment. Materials and methods: We use the dashboard to assist in the management of two observational studies of community-acquired pneumonia. Clinical research associates and managers using the dashboard were surveyed to determine its effectiveness as compared with traditional direct communication. Results: The dashboard has been in use since it was first introduced in May of 2014. Of the 23 staff responding to the survey, 77% felt that it was easier or much easier to use the dashboard for communication than to use direct communication. Conclusion: We have designed and implemented a visualization dashboard for managing multi-site clinical trial enrollment in two community acquired pneumonia studies. Information dashboards are useful for clinical trial management. They can be used in a standalone trial or can be included into a larger management system