Gereç ve Yöntem: Acil servise başvuran ve TKP tanısı alan 61 hastanın yaşamsal bulguları, semptomları, fizik muayene bulguları, özgeçmiş bilgileri, pnömoni ciddiyet skorları, laboratuvar ve radyolojik inceleme sonuçları kaydedildi. Hastaların başvuru anında serum kopeptin düzeyleri ölçüldü. İstatistiksel analizler SPSS 20.0 ve E-PICOS yazılımları ile gerçekleştirildi.

Bulgular: Çalışmaya dahil edilen 61 hastanın 32’si (%52.5) erkekti ve hastaların medyan yaşı 73 (31–91) olarak saptandı. Otuz günlük takip sürecinde 19 hasta hayatını kaybetti. Mortalite görülen hastalarda kopeptin düzeyleri, sağ kalanlara kıyasla anlamlı düzeyde yüksek bulundu (p<0.05). Hastalar pnömoni ciddiyet skorlarına göre gruplandırıldığında, biyobelirteç düzeyleri açısından gruplar arasında anlamlı fark saptanmadı. Biyobelirteçler ve pnömoni ciddiyet skorlarının birlikte değerlendirildiği ROC analizinde en yüksek tanısal doğruluk Kopeptin + CRP + Prokalsitonin kombinasyonunda elde edildi (AUC: 0.747).

Sonuç: Plazma kopeptin düzeyinin, toplum kökenli pnömoni tanısı alan hastalarda hastalık şiddetini ve mortalite riskini öngörmede kullanılabileceği sonucuna varılmıştır. Biyobelirteçlerin çoklu kullanımı ve/veya pnömoni ciddiyet skorlarıyla birlikte değerlendirilmesi, tek başına kullanımına göre hastalık prognozu ve mortalite tahmininde daha etkilidir.

Prediction of disease severity and prognosis in CAP is necessary for the correct and effective use of health system resources. Standardized scoring systems such as pneumonia severity index (PSI), CURB-65 (confusion-urea nitrogen-respiratory rate-blood pressure-65 years of age), national early warning score (NEWS), and national early warning score-lactate (NEWS-L) have been developed for the decisions of admission to the service or intensive care unit, early discharge, and antimicrobial therapy ². While these scoring systems have moderate sensitivity and specificity in determining the treatment method, they have not achieved sufficient success in determining mortality. All these limitations have led clinicians to find reliable, sensitive, and specific prognostic markers.

Adult patients (age > 18 years) who were admitted to the emergency department of the hospital and were diagnosed with CAP based on clinical and laboratory findings were included in the study. CAP diagnosis was made in the presence of at least two symptoms such as fever, cough, sputum, and shortness of breath that suggested an acute lower respiratory tract infection with newly developed pulmonary infiltration on chest X-ray. Patients who were hospitalized in the last 14 days, pregnant women, those with a history of immunosuppression, pulmonary embolism (PE), chronic obstructive pulmonary disease (COPD) attack, decompensated heart failure, pulmonary edema, and acute coronary syndrome were not included in the study. A control group of 25 people with a similar mean age and sex ratio was formed in accordance with the study exclusion criteria.

An informed consent form was obtained from the patients included in the study. The history of the patients was taken from the patients themselves or from their caregivers. They were examined, and imaging and laboratory tests were requested.

Demographic characteristics, history and physical examination, risk factors, vital signs and treatment information of the patients were obtained from the patient follow-up forms. For Copeptin, 5 cc of blood was collected from all patients in standard biochemistry tubes and centrifuged. The plasma was stored at -80 Cº. The stored samples were brought to room temperature and thawed one day before the study. Serum copeptin levels were studied in the biochemistry laboratory of our hospital using the Diasorin Etimax 3000 device and the Human Copeptin ELISA kit.

Hemogram, biochemistry, sedimentation, and ELISA test results were obtained from the hospital database. The CURB-65, PSI, NEWS and NEWS-L scores of the patients included in the study were calculated and recorded. A 30-day follow-up period was conducted after the diagnosis of CAP, and the 30-day mortality rate was recorded.

Statistical Analysis: The SPSS (Statistics Program for Social Scientists) 20 and E-PICOS programs were used for statistical analysis. Continuous data were expressed as mean ± standard Deviation. Categorical data were presented as a percentage (%). The Kolmogorov- Smirnov test was performed to check if the data showed normal distribution. Among the groups, the Mann-Whitney U test was performed to compare the data of two groups that did not fit the normal distribution, the Kruskal-Wallis test was performed for the comparison of the data of more than two groups, and Student's t-test was performed to compare normally distributed data. The Chi-Square test was performed to compare frequency data between the two groups. ROC (Receiver-Operating Characteristic) analyzes were performed to determine the correct cut-off point for the independent markers and to calculate the sensitivity and specificity values. Logistic regression analysis was performed to determine the independent marker affecting mortality, whereby p<0.05 was considered statistically significant.

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Table 1: Demographic characteristics and pneumonia risk scores

When the patients were classified according to PSI, there were 4 (6.6%) patients with a PSI score of 3, 18 (29.5%) patients with a PSI score of 4, and 39 patients (63.9%) with a PSI score of 5. There were 14 (23%) patients with a CURB65 score of 1, 28 (45.9%) with 2, 12 (19.7%) with 3, 6 (9.8%) with 4, and 1 (1.6%) with 5. The mean NEWS score of the patients was 7.1±2.88 and the mean NEWS-L score was 9.6±3.46. When the deceased and surviving patients were compared, PSI, CURB-65, NEWS and NEWS-L scores were found to be higher in the deceased group. PSI and NEWS-L were statistically significant in predicting mortality (p=0.045, p=0.021). The number of patients who died during the 30-day follow-up period was 19 (31.1%) (Table 1, Figure 1).

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Figure 1: Comparison of Pneumonia Risk Scores in patients who died due to CAP and those who survived

Demographic characteristics and copeptin levels of the control and patient groups are shown in Table 2. While there was no difference in age and sex between the groups (p>0.05), the mean copeptin level (77.4 pg/mL) of the patient group was significantly higher than the mean copeptin value (36.3 pg/mL) of the control group (p<0.001).

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Table 2: Demographic characteristics and copeptin levels of control and patient groups

There was a significant difference between the laboratory parameters of the deceased and surviving patients. Copeptin, CRP, PCT, and lactate values were found to be significantly higher in the deceased group [Copeptin (p=0.04), CRP (p=0.01), PCT (p=0.01), lactate (p=0.04)] (Figure 2). No correlation was found between Copeptin and other laboratory values in the correlation analysis (Table 3). A significant difference was found between the PRC, CRP, Copeptin, Lactate, PSI and NEWS-L values of the deceased and surviving patients. In the multivariate analysis, only CRP was found to be significant as an independent risk factor for mortality (Odds Ratio: 1.009, 95%Cl: 1.001-1.018, p=0.025) (Table 4).

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Table 3: Creatinine, lactate, ESH, CRP, PCT and copeptin correlation analysis

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Table 4: Univariate and multivariate logistic regression analysis for mortality

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Figure 2: Comparison of biomarkers in patients who died due to CAP and those who survived

AUC data and confidence intervals obtained from ROC analysis generated by biomarkers and pneumonia severity scores are shown in Table 5. As a result of the analysis, the highest AUC (0.730) values were obtained for CRP and procalcitonin. This was followed by copeptin (AUC: 0.663) and lactate (AUC: 0.657) (Figure 3).

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Figure 3: The cut-off values obtained from the ROC analysis for mortality prediction of CAP patients, and their specificity and sensitivity

AUC data and confidence intervals obtained from ROC analysis of models created with biomarkers and pneumonia severity scores are shown in Table 5. As a result of multiple analyzes, the highest AUC (0.747) value was obtained by adding procalcitonin and copeptin to CRP (Figure 3).

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Table 5: The cut-off values obtained from the ROC analysis for mortality prediction of CAP patients, and their specificity and sensitivity

In this study, the median serum copeptin level of 64.32 (43.08-316.65) pg/mL in patients with CAP was significantly higher than 27.73 (3.26-94.57) pg/mL in the control group. In addition, serum copeptin levels were found to be significantly higher in direct proportion to the severity of CAP in patients. In the study by Mohamed et al. aiming to determine the predictive value of copeptin as a severity indicator of community-acquired pneumonia, mean serum copeptin level was found to be significantly higher in pneumonic patients compared to the control group. A significant positive correlation was found between serum copeptin levels and the degree of respiratory distress ³. In the present study, copeptin levels were found to be high in the patient group with CAP, similar to what has been reported in the literature. Since pneumonia damages the parenchymal areas in the lungs where gas exchange takes place, the ventilation/perfusion balance is disturbed. This causes the release of AVP. Along with AVP, copeptin is also synthesized and the concentration of copeptin increases in the circulation.

In this study, the average copeptin value of the deceased patient group was found to be significantly higher than the average copeptin value of the surviving patients. The sensitivity was 68% and the specificity was 59% for copeptin. In the study by Stolz et al. on the relationship between COPD exacerbation and pneumonia, it was shown that copeptin levels increased as the severity of pneumonia increased when patients were classified according to PSI ⁸. In severe COPD, vasoconstriction caused by hypoxia leads to an increase in copeptin levels along with AVP on V1 receptors. Kolditz et al. studied 51 patients diagnosed with CAP and found that the 7-day survival rate was significantly lower in patients with high copeptin levels ⁹. In the study by Krüger et al., in which they examined the importance of copeptin regarding the severity and prognosis of pneumonia in patients with CAP, copeptin was found to be the strongest parameter in predicting 28-day mortality ¹⁰. In the present study, copeptin was found to be associated with pneumonia severity and mortality, similar to the literature. As the severity of pneumonia increases, conditions such as sepsis that adversely affect circulation occur. Circulatory system damage activates baroreceptors in the aortic arch and carotid sinus, and the decrease in arterial blood pressure causes an increase in serum copeptin levels.

In this study, as a result of multiple analyzes with other biomarkers, the highest value was obtained by adding PCT and copeptin to CRP. In line with these findings, copeptin was shown to be an important biomarker for predicting mortality in patients with CAP. However, in the multivariate logistic regression analysis performed to determine the factors that independently determine mortality in patients with CAP, it was seen that the only independent parameter was CRP. It was shown that one unit increase in CRP increases the risk of mortality by 0.9%. In the study by Masia et al. conducted with 173 patients with CAP, in the multivariate survival analysis including procalcitonin, CRP and PSI, the copeptin level was shown to be the only variable that was statistically significant in predicting mortality independently ¹¹. These results were likely due to an insufficient number of patients and a high number of comorbid diseases.

The mean PCT and CRP values of patients who died in the present study were significantly higher than those of patients who survived. Sensitivity for procalcitonin was 68%, specificity was 67%, whereas sensitivity for C-reactive Protein was 63%, and specificity was 61%. The result of multiple analyses showed that adding procalcitonin to PSI, and adding procalcitonin to CURB-65 significantly increased AUROC. Similarly, the addition of CRP to PSI and the addition of CRP to CURB-65 significantly increased AUROC. In previous studies, CRP and PCT were found to be predictors of mortality ^12-14,18. Min Woo Kim et al. evaluated the relationship between the serum biomarker and pneumonia risk scoring and mortality in a total of 115 patients, and created the best model of mortality prediction by adding CRP and PCT to PSI ¹². In the study by Menendez et al. on the use of biomarkers together with pneumonia risk scoring to predict mortality in patients with CAP, a total of 453 patients were included, and after multiple regression analysis that included PSI and CURB-65 from pneumonia risk scoring systems, only CRP was found to be an independent predictive biomarker ¹³. Studies investigating the effect of procalcitonin on prognosis in patients with diagnosis of CAP have shown that increased procalcitonin is associated with mortality ^14-17. In the present study, PCT and CRP were also shown to be important prognostic biomarkers for mortality.

In this study, it was also shown that lactate is an important biomarker in terms of prognosis in patients with CAP. Sensitivity was found to be 58% and specificity was found to be 83% for lactate. In the study by Sion et al., in which they examined the importance of adding a lactate measurement to NEWS scoring in determining the prognosis, PSI, CURB-65 and NEWS were compared with NEWS-L and the importance of lactate level in terms of prognosis was revealed ³. Gwak et al. investigated the relationship between lactate and mortality in patients hospitalized with the diagnosis of CAP, and the lactate levels of patients who died were found to be significantly higher. In the multivariate logistic regression analysis for hospitalization mortality using lactate, CRP and PSI laboratory variables, only lactate and CRP were found to be significant ¹⁸. In the study by Kaya AE et al., which compared the pneumonia severity scores in pneumonia cases, the NEWS-L score, including the lactate level, was found to be the most successful score in predicting mortality and the need for intensive care and hospitalization ¹⁹. In the present study, similar to the literature, the lactate level of the patients who died was found to be significantly higher than the patients who survived. In relation to this, the NEWS-L score was also found to be statistically significantly higher in patients who died.

As a result, according to the findings we obtained, plasma copeptin is a molecule that can be used in the diagnosis of community-acquired pneumonia and in estimating its severity and mortality. Multiple use of biomarkers and/or use with pneumonia risk scores is superior to their singular use in determining disease prognosis and mortality.

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