Performance of Risk Scores in Predicting Mortality at 3, 6, and 12 Months in Patients Diagnosed with Community-Acquired Pneumonia

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Bastidas, Gabriela Guerrón-Gómez, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3951887/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jul, 2024 Read the published version in BMC Pulmonary Medicine → Version 1 posted 10 You are reading this latest preprint version Abstract Background Risk scores play a crucial role in assessing mortality risk among patients diagnosed with community-acquired pneumonia (CAP). Despite their practicality, there remains a dearth of comparative evidence regarding various risk scoring systems. Methods This multicenter retrospective study was conducted across two high-complexity medical institutions, focusing on individuals diagnosed with CAP. Receiver Operating Characteristic (ROC) curves were generated to assess the predictive performance of each analyzed risk score questionnaire in predicting survival or death at 3, 6, and 12 months post-diagnosis. Results Out of a total of 7454 potentially eligible patients, 3688 were included in the final analysis. Survival at 3, 6, and 12 months was 94.8%, 91.7% and 83.7%, respectively. At 3 months, PSI, CHARLSON, and CRB-65 scores showed ROC curves of 0.74 (95% CI: 0.71–0.77), 0.71 (95% CI: 0.67–0.74), and 0.7 (95% CI: 0.66–0.74). At 6 months, PSI and CHARLSON scores showed performances of 0.74 (95% CI: 0.72–0.77) and 0.72 (95% CI: 0.69–0.74), respectively. At 12 months, all evaluated scores showed poor discriminatory capacity, including PSI, which decreased its capacity to poor with an ROC curve of 0.64 (95% CI: 0.61–0.66). Conclusion In predicting mortality in patients with CAP, it was observed that at 3 months, PSI, CHARLSON, and CRB-65 showed acceptable predictive performances. At 6 months, only PSI and CHARLSON maintained acceptable levels of accuracy. For the 12-month period, all evaluated scores exhibited very limited discriminatory ability, ranging from poor to almost negligible. Pneumonia Risk score Mortality Observational study Figures Figure 1 Introduction Community-acquired pneumonia (CAP) is the leading infectious cause of death worldwide, accounting for 6.1% of all fatal outcomes ( 1 , 2 ). Despite advances in CAP prevention and diagnosis, the incidence is estimated to be 106 to 164 per 10,000 inhabitants in the United States and up to 294 cases per 10,000 inhabitants in Latin America, especially in individuals over 65 years old, those with comorbidities, or immunosuppressed individuals ( 3 ). Mortality from CAP ranges from 5–25% in in-hospital settings and can reach up to 50% in intensive care units (ICUs) ( 1 – 3 ). Therefore, in order to reduce the disease burden and costs associated with CAP, scales have been validated to stratify pneumonia severity, predict prognosis, and anticipate the need for ICU, providing an advantage in guiding early therapeutic approaches and positively impacting short and long-term clinical outcomes ( 1 , 3 , 4 ). Currently, the use of risk scores is recommended by clinical practice guidelines and consensus from the American Thoracic Society/Infectious Diseases Society of America (ATS/IDSA) to assess the prognosis and the most suitable treatment location for CAP. The most recommended ones are the Pneumonia Severity Index (PSI) and CURB-65 ( 5 ). However, the clinical applicability of these scores is primarily for estimating mortality within the first 30 days, and there is still insufficient evidence demonstrating their long-term predictive capacity ( 5 , 6 ). Therefore, efforts have been made to describe the long-term predictive capacity of clinical variables used in the construction of widely studied risk scores for CAP, including azotemia, alteration of mental status, and evidence of pleural effusion in chest X-rays, all of which have evidence associating them with long-term mortality ( 7 – 9 ). Shah et al. ( 7 ) described a strong association between acute kidney injury and unfavorable clinical outcomes, such as an increased risk of progression to chronic kidney disease, congestive heart failure, acute myocardial infarction, stroke, and death at 3 and 6 months. Currently, the use of risk scores is limited to evaluating short-term clinical outcomes such as mortality and the need for mechanical ventilation, and the guidance from clinical practice guidelines and consensus on the ideal tool for long-term outcomes is almost nonexistent or generates ambiguity when choosing to estimate mortality risk. The use of risk scores, addressing intrinsic patient characteristics, clinical signs, symptoms, and laboratory tests, could help identify individuals at high risk of long-term mortality from lower respiratory tract infection ( 6 , 8 ). However, available evidence is limited regarding the use of clinical variables and risk scores in patients diagnosed with CAP ( 6 – 9 ). Therefore, the aim of this article is to analyze and compare the performance of 16 clinical questionnaires in predicting mortality at three, six, and twelve months in adult patients with CAP. Methods Analytical observational study conducted on patients diagnosed with Community-Acquired Pneumonia (CAP) in high-complexity institutions in Colombia. Patients were assessed and admitted to emergency services and Intensive Care Units (ICUs) from January 2010 to January 2020. Eligibility Criteria Men and women aged ≥ 18 years were included, evaluated for at least 6 hours in emergency services or ICUs due to CAP diagnosis. CAP diagnosis was established based on ATS/IDSA criteria, meaning that patients had to present signs and symptoms of lower respiratory tract infection (cough, dyspnea, purulent sputum, crackles, pectoriloquy, etc.), systemic involvement (fever, hypotension, altered consciousness, night sweats, leukocytosis, among others), radiographic findings consistent with pneumonia, and the absence of another disease explaining the clinical picture. Additionally, medical records needed to include sufficient information for the evaluation of CURB-65, CRB-65, SCAP, CORB, ADROP, NEWS, Pneumonia Shock, REA-ICU, PSI, SMART-COP, SMRT-CO, SOAR, qSOFA, SRIS, CAPSI, and Charlson comorbidity index scores (Supplementary File 1). Variables Sociodemographic variables (age and gender), comorbidities through the Charlson index, vital signs, consciousness status, chest X-ray findings such as multilobar involvement or pleural effusion, and laboratory tests including arterial gases, hematocrit, white blood cell count, blood urea nitrogen, serum sodium, albumin, and blood glucose were included. Additionally, the need for ICU, invasive mechanical ventilation (IMV), and/or vasopressor support were considered. The dependent variable was mortality evaluated at 3, 6, and 12 months following CAP diagnosis. To minimize possible errors in outcome classification, the research team collecting data from clinical records had medical expertise in diagnosing the studied pathology. To reduce typing bias, information was reviewed by at least two team members. Sample Size Sample size calculation used data from Lim et al. ( 10 ), describing a sensitivity of 75% and specificity of 69% for CURB-65, and Fine et al. ( 11 ), reporting a sensitivity of 100% and specificity of 52.2% for PSI. Using the formula for paired diagnostic tests, with an expected mortality of 6.1%, 90% power, and statistical significance of 0.05, a minimum of 625 subjects was required. Missing data An imputation analysis addressed missing data, employing weighted mean imputation for quantitative variables and logistic regression for qualitative variables with a loss of less than 10% ( 13 ). Variables with more than 10% data loss were excluded. A comparison between non-imputed and imputed results ensured that imputation did not introduce bias or significantly alter the original data. Statistical Analysis Data were entered into REDCap (Research Electronic Data Capture) ( 12 ) for subsequent analysis using SPSS 25 software (IBM Corp. IBM SPSS Statistics for Windows, Version 25.0 licensed). Qualitative variables were reported in frequencies and percentages, while quantitative variables were summarized using mean and standard deviation for normally distributed ones and median and interquartile range for non-normally distributed ones. Bivariate analysis between questionnaires and the outcome (alive or dead) was performed using the chi-square test for qualitative variables and Student's t-test or Mann-Whitney U test for quantitative variables ( 13 ). Scores obtained for each questionnaire were used to calculate the area under the ROC curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), and negative likelihood ratio (LR-), using the established cutoff point for each questionnaire (Supplementary File 2). ROC curves of different scores were compared with the DeLong test, considering a value of p 0.7 to 0.8, acceptable discrimination ability; > 0.8 to 0.9, excellent discriminatory capacity; and > 0.9, outstanding discriminatory capacity ( 13 ). Results Out of a total of 7454 potentially eligible patients, 3688 were included in the final analysis (Fig. 1 ). Survival at 3, 6, and 12 months was 94.8% (3498/3688), 91.7% (3381/3688) and 83.7% (3086/3688), respectively. Population Characteristics The average age was 63.5 years (SD: 21.39), and 59.3% (2188/3688) of the patients were male. The most common symptoms in the overall population were cough in 82.6% (3045/3688), dyspnea in 67.4% (2486/3688), and fever in 47.6% (1756/3688) (Table 1 ). The most prevalent comorbidities were arterial hypertension in 46.1% (1699/3688), COPD in 25.5% (941/3688), and smoking in 18.1% (668/3688). Table 1 General characteristics of the population. Total population n = 3688 Alive n = 3086 Deaths n = 602 p value Age in years, mean (SD) 63.5 (21.39) 62 (21.78) 70.7 (17.53) < 0.001 Males, n (%) 2188 (59.3) 1801 (58.4) 387 (64.3) 0.007 Cough, n (%) 3045 (82.6) 2586 (83.8) 459 (76.2) < 0.001 Dyspnoea, n (%) 2486 (67.4) 2095 (67.9) 391 (65) 0.160 Fever, n (%) 1756 (47.6) 1500 (48.6) 256 (42.5) 0.006 pleuritic pain, n (%) 954 (25.9) 843 (27.3) 111 (18.4) < 0.001 Alteration of consciousness, n (%) 158 (8.2) 118 (7.1) 40 (15.7) < 0.001 Wheezing, n (%) 829 (22.5) 719 (23.3) 110 (18.3) 0.007 FiO2%, mean (SD) 28.5 (12.34) 28.1 (11.59) 30.3 (14.98) < 0.001 Arterial hypertension, n (%) 1699 (46.1) 1386 (44.9) 313 (52.1) 0.001 Chronic heart failure, n (%) 447 (12.1) 357 (11.6) 90 (15) 0.020 Acute myocardial infarction, n (%) 168 (4.6) 136 (4.4) 32 (5.3) < 0.001 Cerebrovascular disease, n (%) 484 (7) 251 (6.3) 233 (10.8) < 0.001 COPD, n (%) 941 (25.5) 770 (25) 171 (28.5) 0.073 Mellitus diabetes, n (%) 423 (11.5) 342 (11.1) 81 (13.5) 0.093 Chronic kidney disease, n (%) 201 (5.5) 149 (4.8) 52 (8.7) < 0.001 Cancer, n (%) 237 (6.4) 168 (5.4) 69 (11.5) < 0.001 Asthma, n (%) 79 (2.1) 74 (2.4) 5 (0.8) 0.015 Immunosuppression, n (%) 148 (4) 115 (3.7) 33 (5.5) 0.043 Notes: SD: Standard deviation; n: number; FiO2: Fraction of inspired oxygen; COPD: Chronic obstructive pulmonary disease. Arterial Gases and Blood Tests The inspired fraction of oxygen in survivors was 28.1% (SD: 11.59) compared to 30.3% (SD: 14.98) in non-survivors (p < 0.001). Blood urea nitrogen was 4.7 mg/dl lower in survivors compared to the deceased group (22.5 vs. 27.2; p < 0.001). Laboratory test results are described in Supplementary table 3. Treatment During Hospitalization 12.1% (73/602) of deceased patients had septic shock compared to 6.4% (198/3086) of surviving patients (p < 0.001) (Supplementary table 4). The use of vasopressor support and systemic corticosteroids was 12.3% (78/602) and 31.6% (190/602) in deceased patients, respectively. The need for ICU was 7.6% higher in deceased patients compared to the survivor group (17.6 vs. 10) (p < 0.001). Performance of Risk Scores for Mortality at 3, 6, and 12 Months At 3 months, PSI, CHARLSON, and CRB-65 scores showed ROC curves of 0.74 (95% CI: 0.71–0.77), 0.71 (95% CI: 0.67–0.74), and 0.7 (95% CI: 0.66–0.74), (Table 2 ). At 6 months, PSI and CHARLSON scores showed performances of 0.74 (95% CI: 0.72–0.77) and 0.72 (95% CI: 0.69–0.74), respectively (Table 3 ). At 12 months, all evaluated scores showed poor discriminatory capacity, including PSI, which decreased its capacity to poor with an ROC curve of 0.64 (95% CI: 0.61–0.66) (Table 4 ). The score with the lowest performance in predicting mortality at 3, 6, and 12 months was SIRS with an ROC curve of 0.51 (95% CI: 0.47–0.55), 0.5 (95% CI: 0.47–0.54), and 0.5 (95% CI: 0.47–0.52), respectively. Table 2 Performance of Risk Scores in Community Acquired Pneumonia for 3 months Mortality Prediction S (CI 95%) E (CI 95%) VPP (CI 95%) VPN (CI 95%) LR+ (CI 95%) LR- (CI 95%) AUCOR (CI 95%) p value Mortality 3 months CURB-65 ≥ 2 71.9 (70.2–73.7) 52.9 (50.9–54.8) 8.7 (7.6–9.8) 96.8 (96.1–97.5) 1.53 (1.283–1.816) 0.53 (0.446–0.632) 0.69 (0.64–0.73) < 0.001 CRB-65 ≥ 2 49.4 (47.7–51.1) 80.1 (78.7–81.4) 12.3 (11.1–13.4) 96.6 (95.9–97.2) 2.48 (1.836–3.344) 0.63 (0.468–0.853) 0.7 (0.66–0.74) < 0.001 SCAP ≥ 20 28.6 (26.4–30.7) 23.1 (21.2–25.1) 10.2 (8.8–11.6) 96.2 (95.3–97.1) 1.12 (1.027–1.211) 0.62 (0.568–0.704) 0.66 (0.6–0.72) < 0.001 CORB ≥ 2 36.3 (34.6–37.9) 81.3 (79.9–82.6) 10.1 (9-11.1) 95.7 (94.9–96.4) 1.93 (1.411–2.653) 0.78 (0.572–1.076) 0.61 (0.56–0.66) < 0.001 ADROP ≥ 3 44.7 (42.7–46.6) 75.5 (73.8–77.2) 10.5 (9.3–11.7) 95.5 (94.7–96.3) 1.83 (1.373–2.427) 0.73 (0.551–0.974) 0.67 (0.63–0.72) < 0.001 NEWS ≥ 7 48.2 (46.2–50.1) 64.9 (63-66.8) 5.3 (4.4–6.1) 96.9 (96.2–97.5) 1.37 (1.089–1.73) 0.8 (0.634–1.007) 0.61 (0.56–0.66) < 0.001 PNEUMONIA SHOCK ≥ 3 70.2 (68.2–72.3) 59.7 (57.5–61.9) 11.2 (9.8–12.6) 96.5 (95.7–97.3) 1.74 (1.412–2.151) 0.5 (0.404–0.616) 0.69 (0.65–0.74) < 0.001 REA ICU ≥ 7 41.3 (38.8–43.8) 78.1 (76-80.3) 11.3 (9.7–13) 95.2 (94.1–96.3) 1.89 (1.278–2.796) 0.75 (0.508–1.111) 0.65 (0.59–0.7) < 0.001 PSI ≥ 91 75.3 (72.5–78) 59.7 (56.6–62.8) 9.2 (7.4–11) 97.8 (96.9–98.7) 1.87 (1.374–2.543) 0.41 (0.304–0.563) 0.74 (0.71–0.77) < 0.001 SMART-COP ≥ 3 60 (58.4–61.6) 52.3 (50.7–53.9) 6.4 (5.6–7.2) 96 (95.4–96.6) 1.26 (1.082–1.464) 0.76 (0.657–0.89) 0.53 (0.39–0.68) 0.679 SMRT-CO ≥ 3 55.9 (54.2–57.6) 66 (64.4–67.6) 8.6 (7.7–9.6) 96.3 (95.7–97) 1.64 (1.336–2.024) 0.67 (0.543–0.822) 0.62 (0.57–0.66) < 0.001 SOAR ≥ 2 64.3 (62.2–66.5) 54.2 (51.9–56.4) 8.1 (6.9–9.3) 96 (95.2–96.9) 1.4 (1.147–1.717) 0.66 (0.538–0.805) 0.62 (0.57–0.67) < 0.001 qSOFA ≥ 2 22.1 (20.8–23.4) 90.1 (89.1–91) 10.8 (9.8–11.8) 95.5 (94.8–96.2) 2.23 (1.445–3.436) 0.86 (0.561–1.333) 0.61 (0.57–0.65) < 0.001 SRIS ≥ 2 57.4 (55.8–59) 44.1 (42.5–45.7) 5.3 (4.6-6) 95 (94.3–95.7) 1.03 (0.901–1.168) 0.97 (0.85–1.101) 0.51 (0.47–0.55) 0.754 CAPSI ≥ 4 57.8 (55.9–59.7) 63.9 (62-65.8) 8.9 (7.8–10) 96.1 (95.4–96.9) 1.6 (1.289–1.992) 1.52 (1.219–1.884) 0.68 (0.63–0.73) < 0.001 CHARLSON ≥ 3 84.7 (83.6–85.9) 41.7 (40.1–43.3) 7.3 (6.5–8.2) 98.1 (97.6–98.5) 1.45 (1.288–1.641) 0.37 (0.324–0.413) 0.71 (0.67–0.74) < 0.001 Notes: SE: sensitivity; SP: specificity; PPV: positive predictive value; NPV: negative predictive value; LR: like hood ratio; ROC-curve: Area under the receiver operating characteristic curve. Table 3 Performance of Risk Scores in Community Acquired Pneumonia for 6 months Mortality Prediction S (CI 95%) E (CI 95%) VPP (CI 95%) VPN (CI 95%) LR+ (CI 95%) LR- (CI 95%) AUCOR (CI 95%) p value Mortality 6 months CURB-65 ≥ 2 72.1 (70.4–73.9) 53.8 (51.8–55.7) 13.5 (12.2–14.8) 95.1 (94.2–95.9) 1.56 (1.353-1.8) 0.52 (0.449–0.598) 0.69 (0.66–0.73) < 0.001 CRB-65 ≥ 2 47 (45.3–48.7) 80.9 (79.5–82.2) 18.8 (17.5–20.2) 94.2 (93.4–95) 2.46 (1.926–3.134) 0.66 (0.514–0.836) 0.69 (0.66–0.72) < 0.001 SCAP ≥ 20 28.8 (26.6–30.9) 88.4 (86.9–89.9) 20.2 (18.3–22.1) 92.4 (91.2–93.7) 2.49 (1.609–3.843) 0.81 (0.521–1.245) 0.68 (0.63–0.72) < 0.001 CORB ≥ 2 34.2 (32.5–35.8) 81.7 (80.3–83.1) 15.3 (14-16.5) 92.8 (91.9–93.7) 1.87 (1.447–2.41) 0.81 (0.624–1.039) 0.59 (0.56–0.63) < 0.001 ADROP ≥ 3 47.2 (45.2–49.1) 76.5 (74.9–78.2) 17.1 (15.6–18.6) 93.4 (92.4–94.4) 2.01 (1.586–2.547) 0.69 (0.545–0.875) 0.68 (0.65–0.71) < 0.001 NEWS ≥ 7 46.5 (44.6–48.5) 65.2 (63.3–67) 8.3 (7.2–9.4) 94.7 (93.8–95.6) 1.34 (1.107–1.612) 0.82 (0.68–0.99) 0.6 (0.56–0.64) < 0.001 PNEUMONIA SHOCK ≥ 3 69.3 (67.2–71.3) 60.8 (58.7–63) 17.2 (15.6–18.9) 94.4 (93.4–95.4) 1.77 (1.487–2.105) 0.51 (0.425–0.601) 0.69 (0.65–0.73) < 0.001 REA ICU ≥ 7 38.2 (35.7–40.7) 82.6 (80.9–84.3) 20.9 (19.1–22.7) 92 (90.8–93.2) 2.24 (1.655–3.041) 0.74 (0.545-1.00) 0.62 (0.58–0.67) < 0.001 PSI ≥ 91 75.2 (78–78) 60.9 (57.9–64) 14.9 (12.6–17.1) 8.8 (7-10.5) 1.93 (1.492–2.487) 0.41 (0.315–0.524) 0.74 (0.72–0.77) < 0.001 SMART-COP ≥ 3 62.5 (61-64.1) 53 (51.4–54.6) 10.8 (9.8–11.8) 94 (93.2–94.7) 1.33 (1.178–1.502) 0.71 (0.626–0.799) 0.56 (0.42–0.7) 0.402 SMRT-CO ≥ 3 51.8 (50-53.5) 66.4 (64.8–68) 12.8 (11.6–13.9) 93.5 (92.7–94.4) 1.54 (1.303–1.819) 0.73 (0.615–0.859) 0.59 (0.56–0.63) < 0.001 SOAR ≥ 2 66.7 (64.6-148700) 55 (52.8–57.2) 12.9 (11.4–14.4) 94.3 (93.3–95.3) 1.48 (1.255–1.751) 0.61 (0.513–0.715) 0.64 (0.6–0.68) < 0.001 qSOFA ≥ 2 21.5 (20.2–22.8) 90.4 (89.5–91.4) 17 (15.8–18.2) 92.7 (91.9–93.5) 2.25 (1.586–3.194) 0.87 (0.612–1.232) 0.6 (0.57–0.64) < 0.001 SRIS ≥ 2 57.7 (56.1–59.2) 44.2 (42.6–45.8) 8.6 (7.7–9.5) 92 (91.1–92.9) 1.03 (0.931–1.145) 0.96 (0.865–1.064) 0.5 (0.47–0.54) 0.822 CAPSI ≥ 4 56.5 (54.6–58.4) 64.6 (62.7–66.4) 13.6 (12.3–15) 93.7 (92.8–94.7) 1.59 (1.335–1.905) 1.48 (1.243–1.774) 0.67 (0.63–0.7) < 0.001 CHARLSON ≥ 3 87.9 (86.9–89) 42.9 (41.3–44.5) 11.4 (10.4–12.4) 97.7 (97.2–98.2) 1.54 (1.397–1.699) 0.28 (0.255–0.31) 0.72 (0.69–0.74) < 0.001 Notes: SE: sensitivity; SP: specificity; PPV: positive predictive value; NPV: negative predictive value; LR: like hood ratio; ROC-curve: Area under the receiver operating characteristic curve. Table 4 Performance of Risk Scores in Community Acquired Pneumonia for 12 months Mortality Prediction S (CI 95%) E (CI 95%) VPP (CI 95%) VPN (CI 95%) LR+ (CI 95%) LR- (CI 95%) AUCOR (CI 95%) p value Mortality 12 months CURB-65 ≥ 2 63.8 (61.9–65.7) 54.4 (54.4–56.4) 21.6 (20-23.2) 88.4 (87.2–89.7) 1.4 (1.254–1.564) 0.66 (0.595–0.743) 0.63 (0.6–0.66) < 0.001 CRB-65 ≥ 2 38 (36.3–39.6) 81.5 (81.5–82.9) 27.7 (26.1–29.2) 87.6 (86.5–88.7) 2.06 (1.703–2.482) 0.76 (0.63–0.919) 0.62 (0.59–0.65) < 0.001 SCAP ≥ 20 22.3 (20.3–24.3) 88.9 (88.9–90.4) 30.7 (28.5–32.9) 83.8 (82.1–85.5) 2 (1.444–2.779) 0.87 (0.63–1.213) 0.6 (0.56–0.63) < 0.001 CORB ≥ 2 27.7 (26.1–29.3) 81.8 (80.5–83.2) 22.6 (21.1–24) 85.5 (84.3–86.8) 1.52 (1.253–1.853) 0.88 (0.727–1.075) 0.56 (0.53–0.59) < 0.001 ADROP ≥ 3 38.6 (36.7–40.5) 76.9 (75.3–78.6) 25.2 (23.5–26.9) 86.1 (84.8–87.5) 1.67 (1.393–2.01) 0.8 (0.664–0.959) 0.62 (0.6–0.65) < 0.001 NEWS ≥ 7 44.7 (42.8–46.6) 65.8 (64-67.7) 14.3 (12.9–15.7) 90.3 (89.2–91.5) 1.31 (1.132–1.512) 0.84 (0.727–0.971) 0.58 (0.55–0.61) < 0.001 PNEUMONIA SHOCK ≥ 3 58.7 (56.5–60.9) 61.4 (61.4–63.6) 25.7 (23.8–27.7) 86.7 (85.2–88.2) 1.52 (1.328–1.745) 0.67 (0.586–0.77) 0.61 (0.58–0.64) < 0.001 REA ICU ≥ 7 31.3 (28.9–33.7) 78.8 (76.7–80.9) 25.7 (23.4–27.9) 83.1 (81.1–85) 1.48 (1.158–1.884) 0.87 (0.684–1.112) 0.59 (0.56–0.63) < 0.001 PSI ≥ 91 57.8 (54.7–60.9) 61 (57.9–64.1) 22.4 (19.8–25.1) 88.1 (86.1–90.2) 1.48 (1.219–1.803) 0.69 (0.569–0.841) 0.64 (0.61–0.66) < 0.001 SMART-COP ≥ 3 61 (59.4–59.4) 54.1 (52.5–55.8) 20.6 (19.3–21.9) 87.7 (86.6–88.7) 1.33 (1.214–1.457) 0.72 (0.658–0.79) 0.56 (0.44–0.67) 0.352 SMRT-CO ≥ 3 51.7 (49.9–53.4) 67.9 (66.3–69.5) 23.2 (21.7–24.6) 88.2 (87.1–89.3) 1.61 (1.411–1.832) 0.71 (0.625–0.812) 0.61 (0.58–0.63) < 0.001 SOAR ≥ 2 52.6 (50.3–54.8) 54.3 (52.1–56.5) 20.3 (18.5–22) 83.8 (82.2–85.5) 1.15 (1.017–1.301) 0.87 (0.773–0.988) 0.55 (0.52–0.58) 0.004 qSOFA ≥ 2 17.3 (16.1–18.5) 90.8 (89.8–91.7) 26.7 (25.3–28.2) 84.9 (83.7–86.1) 1.87 (1.439–2.431) 0.91 (0.701–1.184) 0.58 (0.56–0.61) < 0.001 SRIS ≥ 2 56.8 (55.2–58.4) 44.2 (42.6–45.8) 16.6 (15.4–17.8) 84 (82.8–85.2) 1.02 (0.942–1.099) 0.98 (0.905–1.057) 0.5 (0.47–0.52) 0.790 CAPSI ≥ 4 49.3 (47.3–51.2) 65 (63.2–66.9) 21.7 (20.1–23.3) 86.7 (85.4–88) 1.41 (1.228–1.616) 1.28 (1.117–1.471) 0.61 (0.58–0.64) < 0.001 CHARLSON ≥ 3 74.1 (72.7–75.5) 43.2 (41.6–44.8) 20.3 (19-21.6) 89.5 (88.5–90.5) 1.3 (1.212–1.402) 0.6 (0.558–0.646) 0.63 (0.6–0.65) < 0.001 Notes: SE: sensitivity; SP: specificity; PPV: positive predictive value; NPV: negative predictive value; LR: like hood ratio; ROC-curve: Area under the receiver operating characteristic curve. Discussion In this cohort study describing the performance of risk scores in predicting mortality in patients with CAP, it was observed that at 3 months, PSI, CHARLSON, and CRB-65 showed acceptable predictive performance. At 6 months, only PSI and CHARLSON maintained acceptable levels of accuracy. For the 12-month period, all evaluated scores exhibited very limited discriminatory ability, ranging from poor to nearly negligible. Patients experiencing fatal outcomes showed increased use of systemic steroids, vasopressor support, and need for ICU admission, with prolonged stays. Conversely, survivors were characterized by younger age, less renal function deterioration, and lower associated cardiovascular and respiratory comorbidity burden. The use of risk scores may serve as a complementary tool for predicting long-term mortality in hospitalized CAP patients. Several risk scores have been proposed to predict the prognosis of severe CAP patients and determine the need for ICU treatment. Anurag et al. ( 14 ) described that SCAP (ROC curve: 0.873) performed well in predicting CAP severity compared to PSI (area under ROC curve: 0.713) and CURB-65 (ROC curve: 0.643). Furthermore, SCAP showed excellent performance in predicting 14-day mortality, with an ROC curve of 0.963. These findings are consistent with those described by España et al. ( 15 ), who validated the SCAP questionnaire as acceptable for predicting 30-day mortality in CAP patients. However, although studies analyzing SCAP show good performance in predicting short-term mortality ( 14 – 16 ), our study generated new results indicating poor performance of SCAP in predicting mortality at 3 and 6 months, and negligible at 12 months. Alan et al. ( 17 ) described the long-term predictive performance of PSI and CURB-65 risk scores over a 6-year follow-up period in hospitalized CAP patients. Initial scores had prognostic accuracy with an area under the ROC curve of 0.79 to 0.83 (p < 0.001) and 0.73 to 0.80 (p < 0.001) after two years of follow-up, respectively. According to our data, PSI showed the best performance in predicting mortality at 3, 6, and 12 months, while CURB-65 exhibited poor performance in these periods. Additionally, in our comprehensive evaluation of clinical variables and laboratory results, such as comorbidities, renal involvement, and age, CHARLSON and NEWS scores demonstrated a strong negative predictive value independently for long-term mortality from CAP compared to other scores, such as CURB-65, CORB, SMART-COP, among others. Uranga et al. ( 18 ) developed and validated a prognostic index (CAPSI) specific for predicting one-year mortality in hospitalized CAP patients. The variables included in the risk score construction were age ≥ 80 years with 4 points, chronic heart failure with 2 points, dementia with 6 points, respiratory rate ≥ 30 breaths/min with 2 points, and blood urea nitrogen ≥ 30 mg/dL with 3 points. It was observed that the risk of one-year mortality increased by 24% (HR: 1.24; 95% CI: 1.19–1.28) for each unit increase in the predictive model. The CAPSI risk score showed predictive accuracy of 0.76 in the derivation cohort and 0.77 in the validation cohort, results superior to those reported in our study. Hoste et al. ( 19 ), in a multicenter cross-sectional study, demonstrated the importance of acute kidney injury and its directly proportional relationship with mortality, prolonged hospital stay, high costs to the system, chronic renal function impairment, and need for renal replacement therapy. In our cohort, deceased patients had higher levels of creatinine and blood urea nitrogen than those who survived at 12 months (27.2 vs. 22.5; p < 0.001) and (1.4 vs. 1.3; p = 0.370), respectively. This was because subjects with fatal outcomes had a higher morbidity burden, with presence of chronic kidney disease (8.7%), ICU requirement (17.6%), and vasopressor support (12.3%). It is noteworthy that the analyzed questionnaires (CURB-65, REA-ICU, ADROP, SCAP, PSI) include the variable of blood urea nitrogen, which can be used to optimize medical treatment through nephroprotective measures, such as fluid resuscitation, minimizing the use of nephrotoxic drugs or contrast media, and monitoring renal function after hospital discharge, thus promoting renal function and reducing mortality rates. The alteration of consciousness was more frequent in patients with fatal long-term outcomes, consistent with the findings of Banerdt et al. ( 8 ), whose study associated central nervous system involvement with delirium, resulting in a higher risk of death from any cause in 711 hospitalized patients. In their study, mortality at 6 months was significantly higher for patients with delirium (44.6%; 95% CI: 39.3%-50.1%) compared to the control group (20%; 95% CI: 15.4%-25.2%) (p < 0.001). Additionally, the presence of pleural effusion has also been associated with long-term mortality. Kookoolis et al. ( 9 ) described in a study of 104 patients with pleural effusion on chest X-ray an increase in mortality from 15% at 30 days to 32% at one year. This study also found that other variables, such as age, disease severity, presence of malignancy, and diagnosis of chronic lung disease, were associated with increased risk of mortality at 12 months, similar to the findings presented in our cohort. Patients with CAP are exposed to inflammation during the acute episode due to cytokine release and treatments received during the illness ( 20 ). This persistent chronic inflammation is associated with increased severity during the acute episode of CAP, prolonged hospital stay, and need for invasive ventilatory therapy, contributing to increased long-term mortality in these patients. Additionally, advanced age, functional limitations, and the presence of cardiovascular, respiratory, and renal comorbidities increase the chronic inflammatory burden in patients with a history of CAP ( 21 ). Since risk scores, such as SCAP, PSI, ADROP, PNEUMONIA shock, and SMART-COP, include clinical variables and laboratory findings that may be primarily related to acute inflammatory states, these scores may indirectly estimate hypercatabolism associated with different phases of the disease ( 22 , 23 ). Limitations Among the limitations of our study is its observational nature, based on information obtained from clinical records, which may have data omissions ( 13 ). It is important to note that the personnel responsible for data collection received adequate training to ensure accuracy in the transcription of the information obtained. Additionally, our study was conducted in multiple healthcare centers, which may be considered a strength for generalizing the results. We also believe that the sample size included was sufficient to meet the objectives and perform relevant hypothesis tests ( 13 ). It is relevant to note that information on causes of death at 12 months could not be obtained, which would have provided important additional data for analysis. Therefore, we suggest that future studies address this issue for a more comprehensive understanding of the results. We consider it pertinent to conduct additional research that can corroborate our findings, as well as externally validate established risk scores to predict mortality at 12 months, such as those described by Uranga et al. ( 18 ), which would help strengthen the evidence and improve accuracy in predicting long-term outcomes in CAP patients. Conclusion In predicting mortality in patients with CAP, it was observed that at 3 months, PSI, CHARLSON, and CRB-65 showed acceptable predictive performances. At 6 months, only PSI and CHARLSON maintained acceptable levels of accuracy. For the 12-month period, all evaluated scores exhibited very limited discriminatory ability, ranging from poor to almost negligible. Declarations Ethics approval and consent to participate The study was conducted in accordance with the principles of the current Helsinki Declaration, as well as local, regional, and international regulations pertaining to clinical research, including Colombian Law on Biomedical Research. Ethical approval was obtained from the Medical Ethics Committee of the Clínica Universidad de La Sabana (approval number 20220102). Prior to participating in the study, all participants provided written informed consent, and the confidentiality of their data was strictly maintained throughout the study. Consent for publication Not applicable. Competing interests The authors declare no competing interests Funding This work was supported by Universidad de la Sabana Grant MED-326-2022. Author Contribution ETQ, ARB, GGG, IPR, DT, LG, JV, CA, EM, JA, EM, JA, NC, AR, VM, MG, MF, CS, and LFR contributed substantially to the study design, data analysis and interpretation, and manuscript writing. ETQ, ABG, GGG and DT had full access to all study data and takes responsibility for data integrity as well as for accuracy of the included data analysis and, especially. All authors read and approved the final manuscript. Acknowledgements The authors are most thankful for the Universidad de La Sabana. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Gadsby NJ, Musher DM. The Microbial Etiology of Community-Acquired Pneumonia in Adults: from Classical Bacteriology to Host Transcriptional Signatures. Clin Microbiol Rev. 2022;35(4):e0001522. Martin-Loeches I, Torres A, Nagavci B, Aliberti S, Antonelli M, Bassetti M, Bos LD, Chalmers JD, Derde L, de Waele J, Garnacho-Montero J, Kollef M, Luna CM, Menendez R, Niederman MS, Ponomarev D, Restrepo MI, Rigau D, Schultz MJ, Weiss E, Welte T, Wunderink R. ERS/ESICM/ESCMID/ALAT guidelines for the management of severe community-acquired pneumonia. Intensive Care Med. 2023;49(6):615–32. Pletz MW, Blasi F, Chalmers JD, Dela Cruz CS, Feldman C, Luna CM, Ramirez JA, Shindo Y, Stolz D, Torres A, Webb B, Welte T, Wunderink R, Aliberti S. International Perspective on the New 2019 American Thoracic Society/Infectious Diseases Society of America Community-Acquired Pneumonia Guideline: A Critical Appraisal by a Global Expert Panel. Chest. 2020;158(5):1912–8. Nair GB, Niederman MS. Updates on community acquired pneumonia management in the ICU. Pharmacol Ther. 2021;217:107663. Cilloniz C, Ferrer M, Pericàs JM, Serrano L, Méndez R, Gabarrús A, Peroni HJ, Ruiz LA, Menéndez R, Zalacain R, Torres A. Validation of IDSA/ATS Guidelines for ICU Admission in Adults Over 80 Years Old With Community-Acquired Pneumonia. Arch Bronconeumol. 2023;59(1):19–26. Odutayo A, Wong CX, Farkouh M, Altman DG, Hopewell S, Emdin CA, Hunn BH. AKI and Long-Term Risk for Cardiovascular Events and Mortality. J Am Soc Nephrol. 2017;28(1):377–87. Shah S, Leonard AC, Harrison K, Meganathan K, Christianson AL, Thakar CV. Mortality and Recovery Associated with Kidney Failure due to Acute Kidney Injury. Clin J Am Soc Nephrol. 2020;15(7):995–1006. Banerdt JK, Mateyo K, Wang L, Lindsell CJ, Riviello ED, Saylor D, Heimburger DC, Ely EW. Delirium as a predictor of mortality and disability among hospitalized patients in Zambia. PLoS ONE. 2021;16(2):e0246330. Kookoolis AS, Puchalski JT, Murphy TE, Araujo KL, Pisani MA. Mortality of Hospitalized Patients with Pleural Effusions. J Pulm Respir Med. 2014;4(3):184. Lim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI, Lewis SA, Macfarlane JT. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58(5):377–82. Fine MJ, Auble TE, Yealy DM, Hanusa BH, Weissfeld LA, Singer DE, Coley CM, Marrie TJ, Kapoor WN. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med. 1997;336(4):243–50. Harris PA, Taylor R, Thielke R, et al. Research electronic data capture (REDCap)-Ametadata-driven methodology and workflow process for providing translationalresearch informatics support. J Biomed Inform. 2009;42(2):377–81. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. New York, New York: John Wiley & Sons, Inc; 2000. p. 162. Anurag A, Preetam M. Validation of PSI/PORT, CURB-65 and SCAP scoring system in COVID-19 pneumonia for prediction of disease severity and 14-day mortality. Clin Respir J. 2021;15(5):467–71. España PP, Capelastegui A, Quintana JM, Bilbao A, Diez R, Pascual S, Esteban C, Zalacaín R, Menendez R, Torres A. Validation and comparison of SCAP as a predictive score for identifying low-risk patients in community-acquired pneumonia. J Infect. 2010;60(2):106–13. Falcone M, Corrao S, Venditti M, Serra P, Licata G. Performance of PSI, CURB-65, and SCAP scores in predicting the outcome of patients with community-acquired and healthcare-associated pneumonia. Intern Emerg Med. 2011;6(5):431–6. Alan M, Grolimund E, Kutz A, Christ-Crain M, Thomann R, Falconnier C, Hoess C, Henzen C, Zimmerli W, Mueller B, Schuetz P. ProHOSP study group. Clinical risk scores and blood biomarkers as predictors of long-term outcome in patients with community-acquired pneumonia: a 6-year prospective follow-up study. J Intern Med. 2015;278(2):174–84. Uranga A, Quintana JM, Aguirre U, Artaraz A, Diez R, Pascual S, Ballaz A, España PP. Predicting 1-year mortality after hospitalization for community-acquired pneumonia. PLoS ONE. 2018;13(2):e0192750. Hoste EA, Bagshaw SM, Bellomo R, Cely CM, Colman R, Cruz DN, Edipidis K, Forni LG, Gomersall CD, Govil D, Honoré PM, Joannes-Boyau O, Joannidis M, Korhonen AM, Lavrentieva A, Mehta RL, Palevsky P, Roessler E, Ronco C, Uchino S, Vazquez JA, Vidal Andrade E, Webb S, Kellum JA. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med. 2015;41(8):1411–23. Póvoa P, Coelho L, Salluh J. When should we use corticosteroids in severe community-acquired pneumonia? Curr Opin Infect Dis. 2021;34(2):169–74. Corrales-Medina VF, Alvarez KN, Yende S. Hospitalization for pneumonia and risk of cardiovascular disease–reply. JAMA. 2015;313(17):1753–4. Casaer MP, Mesotten D, Schetz MR. Bench-to-bedside review: metabolism and nutrition. Crit Care. 2008;12(4):222. Patkova A, Joskova V, Havel E, Kovarik M, Kucharova M, Zadak Z, Hronek M, Energy. Protein, Carbohydrate, and Lipid Intakes and Their Effects on Morbidity and Mortality in Critically Ill Adult Patients: A Systematic Review. Adv Nutr. 2017;8(4):624–34. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.pdf Cite Share Download PDF Status: Published Journal Publication published 09 Jul, 2024 Read the published version in BMC Pulmonary Medicine → Version 1 posted Editorial decision: Revision requested 18 Apr, 2024 Reviews received at journal 17 Apr, 2024 Reviewers agreed at journal 03 Apr, 2024 Reviews received at journal 24 Mar, 2024 Reviewers agreed at journal 04 Mar, 2024 Reviewers invited by journal 26 Feb, 2024 Editor assigned by journal 24 Feb, 2024 Editor invited by journal 21 Feb, 2024 Submission checks completed at journal 21 Feb, 2024 First submitted to journal 12 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3951887","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274291331,"identity":"99f0dffa-c147-46ca-9442-59ce92f890cd","order_by":0,"name":"Eduardo Tuta-Quintero","email":"","orcid":"","institution":"Universidad de La Sabana","correspondingAuthor":false,"prefix":"","firstName":"Eduardo","middleName":"","lastName":"Tuta-Quintero","suffix":""},{"id":274291332,"identity":"efd92b5c-8964-4193-bfe4-dab1bdd8cfff","order_by":1,"name":"Alirio R. Bastidas","email":"data:image/png;base64,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","orcid":"","institution":"Universidad de La Sabana","correspondingAuthor":true,"prefix":"","firstName":"Alirio","middleName":"R.","lastName":"Bastidas","suffix":""},{"id":274291333,"identity":"eae38acf-8825-4233-b089-2f4966502e26","order_by":2,"name":"Gabriela Guerrón-Gómez","email":"","orcid":"","institution":"Universidad de La 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Sabana","correspondingAuthor":false,"prefix":"","firstName":"Eathan","middleName":"","lastName":"Mikler","suffix":""},{"id":274291340,"identity":"cb155c3c-4e35-4f1a-9fd0-fd483a6b9950","order_by":9,"name":"Juan Arcila","email":"","orcid":"","institution":"Universidad de La Sabana","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Arcila","suffix":""},{"id":274291341,"identity":"0e16bc07-32e6-4034-a4b5-035b9b260f96","order_by":10,"name":"Nicolas Chavez","email":"","orcid":"","institution":"Universidad de La Sabana","correspondingAuthor":false,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Chavez","suffix":""},{"id":274291342,"identity":"95e88f9e-8b94-4610-8699-bd27557408fa","order_by":11,"name":"Allison Riviera","email":"","orcid":"","institution":"Universidad de La Sabana","correspondingAuthor":false,"prefix":"","firstName":"Allison","middleName":"","lastName":"Riviera","suffix":""},{"id":274291343,"identity":"64a6e07d-3420-43da-87e2-0d05b61cf5db","order_by":12,"name":"Valentina Maldonado","email":"","orcid":"","institution":"Universidad de La Sabana","correspondingAuthor":false,"prefix":"","firstName":"Valentina","middleName":"","lastName":"Maldonado","suffix":""},{"id":274291344,"identity":"5c2dc425-ffe8-4a5c-a1d8-84b3007c9b76","order_by":13,"name":"María Galindo","email":"","orcid":"","institution":"Universidad de La Sabana","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Galindo","suffix":""},{"id":274291345,"identity":"d83c3b60-28b0-440d-bf36-be09eb98a97f","order_by":14,"name":"María Fernández","email":"","orcid":"","institution":"Universidad de La Sabana","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Fernández","suffix":""},{"id":274291346,"identity":"2949c50b-77d2-43a0-bab2-ed0811d57009","order_by":15,"name":"Carolina Schloss","email":"","orcid":"","institution":"Universidad de La Sabana","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Schloss","suffix":""},{"id":274291347,"identity":"4f8ae9b9-675d-463b-8121-5933fa143aae","order_by":16,"name":"Luis Felipe Reyes","email":"","orcid":"","institution":"Universidad de La Sabana","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"Felipe","lastName":"Reyes","suffix":""}],"badges":[],"createdAt":"2024-02-12 20:29:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3951887/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3951887/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12890-024-03121-7","type":"published","date":"2024-07-10T00:34:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51565758,"identity":"52fad655-3dc0-4f3c-b166-e899b10b774a","added_by":"auto","created_at":"2024-02-23 19:06:01","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35728,"visible":true,"origin":"","legend":"\u003cp\u003ePatient Admission Flowchart.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3951887/v1/5ed6ff9e5a42c9bca873f77e.jpg"},{"id":60035949,"identity":"c792b796-79a6-45ea-837d-c21153ee1757","added_by":"auto","created_at":"2024-07-11 00:34:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1034802,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3951887/v1/8365a34b-f6ad-438f-bcbe-a1574a6a10ba.pdf"},{"id":51565759,"identity":"02b2ba7a-f972-4a42-b998-85606ab22f71","added_by":"auto","created_at":"2024-02-23 19:06:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":297100,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3951887/v1/ac5e40acb61e732a1e5627ec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performance of Risk Scores in Predicting Mortality at 3, 6, and 12 Months in Patients Diagnosed with Community-Acquired Pneumonia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCommunity-acquired pneumonia (CAP) is the leading infectious cause of death worldwide, accounting for 6.1% of all fatal outcomes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite advances in CAP prevention and diagnosis, the incidence is estimated to be 106 to 164 per 10,000 inhabitants in the United States and up to 294 cases per 10,000 inhabitants in Latin America, especially in individuals over 65 years old, those with comorbidities, or immunosuppressed individuals (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Mortality from CAP ranges from 5\u0026ndash;25% in in-hospital settings and can reach up to 50% in intensive care units (ICUs) (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, in order to reduce the disease burden and costs associated with CAP, scales have been validated to stratify pneumonia severity, predict prognosis, and anticipate the need for ICU, providing an advantage in guiding early therapeutic approaches and positively impacting short and long-term clinical outcomes (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, the use of risk scores is recommended by clinical practice guidelines and consensus from the American Thoracic Society/Infectious Diseases Society of America (ATS/IDSA) to assess the prognosis and the most suitable treatment location for CAP. The most recommended ones are the Pneumonia Severity Index (PSI) and CURB-65 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, the clinical applicability of these scores is primarily for estimating mortality within the first 30 days, and there is still insufficient evidence demonstrating their long-term predictive capacity (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Therefore, efforts have been made to describe the long-term predictive capacity of clinical variables used in the construction of widely studied risk scores for CAP, including azotemia, alteration of mental status, and evidence of pleural effusion in chest X-rays, all of which have evidence associating them with long-term mortality (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eShah et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) described a strong association between acute kidney injury and unfavorable clinical outcomes, such as an increased risk of progression to chronic kidney disease, congestive heart failure, acute myocardial infarction, stroke, and death at 3 and 6 months. Currently, the use of risk scores is limited to evaluating short-term clinical outcomes such as mortality and the need for mechanical ventilation, and the guidance from clinical practice guidelines and consensus on the ideal tool for long-term outcomes is almost nonexistent or generates ambiguity when choosing to estimate mortality risk.\u003c/p\u003e \u003cp\u003eThe use of risk scores, addressing intrinsic patient characteristics, clinical signs, symptoms, and laboratory tests, could help identify individuals at high risk of long-term mortality from lower respiratory tract infection (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, available evidence is limited regarding the use of clinical variables and risk scores in patients diagnosed with CAP (\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Therefore, the aim of this article is to analyze and compare the performance of 16 clinical questionnaires in predicting mortality at three, six, and twelve months in adult patients with CAP.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eAnalytical observational study conducted on patients diagnosed with Community-Acquired Pneumonia (CAP) in high-complexity institutions in Colombia. Patients were assessed and admitted to emergency services and Intensive Care Units (ICUs) from January 2010 to January 2020.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEligibility Criteria\u003c/h2\u003e \u003cp\u003eMen and women aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years were included, evaluated for at least 6 hours in emergency services or ICUs due to CAP diagnosis. CAP diagnosis was established based on ATS/IDSA criteria, meaning that patients had to present signs and symptoms of lower respiratory tract infection (cough, dyspnea, purulent sputum, crackles, pectoriloquy, etc.), systemic involvement (fever, hypotension, altered consciousness, night sweats, leukocytosis, among others), radiographic findings consistent with pneumonia, and the absence of another disease explaining the clinical picture. Additionally, medical records needed to include sufficient information for the evaluation of CURB-65, CRB-65, SCAP, CORB, ADROP, NEWS, Pneumonia Shock, REA-ICU, PSI, SMART-COP, SMRT-CO, SOAR, qSOFA, SRIS, CAPSI, and Charlson comorbidity index scores (Supplementary File 1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cp\u003eSociodemographic variables (age and gender), comorbidities through the Charlson index, vital signs, consciousness status, chest X-ray findings such as multilobar involvement or pleural effusion, and laboratory tests including arterial gases, hematocrit, white blood cell count, blood urea nitrogen, serum sodium, albumin, and blood glucose were included. Additionally, the need for ICU, invasive mechanical ventilation (IMV), and/or vasopressor support were considered. The dependent variable was mortality evaluated at 3, 6, and 12 months following CAP diagnosis. To minimize possible errors in outcome classification, the research team collecting data from clinical records had medical expertise in diagnosing the studied pathology. To reduce typing bias, information was reviewed by at least two team members.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eSample Size\u003c/h2\u003e \u003cp\u003eSample size calculation used data from Lim et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), describing a sensitivity of 75% and specificity of 69% for CURB-65, and Fine et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), reporting a sensitivity of 100% and specificity of 52.2% for PSI. Using the formula for paired diagnostic tests, with an expected mortality of 6.1%, 90% power, and statistical significance of 0.05, a minimum of 625 subjects was required.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMissing data\u003c/h2\u003e \u003cp\u003eAn imputation analysis addressed missing data, employing weighted mean imputation for quantitative variables and logistic regression for qualitative variables with a loss of less than 10% (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Variables with more than 10% data loss were excluded. A comparison between non-imputed and imputed results ensured that imputation did not introduce bias or significantly alter the original data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were entered into REDCap (Research Electronic Data Capture) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) for subsequent analysis using SPSS 25 software (IBM Corp. IBM SPSS Statistics for Windows, Version 25.0 licensed). Qualitative variables were reported in frequencies and percentages, while quantitative variables were summarized using mean and standard deviation for normally distributed ones and median and interquartile range for non-normally distributed ones. Bivariate analysis between questionnaires and the outcome (alive or dead) was performed using the chi-square test for qualitative variables and Student's t-test or Mann-Whitney U test for quantitative variables (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Scores obtained for each questionnaire were used to calculate the area under the ROC curve, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+), and negative likelihood ratio (LR-), using the established cutoff point for each questionnaire (Supplementary File 2). ROC curves of different scores were compared with the DeLong test, considering a value of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as significant. The ROC curve was interpreted as follows: 0.50, absence of discriminatory capacity; 0.51 to 0.60, almost null discriminatory capacity; 0.61 to 0.69, poor discriminatory ability; \u0026gt; 0.7 to 0.8, acceptable discrimination ability; \u0026gt; 0.8 to 0.9, excellent discriminatory capacity; and \u0026gt;\u0026thinsp;0.9, outstanding discriminatory capacity (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOut of a total of 7454 potentially eligible patients, 3688 were included in the final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Survival at 3, 6, and 12 months was 94.8% (3498/3688), 91.7% (3381/3688) and 83.7% (3086/3688), respectively.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Characteristics\u003c/h2\u003e \u003cp\u003eThe average age was 63.5 years (SD: 21.39), and 59.3% (2188/3688) of the patients were male. The most common symptoms in the overall population were cough in 82.6% (3045/3688), dyspnea in 67.4% (2486/3688), and fever in 47.6% (1756/3688) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The most prevalent comorbidities were arterial hypertension in 46.1% (1699/3688), COPD in 25.5% (941/3688), and smoking in 18.1% (668/3688).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics of the population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal population n\u0026thinsp;=\u0026thinsp;3688\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAlive n\u0026thinsp;=\u0026thinsp;3086\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDeaths n\u0026thinsp;=\u0026thinsp;602\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.5 (21.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (21.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.7 (17.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMales, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2188 (59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1801 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e387 (64.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3045 (82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2586 (83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e459 (76.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnoea, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2486 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2095 (67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e391 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1756 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1500 (48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epleuritic pain, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e954 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e843 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlteration of consciousness, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWheezing, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e829 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e719 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFiO2%, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.5 (12.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.1 (11.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.3 (14.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArterial hypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1699 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1386 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e313 (52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic heart failure, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e447 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e357 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute myocardial infarction, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e168 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e484 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e233 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e941 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e770 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMellitus diabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e423 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e342 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e149 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e237 (6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsthma, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunosuppression, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (3.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: SD: Standard deviation; n: number; FiO2: Fraction of inspired oxygen; COPD: Chronic obstructive pulmonary disease.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eArterial Gases and Blood Tests\u003c/h2\u003e \u003cp\u003eThe inspired fraction of oxygen in survivors was 28.1% (SD: 11.59) compared to 30.3% (SD: 14.98) in non-survivors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Blood urea nitrogen was 4.7 mg/dl lower in survivors compared to the deceased group (22.5 vs. 27.2; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Laboratory test results are described in Supplementary table 3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTreatment During Hospitalization\u003c/h2\u003e \u003cp\u003e12.1% (73/602) of deceased patients had septic shock compared to 6.4% (198/3086) of surviving patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Supplementary table 4). The use of vasopressor support and systemic corticosteroids was 12.3% (78/602) and 31.6% (190/602) in deceased patients, respectively. The need for ICU was 7.6% higher in deceased patients compared to the survivor group (17.6 vs. 10) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePerformance of Risk Scores for Mortality at 3, 6, and 12 Months\u003c/h2\u003e \u003cp\u003eAt 3 months, PSI, CHARLSON, and CRB-65 scores showed ROC curves of 0.74 (95% CI: 0.71\u0026ndash;0.77), 0.71 (95% CI: 0.67\u0026ndash;0.74), and 0.7 (95% CI: 0.66\u0026ndash;0.74), (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). At 6 months, PSI and CHARLSON scores showed performances of 0.74 (95% CI: 0.72\u0026ndash;0.77) and 0.72 (95% CI: 0.69\u0026ndash;0.74), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At 12 months, all evaluated scores showed poor discriminatory capacity, including PSI, which decreased its capacity to poor with an ROC curve of 0.64 (95% CI: 0.61\u0026ndash;0.66) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The score with the lowest performance in predicting mortality at 3, 6, and 12 months was SIRS with an ROC curve of 0.51 (95% CI: 0.47\u0026ndash;0.55), 0.5 (95% CI: 0.47\u0026ndash;0.54), and 0.5 (95% CI: 0.47\u0026ndash;0.52), respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance of Risk Scores in Community Acquired Pneumonia for 3 months Mortality Prediction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVPP (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVPN (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLR+ (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLR- (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUCOR (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMortality 3 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCURB-65\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.9 (70.2\u0026ndash;73.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.9 (50.9\u0026ndash;54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.7 (7.6\u0026ndash;9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.8 (96.1\u0026ndash;97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.53 (1.283\u0026ndash;1.816)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.53 (0.446\u0026ndash;0.632)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.69 (0.64\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRB-65\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.4 (47.7\u0026ndash;51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.1 (78.7\u0026ndash;81.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.3 (11.1\u0026ndash;13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.6 (95.9\u0026ndash;97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.48 (1.836\u0026ndash;3.344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63 (0.468\u0026ndash;0.853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.7 (0.66\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCAP\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.6 (26.4\u0026ndash;30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.1 (21.2\u0026ndash;25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.2 (8.8\u0026ndash;11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.2 (95.3\u0026ndash;97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.12 (1.027\u0026ndash;1.211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62 (0.568\u0026ndash;0.704)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.66 (0.6\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCORB\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.3 (34.6\u0026ndash;37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.3 (79.9\u0026ndash;82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.1 (9-11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.7 (94.9\u0026ndash;96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.93 (1.411\u0026ndash;2.653)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.78 (0.572\u0026ndash;1.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61 (0.56\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADROP\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.7 (42.7\u0026ndash;46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.5 (73.8\u0026ndash;77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.5 (9.3\u0026ndash;11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.5 (94.7\u0026ndash;96.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.83 (1.373\u0026ndash;2.427)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73 (0.551\u0026ndash;0.974)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.67 (0.63\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEWS \u0026ge; 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.2 (46.2\u0026ndash;50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.9 (63-66.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3 (4.4\u0026ndash;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.9 (96.2\u0026ndash;97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37 (1.089\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8 (0.634\u0026ndash;1.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61 (0.56\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNEUMONIA SHOCK\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.2 (68.2\u0026ndash;72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.7 (57.5\u0026ndash;61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.2 (9.8\u0026ndash;12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.5 (95.7\u0026ndash;97.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.74 (1.412\u0026ndash;2.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5 (0.404\u0026ndash;0.616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.69 (0.65\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREA ICU\u0026thinsp;\u0026ge;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.3 (38.8\u0026ndash;43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.1 (76-80.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.3 (9.7\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.2 (94.1\u0026ndash;96.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.89 (1.278\u0026ndash;2.796)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75 (0.508\u0026ndash;1.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.65 (0.59\u0026ndash;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSI\u0026thinsp;\u0026ge;\u0026thinsp;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.3 (72.5\u0026ndash;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.7 (56.6\u0026ndash;62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.2 (7.4\u0026ndash;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.8 (96.9\u0026ndash;98.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.87 (1.374\u0026ndash;2.543)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41 (0.304\u0026ndash;0.563)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.74 (0.71\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMART-COP \u0026ge; 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60 (58.4\u0026ndash;61.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.3 (50.7\u0026ndash;53.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.4 (5.6\u0026ndash;7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96 (95.4\u0026ndash;96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.26 (1.082\u0026ndash;1.464)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.76 (0.657\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.53 (0.39\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMRT-CO \u0026ge; 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55.9 (54.2\u0026ndash;57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (64.4\u0026ndash;67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.6 (7.7\u0026ndash;9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.3 (95.7\u0026ndash;97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.64 (1.336\u0026ndash;2.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67 (0.543\u0026ndash;0.822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.62 (0.57\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOAR\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64.3 (62.2\u0026ndash;66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.2 (51.9\u0026ndash;56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.1 (6.9\u0026ndash;9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96 (95.2\u0026ndash;96.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.4 (1.147\u0026ndash;1.717)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66 (0.538\u0026ndash;0.805)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.62 (0.57\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eqSOFA\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.1 (20.8\u0026ndash;23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.1 (89.1\u0026ndash;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.8 (9.8\u0026ndash;11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.5 (94.8\u0026ndash;96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.23 (1.445\u0026ndash;3.436)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.86 (0.561\u0026ndash;1.333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61 (0.57\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRIS\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.4 (55.8\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.1 (42.5\u0026ndash;45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.3 (4.6-6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95 (94.3\u0026ndash;95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.03 (0.901\u0026ndash;1.168)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97 (0.85\u0026ndash;1.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.51 (0.47\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPSI\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.8 (55.9\u0026ndash;59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.9 (62-65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.9 (7.8\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.1 (95.4\u0026ndash;96.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.6 (1.289\u0026ndash;1.992)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.52 (1.219\u0026ndash;1.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.68 (0.63\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHARLSON\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.7 (83.6\u0026ndash;85.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.7 (40.1\u0026ndash;43.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.3 (6.5\u0026ndash;8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.1 (97.6\u0026ndash;98.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.45 (1.288\u0026ndash;1.641)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37 (0.324\u0026ndash;0.413)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.71 (0.67\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNotes: SE: sensitivity; SP: specificity; PPV: positive predictive value; NPV: negative predictive value; LR: like hood ratio; ROC-curve: Area under the receiver operating characteristic curve.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance of Risk Scores in Community Acquired Pneumonia for 6 months Mortality Prediction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVPP (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVPN (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLR+ (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLR- (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUCOR (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMortality 6 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCURB-65\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.1 (70.4\u0026ndash;73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.8 (51.8\u0026ndash;55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.5 (12.2\u0026ndash;14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e95.1 (94.2\u0026ndash;95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.56 (1.353-1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.52 (0.449\u0026ndash;0.598)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.69 (0.66\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRB-65\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (45.3\u0026ndash;48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.9 (79.5\u0026ndash;82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.8 (17.5\u0026ndash;20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.2 (93.4\u0026ndash;95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.46 (1.926\u0026ndash;3.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66 (0.514\u0026ndash;0.836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.69 (0.66\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCAP\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.8 (26.6\u0026ndash;30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.4 (86.9\u0026ndash;89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.2 (18.3\u0026ndash;22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.4 (91.2\u0026ndash;93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.49 (1.609\u0026ndash;3.843)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.81 (0.521\u0026ndash;1.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.68 (0.63\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCORB\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.2 (32.5\u0026ndash;35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.7 (80.3\u0026ndash;83.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.3 (14-16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.8 (91.9\u0026ndash;93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.87 (1.447\u0026ndash;2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.81 (0.624\u0026ndash;1.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.59 (0.56\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADROP\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.2 (45.2\u0026ndash;49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.5 (74.9\u0026ndash;78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.1 (15.6\u0026ndash;18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.4 (92.4\u0026ndash;94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.01 (1.586\u0026ndash;2.547)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69 (0.545\u0026ndash;0.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.68 (0.65\u0026ndash;0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEWS \u0026ge; 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.5 (44.6\u0026ndash;48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.2 (63.3\u0026ndash;67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.3 (7.2\u0026ndash;9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.7 (93.8\u0026ndash;95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34 (1.107\u0026ndash;1.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82 (0.68\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.6 (0.56\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNEUMONIA SHOCK\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69.3 (67.2\u0026ndash;71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.8 (58.7\u0026ndash;63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.2 (15.6\u0026ndash;18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.4 (93.4\u0026ndash;95.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.77 (1.487\u0026ndash;2.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.51 (0.425\u0026ndash;0.601)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.69 (0.65\u0026ndash;0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREA ICU\u0026thinsp;\u0026ge;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.2 (35.7\u0026ndash;40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.6 (80.9\u0026ndash;84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.9 (19.1\u0026ndash;22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92 (90.8\u0026ndash;93.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.24 (1.655\u0026ndash;3.041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.74 (0.545-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.62 (0.58\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSI\u0026thinsp;\u0026ge;\u0026thinsp;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.2 (78\u0026ndash;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.9 (57.9\u0026ndash;64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.9 (12.6\u0026ndash;17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.8 (7-10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93 (1.492\u0026ndash;2.487)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41 (0.315\u0026ndash;0.524)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.74 (0.72\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMART-COP \u0026ge; 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.5 (61-64.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53 (51.4\u0026ndash;54.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.8 (9.8\u0026ndash;11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94 (93.2\u0026ndash;94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.33 (1.178\u0026ndash;1.502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71 (0.626\u0026ndash;0.799)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.56 (0.42\u0026ndash;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMRT-CO \u0026ge; 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.8 (50-53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.4 (64.8\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.8 (11.6\u0026ndash;13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.5 (92.7\u0026ndash;94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54 (1.303\u0026ndash;1.819)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73 (0.615\u0026ndash;0.859)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.59 (0.56\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOAR\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.7 (64.6-148700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55 (52.8\u0026ndash;57.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.9 (11.4\u0026ndash;14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.3 (93.3\u0026ndash;95.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48 (1.255\u0026ndash;1.751)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.61 (0.513\u0026ndash;0.715)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.64 (0.6\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eqSOFA\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.5 (20.2\u0026ndash;22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.4 (89.5\u0026ndash;91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (15.8\u0026ndash;18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.7 (91.9\u0026ndash;93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.25 (1.586\u0026ndash;3.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87 (0.612\u0026ndash;1.232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.6 (0.57\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRIS\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.7 (56.1\u0026ndash;59.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.2 (42.6\u0026ndash;45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.6 (7.7\u0026ndash;9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92 (91.1\u0026ndash;92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.03 (0.931\u0026ndash;1.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96 (0.865\u0026ndash;1.064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5 (0.47\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPSI\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.5 (54.6\u0026ndash;58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.6 (62.7\u0026ndash;66.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.6 (12.3\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.7 (92.8\u0026ndash;94.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.59 (1.335\u0026ndash;1.905)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.48 (1.243\u0026ndash;1.774)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.67 (0.63\u0026ndash;0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHARLSON\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.9 (86.9\u0026ndash;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.9 (41.3\u0026ndash;44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.4 (10.4\u0026ndash;12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e97.7 (97.2\u0026ndash;98.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.54 (1.397\u0026ndash;1.699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.28 (0.255\u0026ndash;0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.72 (0.69\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNotes: SE: sensitivity; SP: specificity; PPV: positive predictive value; NPV: negative predictive value; LR: like hood ratio; ROC-curve: Area under the receiver operating characteristic curve.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance of Risk Scores in Community Acquired Pneumonia for 12 months Mortality Prediction\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVPP (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVPN (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLR+ (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLR- (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAUCOR (CI 95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMortality 12 months\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCURB-65\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.8 (61.9\u0026ndash;65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.4 (54.4\u0026ndash;56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.6 (20-23.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.4 (87.2\u0026ndash;89.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.4 (1.254\u0026ndash;1.564)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66 (0.595\u0026ndash;0.743)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.63 (0.6\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRB-65\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38 (36.3\u0026ndash;39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.5 (81.5\u0026ndash;82.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.7 (26.1\u0026ndash;29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.6 (86.5\u0026ndash;88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.06 (1.703\u0026ndash;2.482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.76 (0.63\u0026ndash;0.919)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.62 (0.59\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCAP\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.3 (20.3\u0026ndash;24.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.9 (88.9\u0026ndash;90.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.7 (28.5\u0026ndash;32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.8 (82.1\u0026ndash;85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2 (1.444\u0026ndash;2.779)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87 (0.63\u0026ndash;1.213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.6 (0.56\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCORB\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.7 (26.1\u0026ndash;29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.8 (80.5\u0026ndash;83.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.6 (21.1\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.5 (84.3\u0026ndash;86.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.52 (1.253\u0026ndash;1.853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88 (0.727\u0026ndash;1.075)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.56 (0.53\u0026ndash;0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADROP\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.6 (36.7\u0026ndash;40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.9 (75.3\u0026ndash;78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.2 (23.5\u0026ndash;26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.1 (84.8\u0026ndash;87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.67 (1.393\u0026ndash;2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8 (0.664\u0026ndash;0.959)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.62 (0.6\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEWS \u0026ge; 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.7 (42.8\u0026ndash;46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.8 (64-67.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.3 (12.9\u0026ndash;15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90.3 (89.2\u0026ndash;91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.31 (1.132\u0026ndash;1.512)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84 (0.727\u0026ndash;0.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.58 (0.55\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNEUMONIA SHOCK\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.7 (56.5\u0026ndash;60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.4 (61.4\u0026ndash;63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.7 (23.8\u0026ndash;27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.7 (85.2\u0026ndash;88.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.52 (1.328\u0026ndash;1.745)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67 (0.586\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61 (0.58\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREA ICU\u0026thinsp;\u0026ge;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.3 (28.9\u0026ndash;33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.8 (76.7\u0026ndash;80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.7 (23.4\u0026ndash;27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.1 (81.1\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.48 (1.158\u0026ndash;1.884)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87 (0.684\u0026ndash;1.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.59 (0.56\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSI\u0026thinsp;\u0026ge;\u0026thinsp;91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.8 (54.7\u0026ndash;60.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (57.9\u0026ndash;64.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.4 (19.8\u0026ndash;25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.1 (86.1\u0026ndash;90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.48 (1.219\u0026ndash;1.803)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69 (0.569\u0026ndash;0.841)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.64 (0.61\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMART-COP \u0026ge; 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61 (59.4\u0026ndash;59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.1 (52.5\u0026ndash;55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.6 (19.3\u0026ndash;21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.7 (86.6\u0026ndash;88.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.33 (1.214\u0026ndash;1.457)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.72 (0.658\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.56 (0.44\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMRT-CO \u0026ge; 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.7 (49.9\u0026ndash;53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67.9 (66.3\u0026ndash;69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.2 (21.7\u0026ndash;24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.2 (87.1\u0026ndash;89.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.61 (1.411\u0026ndash;1.832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.71 (0.625\u0026ndash;0.812)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61 (0.58\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOAR\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52.6 (50.3\u0026ndash;54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.3 (52.1\u0026ndash;56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.3 (18.5\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.8 (82.2\u0026ndash;85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.15 (1.017\u0026ndash;1.301)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87 (0.773\u0026ndash;0.988)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.55 (0.52\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eqSOFA\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.3 (16.1\u0026ndash;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.8 (89.8\u0026ndash;91.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.7 (25.3\u0026ndash;28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.9 (83.7\u0026ndash;86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.87 (1.439\u0026ndash;2.431)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.91 (0.701\u0026ndash;1.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.58 (0.56\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRIS\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56.8 (55.2\u0026ndash;58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.2 (42.6\u0026ndash;45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.6 (15.4\u0026ndash;17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84 (82.8\u0026ndash;85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.02 (0.942\u0026ndash;1.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.98 (0.905\u0026ndash;1.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5 (0.47\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAPSI\u0026thinsp;\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.3 (47.3\u0026ndash;51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65 (63.2\u0026ndash;66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.7 (20.1\u0026ndash;23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.7 (85.4\u0026ndash;88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.41 (1.228\u0026ndash;1.616)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.28 (1.117\u0026ndash;1.471)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.61 (0.58\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHARLSON\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.1 (72.7\u0026ndash;75.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.2 (41.6\u0026ndash;44.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.3 (19-21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e89.5 (88.5\u0026ndash;90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.3 (1.212\u0026ndash;1.402)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.6 (0.558\u0026ndash;0.646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.63 (0.6\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNotes: SE: sensitivity; SP: specificity; PPV: positive predictive value; NPV: negative predictive value; LR: like hood ratio; ROC-curve: Area under the receiver operating characteristic curve.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cohort study describing the performance of risk scores in predicting mortality in patients with CAP, it was observed that at 3 months, PSI, CHARLSON, and CRB-65 showed acceptable predictive performance. At 6 months, only PSI and CHARLSON maintained acceptable levels of accuracy. For the 12-month period, all evaluated scores exhibited very limited discriminatory ability, ranging from poor to nearly negligible. Patients experiencing fatal outcomes showed increased use of systemic steroids, vasopressor support, and need for ICU admission, with prolonged stays. Conversely, survivors were characterized by younger age, less renal function deterioration, and lower associated cardiovascular and respiratory comorbidity burden. The use of risk scores may serve as a complementary tool for predicting long-term mortality in hospitalized CAP patients.\u003c/p\u003e \u003cp\u003eSeveral risk scores have been proposed to predict the prognosis of severe CAP patients and determine the need for ICU treatment. Anurag et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) described that SCAP (ROC curve: 0.873) performed well in predicting CAP severity compared to PSI (area under ROC curve: 0.713) and CURB-65 (ROC curve: 0.643). Furthermore, SCAP showed excellent performance in predicting 14-day mortality, with an ROC curve of 0.963. These findings are consistent with those described by Espa\u0026ntilde;a et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), who validated the SCAP questionnaire as acceptable for predicting 30-day mortality in CAP patients. However, although studies analyzing SCAP show good performance in predicting short-term mortality (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), our study generated new results indicating poor performance of SCAP in predicting mortality at 3 and 6 months, and negligible at 12 months.\u003c/p\u003e \u003cp\u003eAlan et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) described the long-term predictive performance of PSI and CURB-65 risk scores over a 6-year follow-up period in hospitalized CAP patients. Initial scores had prognostic accuracy with an area under the ROC curve of 0.79 to 0.83 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 0.73 to 0.80 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) after two years of follow-up, respectively. According to our data, PSI showed the best performance in predicting mortality at 3, 6, and 12 months, while CURB-65 exhibited poor performance in these periods. Additionally, in our comprehensive evaluation of clinical variables and laboratory results, such as comorbidities, renal involvement, and age, CHARLSON and NEWS scores demonstrated a strong negative predictive value independently for long-term mortality from CAP compared to other scores, such as CURB-65, CORB, SMART-COP, among others.\u003c/p\u003e \u003cp\u003eUranga et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) developed and validated a prognostic index (CAPSI) specific for predicting one-year mortality in hospitalized CAP patients. The variables included in the risk score construction were age\u0026thinsp;\u0026ge;\u0026thinsp;80 years with 4 points, chronic heart failure with 2 points, dementia with 6 points, respiratory rate\u0026thinsp;\u0026ge;\u0026thinsp;30 breaths/min with 2 points, and blood urea nitrogen\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/dL with 3 points. It was observed that the risk of one-year mortality increased by 24% (HR: 1.24; 95% CI: 1.19\u0026ndash;1.28) for each unit increase in the predictive model. The CAPSI risk score showed predictive accuracy of 0.76 in the derivation cohort and 0.77 in the validation cohort, results superior to those reported in our study.\u003c/p\u003e \u003cp\u003eHoste et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), in a multicenter cross-sectional study, demonstrated the importance of acute kidney injury and its directly proportional relationship with mortality, prolonged hospital stay, high costs to the system, chronic renal function impairment, and need for renal replacement therapy. In our cohort, deceased patients had higher levels of creatinine and blood urea nitrogen than those who survived at 12 months (27.2 vs. 22.5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and (1.4 vs. 1.3; p\u0026thinsp;=\u0026thinsp;0.370), respectively. This was because subjects with fatal outcomes had a higher morbidity burden, with presence of chronic kidney disease (8.7%), ICU requirement (17.6%), and vasopressor support (12.3%). It is noteworthy that the analyzed questionnaires (CURB-65, REA-ICU, ADROP, SCAP, PSI) include the variable of blood urea nitrogen, which can be used to optimize medical treatment through nephroprotective measures, such as fluid resuscitation, minimizing the use of nephrotoxic drugs or contrast media, and monitoring renal function after hospital discharge, thus promoting renal function and reducing mortality rates.\u003c/p\u003e \u003cp\u003eThe alteration of consciousness was more frequent in patients with fatal long-term outcomes, consistent with the findings of Banerdt et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), whose study associated central nervous system involvement with delirium, resulting in a higher risk of death from any cause in 711 hospitalized patients. In their study, mortality at 6 months was significantly higher for patients with delirium (44.6%; 95% CI: 39.3%-50.1%) compared to the control group (20%; 95% CI: 15.4%-25.2%) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, the presence of pleural effusion has also been associated with long-term mortality. Kookoolis et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) described in a study of 104 patients with pleural effusion on chest X-ray an increase in mortality from 15% at 30 days to 32% at one year. This study also found that other variables, such as age, disease severity, presence of malignancy, and diagnosis of chronic lung disease, were associated with increased risk of mortality at 12 months, similar to the findings presented in our cohort.\u003c/p\u003e \u003cp\u003ePatients with CAP are exposed to inflammation during the acute episode due to cytokine release and treatments received during the illness (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This persistent chronic inflammation is associated with increased severity during the acute episode of CAP, prolonged hospital stay, and need for invasive ventilatory therapy, contributing to increased long-term mortality in these patients. Additionally, advanced age, functional limitations, and the presence of cardiovascular, respiratory, and renal comorbidities increase the chronic inflammatory burden in patients with a history of CAP (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Since risk scores, such as SCAP, PSI, ADROP, PNEUMONIA shock, and SMART-COP, include clinical variables and laboratory findings that may be primarily related to acute inflammatory states, these scores may indirectly estimate hypercatabolism associated with different phases of the disease (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eAmong the limitations of our study is its observational nature, based on information obtained from clinical records, which may have data omissions (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). It is important to note that the personnel responsible for data collection received adequate training to ensure accuracy in the transcription of the information obtained. Additionally, our study was conducted in multiple healthcare centers, which may be considered a strength for generalizing the results. We also believe that the sample size included was sufficient to meet the objectives and perform relevant hypothesis tests (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). It is relevant to note that information on causes of death at 12 months could not be obtained, which would have provided important additional data for analysis. Therefore, we suggest that future studies address this issue for a more comprehensive understanding of the results. We consider it pertinent to conduct additional research that can corroborate our findings, as well as externally validate established risk scores to predict mortality at 12 months, such as those described by Uranga et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), which would help strengthen the evidence and improve accuracy in predicting long-term outcomes in CAP patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn predicting mortality in patients with CAP, it was observed that at 3 months, PSI, CHARLSON, and CRB-65 showed acceptable predictive performances. At 6 months, only PSI and CHARLSON maintained acceptable levels of accuracy. For the 12-month period, all evaluated scores exhibited very limited discriminatory ability, ranging from poor to almost negligible.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe study was conducted in accordance with the principles of the current Helsinki Declaration, as well as local, regional, and international regulations pertaining to clinical research, including Colombian Law on Biomedical Research. Ethical approval was obtained from the Medical Ethics Committee of the Cl\u0026iacute;nica Universidad de La Sabana (approval number 20220102). Prior to participating in the study, all participants provided written informed consent, and the confidentiality of their data was strictly maintained throughout the study.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by Universidad de la Sabana Grant MED-326-2022.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eETQ, ARB, GGG, IPR, DT, LG, JV, CA, EM, JA, EM, JA, NC, AR, VM, MG, MF, CS, and LFR contributed substantially to the study design, data analysis and interpretation, and manuscript writing. ETQ, ABG, GGG and DT had full access to all study data and takes responsibility for data integrity as well as for accuracy of the included data analysis and, especially. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors are most thankful for the Universidad de La Sabana.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGadsby NJ, Musher DM. The Microbial Etiology of Community-Acquired Pneumonia in Adults: from Classical Bacteriology to Host Transcriptional Signatures. Clin Microbiol Rev. 2022;35(4):e0001522.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin-Loeches I, Torres A, Nagavci B, Aliberti S, Antonelli M, Bassetti M, Bos LD, Chalmers JD, Derde L, de Waele J, Garnacho-Montero J, Kollef M, Luna CM, Menendez R, Niederman MS, Ponomarev D, Restrepo MI, Rigau D, Schultz MJ, Weiss E, Welte T, Wunderink R. ERS/ESICM/ESCMID/ALAT guidelines for the management of severe community-acquired pneumonia. Intensive Care Med. 2023;49(6):615\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePletz MW, Blasi F, Chalmers JD, Dela Cruz CS, Feldman C, Luna CM, Ramirez JA, Shindo Y, Stolz D, Torres A, Webb B, Welte T, Wunderink R, Aliberti S. International Perspective on the New 2019 American Thoracic Society/Infectious Diseases Society of America Community-Acquired Pneumonia Guideline: A Critical Appraisal by a Global Expert Panel. Chest. 2020;158(5):1912\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNair GB, Niederman MS. Updates on community acquired pneumonia management in the ICU. Pharmacol Ther. 2021;217:107663.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCilloniz C, Ferrer M, Peric\u0026agrave;s JM, Serrano L, M\u0026eacute;ndez R, Gabarr\u0026uacute;s A, Peroni HJ, Ruiz LA, Men\u0026eacute;ndez R, Zalacain R, Torres A. Validation of IDSA/ATS Guidelines for ICU Admission in Adults Over 80 Years Old With Community-Acquired Pneumonia. Arch Bronconeumol. 2023;59(1):19\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOdutayo A, Wong CX, Farkouh M, Altman DG, Hopewell S, Emdin CA, Hunn BH. AKI and Long-Term Risk for Cardiovascular Events and Mortality. J Am Soc Nephrol. 2017;28(1):377\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah S, Leonard AC, Harrison K, Meganathan K, Christianson AL, Thakar CV. Mortality and Recovery Associated with Kidney Failure due to Acute Kidney Injury. Clin J Am Soc Nephrol. 2020;15(7):995\u0026ndash;1006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerdt JK, Mateyo K, Wang L, Lindsell CJ, Riviello ED, Saylor D, Heimburger DC, Ely EW. Delirium as a predictor of mortality and disability among hospitalized patients in Zambia. PLoS ONE. 2021;16(2):e0246330.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKookoolis AS, Puchalski JT, Murphy TE, Araujo KL, Pisani MA. Mortality of Hospitalized Patients with Pleural Effusions. J Pulm Respir Med. 2014;4(3):184.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim WS, van der Eerden MM, Laing R, Boersma WG, Karalus N, Town GI, Lewis SA, Macfarlane JT. Defining community acquired pneumonia severity on presentation to hospital: an international derivation and validation study. Thorax. 2003;58(5):377\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFine MJ, Auble TE, Yealy DM, Hanusa BH, Weissfeld LA, Singer DE, Coley CM, Marrie TJ, Kapoor WN. A prediction rule to identify low-risk patients with community-acquired pneumonia. N Engl J Med. 1997;336(4):243\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarris PA, Taylor R, Thielke R, et al. Research electronic data capture (REDCap)-Ametadata-driven methodology and workflow process for providing translationalresearch informatics support. J Biomed Inform. 2009;42(2):377\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. New York, New York: John Wiley \u0026amp; Sons, Inc; 2000. p. 162.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnurag A, Preetam M. Validation of PSI/PORT, CURB-65 and SCAP scoring system in COVID-19 pneumonia for prediction of disease severity and 14-day mortality. Clin Respir J. 2021;15(5):467\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEspa\u0026ntilde;a PP, Capelastegui A, Quintana JM, Bilbao A, Diez R, Pascual S, Esteban C, Zalaca\u0026iacute;n R, Menendez R, Torres A. Validation and comparison of SCAP as a predictive score for identifying low-risk patients in community-acquired pneumonia. J Infect. 2010;60(2):106\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalcone M, Corrao S, Venditti M, Serra P, Licata G. Performance of PSI, CURB-65, and SCAP scores in predicting the outcome of patients with community-acquired and healthcare-associated pneumonia. Intern Emerg Med. 2011;6(5):431\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlan M, Grolimund E, Kutz A, Christ-Crain M, Thomann R, Falconnier C, Hoess C, Henzen C, Zimmerli W, Mueller B, Schuetz P. ProHOSP study group. Clinical risk scores and blood biomarkers as predictors of long-term outcome in patients with community-acquired pneumonia: a 6-year prospective follow-up study. J Intern Med. 2015;278(2):174\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUranga A, Quintana JM, Aguirre U, Artaraz A, Diez R, Pascual S, Ballaz A, Espa\u0026ntilde;a PP. Predicting 1-year mortality after hospitalization for community-acquired pneumonia. PLoS ONE. 2018;13(2):e0192750.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoste EA, Bagshaw SM, Bellomo R, Cely CM, Colman R, Cruz DN, Edipidis K, Forni LG, Gomersall CD, Govil D, Honor\u0026eacute; PM, Joannes-Boyau O, Joannidis M, Korhonen AM, Lavrentieva A, Mehta RL, Palevsky P, Roessler E, Ronco C, Uchino S, Vazquez JA, Vidal Andrade E, Webb S, Kellum JA. Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med. 2015;41(8):1411\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026oacute;voa P, Coelho L, Salluh J. When should we use corticosteroids in severe community-acquired pneumonia? Curr Opin Infect Dis. 2021;34(2):169\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorrales-Medina VF, Alvarez KN, Yende S. Hospitalization for pneumonia and risk of cardiovascular disease\u0026ndash;reply. JAMA. 2015;313(17):1753\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasaer MP, Mesotten D, Schetz MR. Bench-to-bedside review: metabolism and nutrition. Crit Care. 2008;12(4):222.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatkova A, Joskova V, Havel E, Kovarik M, Kucharova M, Zadak Z, Hronek M, Energy. Protein, Carbohydrate, and Lipid Intakes and Their Effects on Morbidity and Mortality in Critically Ill Adult Patients: A Systematic Review. Adv Nutr. 2017;8(4):624\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pneumonia, Risk score, Mortality, Observational study","lastPublishedDoi":"10.21203/rs.3.rs-3951887/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3951887/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRisk scores play a crucial role in assessing mortality risk among patients diagnosed with community-acquired pneumonia (CAP). Despite their practicality, there remains a dearth of comparative evidence regarding various risk scoring systems.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis multicenter retrospective study was conducted across two high-complexity medical institutions, focusing on individuals diagnosed with CAP. Receiver Operating Characteristic (ROC) curves were generated to assess the predictive performance of each analyzed risk score questionnaire in predicting survival or death at 3, 6, and 12 months post-diagnosis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOut of a total of 7454 potentially eligible patients, 3688 were included in the final analysis. Survival at 3, 6, and 12 months was 94.8%, 91.7% and 83.7%, respectively. At 3 months, PSI, CHARLSON, and CRB-65 scores showed ROC curves of 0.74 (95% CI: 0.71\u0026ndash;0.77), 0.71 (95% CI: 0.67\u0026ndash;0.74), and 0.7 (95% CI: 0.66\u0026ndash;0.74). At 6 months, PSI and CHARLSON scores showed performances of 0.74 (95% CI: 0.72\u0026ndash;0.77) and 0.72 (95% CI: 0.69\u0026ndash;0.74), respectively. At 12 months, all evaluated scores showed poor discriminatory capacity, including PSI, which decreased its capacity to poor with an ROC curve of 0.64 (95% CI: 0.61\u0026ndash;0.66).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn predicting mortality in patients with CAP, it was observed that at 3 months, PSI, CHARLSON, and CRB-65 showed acceptable predictive performances. At 6 months, only PSI and CHARLSON maintained acceptable levels of accuracy. For the 12-month period, all evaluated scores exhibited very limited discriminatory ability, ranging from poor to almost negligible.\u003c/p\u003e","manuscriptTitle":"Performance of Risk Scores in Predicting Mortality at 3, 6, and 12 Months in Patients Diagnosed with Community-Acquired Pneumonia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-23 19:05:56","doi":"10.21203/rs.3.rs-3951887/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-18T07:28:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-17T12:41:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"fe1193b2-db6c-4a7c-ad14-93a921e8724b","date":"2024-04-03T10:19:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-24T09:06:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15dc645a-f519-4a0c-bdb7-2914ba30e4c7","date":"2024-03-04T05:44:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-26T07:17:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-25T03:46:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-21T15:22:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-21T15:21:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2024-02-12T20:26:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"39fc748b-ced2-41a2-a30c-92fab86b9808","owner":[],"postedDate":"February 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-11T00:34:08+00:00","versionOfRecord":{"articleIdentity":"rs-3951887","link":"https://doi.org/10.1186/s12890-024-03121-7","journal":{"identity":"bmc-pulmonary-medicine","isVorOnly":false,"title":"BMC Pulmonary Medicine"},"publishedOn":"2024-07-10 00:34:08","publishedOnDateReadable":"July 10th, 2024"},"versionCreatedAt":"2024-02-23 19:05:56","video":"","vorDoi":"10.1186/s12890-024-03121-7","vorDoiUrl":"https://doi.org/10.1186/s12890-024-03121-7","workflowStages":[]},"version":"v1","identity":"rs-3951887","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3951887","identity":"rs-3951887","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}