Abstract
There are several established prognostic scoring systems for community-acquired pneumonia (CAP). The Pneumonia Severity Index (PSI) is a prediction rule consisting of 20 variables to identify low-risk patients with CAP. Although PSI had high discrimination ability, it is complex to calculate and difficult to use in busy hospital settings. The CURB-65 score is much simpler to use than is PSI, but it has lower sensitivity for predicting mortality compared with PSI. The A-DROP score is a modified version of the CURB-65 score and provides similar predictive power to that of CURB-65. This study was performed to determine whether a simpler score (CURB-65, A-DROP), expanded with a small number of additional variables, can predict mortality more accurately than PSI. We conducted a retrospective observational study of 1,031 patients with CAP who were hospitalized at a tertiary teaching hospital. We used age, sex, comorbidities, vital signs, and laboratory findings as prognostic variables. We compared the PSI, CURB-65, and A-DROP scores using receiver operating characteristic curve analysis. The areas under the curves (AUCs) of PSI, CURB-65, and A-DROP were 0.735, 0.701, and 0.730, respectively.Multivariable analysis identified malignancy [odds ratio (OR): 2.17, 95% confidence interval (CI): 1.13–4.17], respiration rate ≥ 24/min [OR: 2.18, 95% CI: 1.24–3.82], heart rate ≥ 100/min [OR: 2.92, 95% CI: 1.68–5.08], albumin ≤ 3.09 g/dL [OR: 3.85, 95% CI: 2.09–7.07], lactate > 1.7 mmol/L [OR: 2.59, 95% CI: 1.53–4.38], and N-terminal prohormone brain natriuretic peptide > 500 pg/mL [OR: 2.23, 95% CI: 1.26–3.95] as prognostic factors. Using the prognostic variables identified in the multivariable analysis, we assembled a new scoring system, the expanded A-DROP score. The AUC of this score for the prediction of 28-day mortality was 0.834 (95% CI: 0.794–0.874). Bootstrap validation yielded an estimated AUC of 0.833, indicating negligible overfitting of the model.The expanded A-DROP score is a relatively simple and effective scoring system, and its predictive value was superior to those of other scoring systems.
Similar content being viewed by others
Introduction
Community-acquired pneumonia (CAP) is a significant cause of morbidity and mortality throughout the world. It is the seventh-leading cause of death in the United States, where about 910,000 episodes of CAP occur annually in adults aged >65 years1. In Korea, CAP ranked tenth (7.1 deaths per 100,000 population) among all causes of death in 2004 and fifth (21.4 deaths per 100,000 population) in 2013, which was the highest mortality rate among causes of death due to infection2. In the management of CAP, initial assessment of disease severity is essential because it determines the therapeutic approach, including decisions about the need for hospitalization or intensive care unit admission, the extent of diagnostic testing, and the type of antibiotic treatment1.
Several established severity scores and multiple biomarkers have been used to assess the severity of CAP. The Pneumonia Severity Index (PSI) is a predictive tool used to identify low-risk patients with CAP. Proposed in 1997, the PSI consists of 20 variables, including demographic characteristics, comorbidities, and physical examination, laboratory, and radiographic findings. Although the PSI has high discriminatory value, its calculation is complex, it is difficult to use in busy hospital settings, and it underestimates disease severity in young patients with no comorbidity3.
The CURB-65 score, consisting of confusion, urea >7 mmol/L, respiratory rate ≥30 breaths/min, blood pressure (systolic <90 mmHg or diastolic ≤60 mmHg), and age ≥65 years was proposed in 2003. It is simple to use, but was developed to identify patients with severe CAP at high risk of mortality. Thus, it does not identify patients at low risk of mortality, for whom home treatment might be suitable4.
The A-DROP score, consisting of age ≥70 years in males or ≥75 years in females, blood urea nitrogen ≥21 mg/dL or dehydration, oxyhemoglobin saturation measured by pulse oximetry ≤90% or partial oxygen pressure in arterial blood ≤60 mmHg, confusion, and systolic blood pressure ≤90 mmHg, is a modified version of the CURB-65 score proposed by the Japanese Respiratory Society in 20065. Its predictive power is similar to that of the CURB-65 and PSI6,7.
This study was conducted to identify prognostic factors for 28-day mortality in patients with CAP, and to compare the predictive value of three pneumonia severity scores. Following these analyses, we developed a simpler and more accurate scoring system by expanding the A-DROP score, and evaluated its efficacy compared with that of preexisting scores for severity assessment.
Methods
Study design
We performed a retrospective observational study of 1,031 patients with CAP who were hospitalized at Yeungnam University Hospital (a 930-bed, university-affiliated, tertiary referral hospital in Daegu, South Korea) between March 2012 and February 2014.
During the study period, all consecutive CAP patients admitted to the hospital via the emergency or outpatient department were considered to be eligible for inclusion. Pneumonia was defined as the presence of new radiographic infiltrate and at least two of the following criteria: fever (>38 °C) or hypothermia (≤35 °C), new cough with or without sputum production, pleuritic chest pain, dyspnea, and altered breath sounds on auscultation8. This study included some patients previously defined as having healthcare-associated pneumonia (HCAP), because the 2016 American Thoracic Society and Infectious Diseases Society of America (ATS/IDSA) guidelines removed the concept of HCAP9. Therefore, we considered previously defined HCAP as CAP in this study. This study excluded patients with hospital-acquired pneumonia that developed after >48 h hospitalization, those aged <18 years, immunocompromised patients (such as those with neutropenia after chemotherapy, human immunodeficiency virus infection, solid organ transplant recipients, and those receiving corticosteroids or other immunosuppressive agents), and patients with active Mycobacteria tuberculosis infection. This study only included the first admission in the case of multiple admissions for a same patient. The primary endpoint was 28-day mortality, and several clinical variables were compared between the survival and non-survival groups.
Antibiotic therapy was initiated according to the 2007 ATS/IDSA guidelines1, according to the attending physicians’ decisions and taking into consideration the severity of the disease and patients’ underlying conditions. When a pathogen was identified, antibiotic therapy was modulated according to the susceptibility test results.
As reported previously10, identified pathogens that were not susceptible to β-lactams, macrolides, and fluoroquinolones were defined as multidrug-resistant (MDR) pathogens.
This study was conducted in accordance with the Declaration of Helsinki. It was reviewed and approved by the institutional review board of our hospital (YUMC IRB 2017-11-013), and the requirement for informed consent was waived because of the retrospective design.
Data collection
Data on patients’ age, sex, comorbidities, and vital signs were collected. The severity of pneumonia was assessed in all patients on admission using the PSI3, CURB-65 score [confusion, urea >7 mmol/L, respiratory rate ≥30 breaths/min, blood pressure (systolic <90 mmHg or diastolic ≤60 mmHg), and age ≥65 years]4, and A-DROP score [age ≥70 years for males and ≥75 years for females, blood urea nitrogen (BUN) ≥21 mg/dL or dehydration, oxyhemoglobin saturation measured by pulse oximetry ≤90% or partial pressure of oxygen in arterial blood (PaO2) ≤60 mmHg, confusion, and systolic blood pressure ≤90 mmHg]5.
Laboratory results, including complete blood counts with differentials and C-reactive protein (CRP), procalcitonin, lactate, N-terminal prohormone brain natriuretic peptide (NT-ProBNP), BUN, creatinine, albumin, glucose, sodium, and alkaline phosphatase (ALP) levels, were reviewed. PaO2, partial pressure of carbon dioxide in arterial blood (PaCO2), fraction of inspired oxygen (FiO2) and PaO2/FiO2 values were obtained from the patients’ arterial blood samples. The laboratory findings were analyzed within 24 hours after admission.
Two investigators (JHA and EYC) independently reviewed the baseline data using electronic medical records.
Statistical analysis
Continuous variables are expressed as means ± standard deviation and were compared using Student’s t-test or the Mann–Whitney U test. Categorical variables were compared using the chi-squared test or Fisher’s exact test. Receiver operating characteristic (ROC) curve analyses were performed to assess the effectiveness of pneumonia severity scores for predicting CAP prognosis. When continuous variables were converted to categorical variables, cut-off values were determined using ROC curves. Multivariable logistic regression analyses were performed to identify independent prognostic factors for mortality using variables with p values < 0.05 in univariable analyses, with odds ratios (ORs) and 95% confidence intervals (CIs). In all analyses, two-tailed p values < 0.05 were considered to indicate significance.
We conducted internal validation of the expanded A-DROP model to address the possibility of overfitting causing optimism regarding the model’s performance. Among several internal validation methods, bootstrapping is known to provide more stable estimates with lower bias than other methods11. We used the bootstrap resampling method whereby our derived model was repeatedly fit in 1,000 bootstrap samples. The AUC estimates of these bootstrap models in the original study sample were then computed. We took the mean AUC for these 1,000 bootstrap models to represent the estimate of model performance. All statistical procedures were performed using SPSS software (ver. 21.0; SPSS Inc., Chicago, IL, USA) and R statistical software (version 3.4.3, Vienna, Austria).
Results
Baseline characteristics
During the study period, 1,046 patients with CAP were hospitalized. After application of the exclusion criteria, 1,031 patients were enrolled in the study. Enrolled patients were divided into two groups: 935 (90.7%) survivors and 96 (9.3%) non-survivors.
The demographic and baseline clinical characteristics of the patients with CAP are presented in Table 1. The non-survival group was significantly older than the survival group (mean age: 73.7 ± 11.2 vs. 68.7 ± 14.5 years, p < 0.001). The non-survival group was significantly more likely to have comorbidities, such as congestive heart failure (14.6% vs. 7.1%, p = 0.009), dementia (13.5% vs. 7.2%, p = 0.026) and malignancy (19.8% vs. 10.4%, p = 0.005).
CURB-65, PSI, and A-DROP scores differed significantly between the survival and non-survival groups. Table 2 presents 28-day mortality rates according to these three scores. A significant increase in mortality was observed with higher CURB-65, PSI, and A-DROP scores.
The clinical and laboratory findings are shown in Table 3. Of the vital signs, systolic blood pressure was significantly lower in the non-survival group (114.4 ± 26.8 mmHg vs. 120.5 ± 23.4 mmHg, p = 0.047). Respiration rate and heart rate were significantly higher in the non-survival group (26.6 ± 5.7vs. 22.7 ± 4.6, p < 0.001; and 107.6 ± 22.2 vs. 93.0 ± 19.4, p < 0.001).
With regard to the laboratory findings, arterial pH, PaO2, PaO2/FiO2, lactate, BUN, creatinine, albumin, sodium, hemoglobin, hematocrit, platelet, ALP, procalcitonin, CRP, and NT-ProBNP differed significantly between the two groups. PaCO2, glucose level, and white blood cell count were not significantly different between the two groups.
Prognostic factors for 28-day mortality in patients with CAP
In univariable analysis, age, pleural effusion, comorbidities (including congestive heart failure, dementia, and malignancy), vital signs (including systolic blood pressure, respiratory and heart rates), and laboratory findings (including arterial pH, hematocrit, platelet count, and PaO2, PaO2/FiO2, BUN, creatinine, albumin, hemoglobin, ALP, CRP, lactate, procalcitonin, and NT-ProBNP levels) were significant prognostic factors for mortality in patients with CAP. In multivariable analysis, malignancy (OR: 1.99, 95% CI: 1.04–3.81, p = 0.039), respiratory rate (OR: 1.06, 95% CI: 1.02–1.10, p = 0.008), heart rate (OR: 1.02, 95% CI: 1.01–1.03, p < 0.001), albumin level (OR: 0.27, 95% CI: 0.18–0.41, p < 0.001), platelet count (OR: 0.998, 95% CI 0.996–1.00, p = 0.03), lactate level (OR: 1.28, 95% CI: 1.10–1.49, p = 0.002) and NT-ProBNP level (OR: 1.00, 95% CI: 1.00–1.00, p = 0.001) were significant prognostic factors (Table 4).
When the above-listed significant variables were converted to categorical variables using cut-off values, malignancy (OR: 2.17, 95% CI: 1.13–4.17, p = 0.021), respiratory rate ≥24 breaths/min (OR: 2.18, 95% CI: 1.24–3.82, p = 0.007), heart rate ≥100 beats/min (OR: 2.92, 95% CI: 1.68–5.08, p < 0.001), albumin ≤3.09 g/dL (OR: 3.85, 95% CI: 2.09–7.07, p < 0.001), lactate >1.7 mmol/L (OR: 2.59, 95% CI: 1.53–4.38, p < 0.001) and NT-ProBNP >500 pg/mL (OR: 2.23, 95% CI: 1.26–3.95, p = 0.006) were significant prognostic factors in patients with CAP (Table 5). Table 6 shows the prevalence of these risk factors in different classes of the CURB-65 score, PSI, and A-DROP scores.
ROC curves for mortality prediction with the three preexisting pneumonia severity scoring systems
ROC analysis generated areas under the curve (AUCs) for the prediction of 28-day mortality of 0.735 (95% CI: 0.686–0.784), 0.701 (95% CI: 0.648–0.754), and 0.730 (95% CI: 0.678–0.782) for the PSI, CURB-65, and A-DROP scores, respectively (Fig. 1).
New scoring system developed for assessment of pneumonia severity
We developed four new score models using newly found significant prognostic variables,which expanded CURB-65 and A-DROP scores. As NT-proBNP and albumin are not available on admission in some hospitals, we constructed two models for each score. Model 1 consists of eight parameters, i.e., confusion, urea >7 mmol/L, respiration rate ≥30 breaths/min, blood pressure (systolic <90 mmHg or diastolic ≤60 mmHg), age ≥65 years, malignancy, heart rate ≥100/min, and lactate >1.7 mmol/L. Model 2 consists of 10 parameters, i.e., confusion, urea >7 mmol/L, respiratory rate ≥30 breaths/min, blood pressure (systolic <90 mmHg or diastolic ≤60 mmHg), age ≥65 years, malignancy, heart rate ≥100/min, albumin ≤3.09 g/dL, lactate >1.7 mmol/L, and NT-ProBNP >500 pg/mL. Model 3 consists of eight parameters, i.e., age ≥70 years for males or ≥75 years for females, blood urea nitrogen ≥21 mg/dL or dehydration, oxyhemoglobin saturation measured by pulse oximetry ≤90% or partial pressure of oxygen in arterial blood ≤60 mmHg, confusion, systolic blood pressure ≤90 mmHg, malignancy, heart rate ≥100/min, and lactate >1.7 mmol/L. Model 4 is composed of 10 parameters, i.e., age ≥70 years for males or ≥75 years for females, blood urea nitrogen ≥21 mg/dL or dehydration, oxyhemoglobin saturation measured by pulse oximetry ≤90% or partial pressure of oxygen in arterial blood ≤60 mmHg, confusion, systolic blood pressure ≤90 mmHg, malignancy, heart rate ≥100/min, albumin ≤3.09 g/dL, lactate >1.7 mmol/L, and NT-ProBNP >500 pg/mL. Respiratory rate ≥24/min was not added in the new score because CURB-65 and A-DROP scores already included respiratory parameters.
The predictive value of expanded CURB-65 score for prediction of 28-day mortality was superior (Model 1 AUC = 0.784, 95% CI: 0.740–0.828, Model 2 AUC = 0.821, 95% CI: 0.781–0.861) to PSI (AUC = 0.735, 95% CI: 0.686–0.784). The predictive value of expanded A-DROP score for prediction of 28-day mortality was also superior (Model 3 AUC = 0.805, 95% CI: 0.761–0.848, Model 4 AUC = 0.834, 95% CI: 0.794–0.874) to other scoring systems, such as PSI (AUC = 0.735, 95% CI: 0.686–0.784), CURB-65 score (AUC = 0.701, 95% CI: 0.648–0.754), and A-DROP score (AUC = 0.730, 95% CI: 0.678–0.782). Expanded A-DROP score (model 4) showed the highest predictive value among the four models. Validation of the expanded A-DROP score (model 4) using bootstrap resampling methods yielded an AUC of 0.833.
Discussion
Among 1,031 patients with CAP, the 28-day mortality rate was 9.3% in this study. AUCs from the ROC analysis for the prediction of 28-day mortality were 0.735 (95% CI: 0.686–0.784), 0.701 (95% CI: 0.648–0.754), and 0.730 (95% CI: 0.678–0.782) for the PSI, CURB-65, and A-DROP scores, respectively. We showed that the presence of malignancy as a comorbidity, tachypnea, tachycardia, low albumin level, high lactate level, and high NT-ProBNP level were independent predictors of poor prognosis in patients with CAP. In addition, we proposed a new pneumonia severity score using newly identified prognostic variables. The expanded A-DROP score, based on one comorbidity (malignancy), tachycardia, and three laboratory findings (albumin, lactate, and NT-ProBNP levels), predicted mortality with a larger AUC (0.834) than that for the PSI. To our knowledge, this is the largest study to evaluate the usefulness of pneumonia severity score systems for the prediction of mortality in South Korean populations.
The mortality rate in our study was higher than in previous studies performed in the USA and Europe12. The number of patients with PSI risk class IV–V was higher in our study compared to those performed in the USA and Europe, which can explain the higher mortality in our cohort.
The ROC scores for CURB-65 and A-DROP in this study were lower than in some previous studies7,13. However, many studies showed lower ROC scores for CURB-65 and A-DROP than in our study14,15,16,17. The reasons for this difference are not clear. Further multicenter studies in larger numbers of patients are needed to improve the efficacy of these scores.
Five important factors improved the performance of the A-DROP score: the presence of malignancy, tachycardia, hypoalbuminemia, increased blood lactate level, and increased NT-ProBNP level. Malignancy is a well-established prognostic factor for CAP, worth 30 points in the PSI3. Ito et al.18 reported that the presence of malignancy was a prognostic factor for hospitalized patients with CAP aged >15 years. Similarly, we found that malignancy was a poor prognostic factor for hospitalized patients with CAP aged >18 years.
Tachycardia is a known prognostic factor for CAP. It is included in the PSI (heart rate ≥125 beats/min, 10 points)3 and in the Acute Physiology and Chronic Health Evaluation II score19. Several studies have shown that tachycardia is a prognostic factor for CAP20,21. However, as heart rate cut-off values have varied among studies, further studies are needed to confirm the usefulness of this factor.
Among laboratory parameters, we found that the albumin, lactate, and NT-ProBNP levels were significant prognostic factors for CAP. Albumin is synthesized in the liver using the amino acids in hepatocytes. Thus, decreased liver function and malnutrition may result in hypoalbuminemia. An imbalance between intravascular and extravascular albumin levels may also result in hypoalbuminemia. Kim et al.22 reported that patients aged >18 years with debilitating conditions and aspiration pneumonia were assigned more frequently to the hypoalbuminemia group than to the non-hypoalbuminemia group. Previous studies have shown that a low initial serum albumin concentration is an independent risk factor for mortality in patients with CAP13,18,22. In our study, hypoalbuminemia had the highest OR for mortality prediction. Malnutrition or underlying disease might influence the mortality of CAP.
The lactate level has been used widely in critically ill patients to assess perfusion status, organ dysfunction, treatment response, and prognosis23. In patients with pneumonia, hyperlactatemia is associated with mortality, hospitalization, and intensive care unit admission. The addition of the lactate level significantly improved the prognostic value of the CURB-65 score for CAP mortality prediction in previous studies15,24. NT-ProBNP is used in the assessment of cardiac dysfunction. It is secreted in response to excessive stretching of cardiomyocytes and regulates natriuresis, body fluid volume, vascular pressure, and electrolyte balance25,26. Some studies have suggested that increased BNP levels in patients with CAP are related to the inflammatory response and local hypoxia in the pulmonary circulatory system27. NT-ProBNP was a strong predictor of mortality in hospitalized patients with CAP, with a performance in predicting mortality comparable to that of the PSI and CURB-65 scores28,29.
The expanded A-DROP score, which employs 10 significant risk factors to predict CAP severity, has greater predictive value than do preexisting severity scores. As NT-proBNP and albumin are not available on admission in some hospitals, we constructed two models. Not only model 4 (10 parameters) but also model 3 (8 parameters) showed higher predictive value compared to pre-existing severity scores. This finding has several significant implications. First, the score decreased the relative effects of age and comorbidities and removed the need for use radiographic findings in the calculation, as compared to PSI. PSI tends to weight age and comorbidities heavily. The expanded A-DROP score significantly improved prediction of high-risk patients by alleviating the weight of age and comorbidities in the calculation. In addition, the score includes several significant biomarkers that are not included in other severity scores. After addressing weaknesses in the preexisting score, the expanded A-DROP score showed greatly improved predictive value (AUC = 0.834). We found similar results (AUC = 0.833) with bootstrap validation. Second, the score can be used in real practice; it is a simpler (10-variable) alternative to the PSI (20-variable) for the prediction of short-term mortality in patients with CAP. The results for the expanded A-DROP score were superior to those for the PSI. In this study, patients with scores of 0, 1, and 2 were considered to be at low risk (1.5%) of mortality, with the possibility of management on a hospital outpatient basis. Patients with scores of 3 and 4 were regarded to be at intermediate risk (11.4%) of mortality, such that hospitalization should be considered. Patients with scores ≥5 were considered to be at high risk (30.1%) of mortality, and initial care in an intensive care unit should be considered (Fig. 2). However, we emphasize that the applicability of the expanded A-DROP score can only be determined by external validation in different cohorts of CAP patients. We hope that other groups will validate the accuracy of this score for predicting mortality in other patient populations.
This study had several limitations. First, as no external validation was conducted in this study, the applicability of the expanded A-DROP score in clinical practice requires further study. Second, it was retrospective and conducted at a single center in South Korea, which had only hospitalized patients. Thus, this new scoring system cannot be applied to outpatients. Third, medical records regarding restriction of treatment escalation, such as do-not-resuscitate orders, were insufficient. Thus, the influence of such restriction on mortality was not considered. Fourth, selection bias could not be avoided as we did not use population-based data and the severity of patients’ conditions may differ among tertiary care hospitals in the same area.
In conclusion, five clinical factors – the presence of malignancy, tachycardia, hypoalbuminemia, increased blood lactate level, and increased NT-ProBNP level – are independent predictors of 28-day mortality in patients with CAP. By adding these predictors to the original A-DROP score, we developed a simpler and more accurate scoring system for the prediction of CAP severity in hospitalized patients.
References
Mandell, L. A. et al. Infectious Diseases Society of America/American Thoracic Society consensus guidelines on the management of community-acquired pneumonia in adults. Clinical Infectious Diseases: an Official Publication of the Infectious Diseases Society of America 44(Suppl 2), S27–72, https://doi.org/10.1086/511159 (2007).
Shin, H. Y. et al. Cause-of-death statistics in the Republic of Korea, 2014. J Korean Med Assoc 59, 221–232 (2016).
Fine, M. J. et al. A prediction rule to identify low-risk patients with community-acquired pneumonia. The New England Journal of Medicine 336, 243–250, https://doi.org/10.1056/NEJM199701233360402 (1997).
Lim, W. S. et al. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax 58, 377–382 (2003).
Miyashita, N., Matsushima, T., Oka, M. & Japanese Respiratory, S. The JRS guidelines for the management of community-acquired pneumonia in adults: an update and new recommendations. Intern Med 45, 419–428 (2006).
Usui, K., Tanaka, Y., Noda, H. & Ishihara, T. [Comparison of three prediction rules for prognosis in community acquired pneumonia: Pneumonia Severity Index (PSI), CURB-65, and A-DROP]. Nihon Kokyuki Gakkai zasshi = The Journal of the Japanese Respiratory Society 47, 781–785 (2009).
Shindo, Y. et al. Comparison of severity scoring systems A-DROP and CURB-65 for community-acquired pneumonia. Respirology 13, 731–735, https://doi.org/10.1111/j.1440-1843.2008.01329.x (2008).
Carratala, J. et al. Outpatient care compared with hospitalization for community-acquired pneumonia: a randomized trial in low-risk patients. Annals of Internal Medicine 142, 165–172 (2005).
Kalil, A. C. et al. Management of Adults With Hospital-acquired and Ventilator-associated Pneumonia: 2016 Clinical Practice Guidelines by the Infectious Diseases Society of America and the American Thoracic Society. Clinical Infectious Diseases: an Official Publication of the Infectious Diseases Society of America 63, e61–e111, https://doi.org/10.1093/cid/ciw353 (2016).
Ahn, J. H. et al. Clinical characteristics and prognostic risk factors of healthcare-associated pneumonia in a Korean tertiary teaching hospital. Medicine 96, e8243, https://doi.org/10.1097/MD.0000000000008243 (2017).
Steyerberg, E. W. et al. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. Journal of Clinical Epidemiology 54, 774–781 (2001).
Arnold, F. W., Wiemken, T. L., Peyrani, P., Ramirez, J. A. & Brock, G. N. Mortality differences among hospitalized patients with community-acquired pneumonia in three world regions: results from the Community-Acquired Pneumonia Organization (CAPO) International Cohort Study. Respiratory Medicine 107, 1101–1111, https://doi.org/10.1016/j.rmed.2013.04.003 (2013).
Liu, J. L. et al. Expanded CURB-65: a new score system predicts severity of community-acquired pneumonia with superior efficiency. Scientific Reports 6, 22911, https://doi.org/10.1038/srep22911 (2016).
Jo, S. et al. Validation of modified early warning score using serum lactate level in community-acquired pneumonia patients. The National Early Warning Score-Lactate score. The American Journal of Emergency Medicine 34, 536–541, https://doi.org/10.1016/j.ajem.2015.12.067 (2016).
Chen, Y. X. & Li, C. S. Lactate on emergency department arrival as a predictor of mortality and site-of-care in pneumonia patients: a cohort study. Thorax 70, 404–410, https://doi.org/10.1136/thoraxjnl-2014-206461 (2015).
Pflug, M. A. et al. Short-term mortality of adult inpatients with community-acquired pneumonia: external validation of a modified CURB-65 score. Postgraduate Medical Journal 91, 77–82, https://doi.org/10.1136/postgradmedj-2014-132802 (2015).
Nullmann, H. et al. External validation of the CURSI criteria (confusion, urea, respiratory rate and shock index) in adults hospitalised for community-acquired pneumonia. BMC Infectious Diseases 14, 39, https://doi.org/10.1186/1471-2334-14-39 (2014).
Ito, A. et al. Prognostic factors in hospitalized community-acquired pneumonia: a retrospective study of a prospective observational cohort. BMC Pulmonary Medicine 17, 78, https://doi.org/10.1186/s12890-017-0424-4 (2017).
Knaus, W. A., Draper, E. A., Wagner, D. P. & Zimmerman, J. E. APACHE II: a severity of disease classification system. Critical Care Medicine 13, 818–829 (1985).
Braun, E., Kheir, J., Mashiach, T., Naffaa, M. & Azzam, Z. S. Is elevated red cell distribution width a prognostic predictor in adult patients with community acquired pneumonia? BMC Infectious Diseases 14, 129, https://doi.org/10.1186/1471-2334-14-129 (2014).
Kolditz, M. et al. Community-acquired pneumonia as medical emergency: predictors of early deterioration. Thorax 70, 551–558, https://doi.org/10.1136/thoraxjnl-2014-206744 (2015).
Kim, H. et al. Diagnostic performance of initial serum albumin level for predicting in-hospital mortality among aspiration pneumonia patients. The American Journal of Emergency Medicine, https://doi.org/10.1016/j.ajem.2017.06.039 (2017).
Singer, M. et al. The Third International Consensus Definitions for Sepsis and SepticShock (Sepsis-3). Jama 315, 801–810, https://doi.org/10.1001/jama.2016.0287 (2016).
Frenzen, F. S. et al. Admission lactate predicts poor prognosis independently of the CRB/CURB-65 scores in community-acquired pneumonia. Clinical Microbiology and Infection: the Official Publication of the European Society of Clinical Microbiology and Infectious Diseases, https://doi.org/10.1016/j.cmi.2017.07.007 (2017).
Hall, C. Essential biochemistry and physiology of (NT-pro)BNP. European Journal of Heart Failure 6, 257–260, https://doi.org/10.1016/j.ejheart.2003.12.015 (2004).
Brueckmann, M. et al. Prognostic value of plasma N-terminal pro-brain natriuretic peptide in patients with severe sepsis. Circulation 112, 527–534, https://doi.org/10.1161/CIRCULATIONAHA.104.472050 (2005).
Li, J., Ye, H. & Zhao, L. B-type natriuretic peptide in predicting the severity of community-acquired pneumonia. World Journal of Emergency Medicine 6, 131–136, https://doi.org/10.5847/wjem.j.1920-8642.2015.02.008 (2015).
Jeong, K. Y. et al. Prognostic value of N-terminal pro-brain natriuretic peptide in hospitalised patients with community-acquired pneumonia. Emergency Medicine Journal: EMJ 28, 122–127, https://doi.org/10.1136/emj.2009.089383 (2011).
Chang, C. L. et al. Biomarkers of cardiac dysfunction and mortality from community-acquired pneumonia in adults. PloS one 8, e62612, https://doi.org/10.1371/journal.pone.0062612 (2013).
Acknowledgements
This article was supported by Yeungnam University Research fund in 2017.
Author information
Authors and Affiliations
Contributions
J.H.A. and E.Y.C. conceived and designed the study. J.H.A. and E.Y.C. collected samples. J.H.A. analyzed the data. J.H.A. and E.Y.C. wrote and edited the paper.
Corresponding author
Ethics declarations
Competing Interests
The authors declare no competing interests.
Additional information
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Ahn, J.H., Choi, E.Y. Expanded A-DROP Score: A New Scoring System for the Prediction of Mortality in Hospitalized Patients with Community-acquired Pneumonia. Sci Rep 8, 14588 (2018). https://doi.org/10.1038/s41598-018-32750-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-018-32750-2
Keywords
This article is cited by
-
The prognostic value of rapid risk scores among patients with community-acquired pneumonia
Wiener klinische Wochenschrift (2023)
-
Machine learning-derived prediction of in-hospital mortality in patients with severe acute respiratory infection: analysis of claims data from the German-wide Helios hospital network
Respiratory Research (2022)
-
Mortality Predictive Scores for Community-Acquired Pneumonia in Children
Current Infectious Disease Reports (2019)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.