Abstract
Background
Community-acquired pneumonia (CAP) significantly affects elderly patients, leading to high morbidity and mortality rates. This study investigates the interplay between mental health, nutritional status, and comorbidities in determining the prognosis of elderly patients with CAP.
Methods
A retrospective cohort study was conducted with 455 patients aged 75 and older who were hospitalized for CAP. Clinical data, including demographic information, comorbidities, and laboratory results, were collected. The WHO-5 Well-Being Index (WHO-5), Mini Nutritional Assessment Short Form (MNA-SF), and Charlson Comorbidity Index (CCI) were utilized to assess mental health, nutritional status, and comorbidity burden. Statistical analyses included logistic regression, Kaplan–Meier survival analysis, and mediation analyses.
Results
The study found that the 28-day mortality rate was 9.67%, while the 90-day mortality rate reached 12.31%. Spearman's correlation analysis revealed significant positive correlations between the WHO-5 Well-Being Index and MNA-SF scores (r = 0.560) and albumin levels (r = 0.245), while negative correlations were observed with CCI (r = -0.202) and C-reactive protein levels (r = -0.242). Logistic regression analysis indicated that comorbidity, malnutrition, lower well-being, CAP severity, and mechanical ventilation are significant predictors of 28-day and 90-day mortality. Kaplan–Meier survival analysis demonstrated statistically significant differences in cumulative survival among various well-being groups. Multiple mediation analyses showed that mental well-being and nutritional status significantly mediated the association between CCI and 28-day and 90-day mortality.
Conclusion
This study emphasizes the critical roles of mental health, nutritional status, and comorbidities in the prognosis of elderly patients with CAP. Integrating these factors into clinical assessments may provide insights to inform management strategies, potentially improving patient outcomes and reducing mortality rates in this vulnerable population.
Keywords: Community-Acquired Pneumonia, Elderly patients, Nutritional status, Mental health, Comorbidities
Introduction
Community-acquired pneumonia (CAP) poses a significant health challenge for elderly patients, characterized by high morbidity and mortality rates, thereby constituting a global public health concern [1, 2]. As the global population continues to age, the prevalence of CAP among this demographic is rising, particularly among elderly individuals with multiple underlying health conditions, leading to poorer prognoses [3, 4]. Consequently, investigating the clinical characteristics and prognostic factors associated with CAP is essential for enhancing the management and treatment of elderly patients.
Recent studies emphasize that, beyond traditional physiological indicators, non-physiological factors—such as mental health, nutritional status, and comorbidities—significantly influence outcomes for elderly CAP patients [5–7]. However, there is a notable lack of research regarding the interactions among mental health, nutritional status, and comorbidities in the context of elderly CAP patients. This knowledge gap underscores the necessity for a comprehensive exploration of these interrelationships to provide a foundation for improving clinical interventions in the future.
Firstly, the growing recognition of the importance of mental health in elderly CAP patients is noteworthy. Research indicates a strong correlation between depressive symptoms and health outcomes; a 2019 study found that depression significantly elevates the mortality risk among hospitalized elderly CAP patients [8]. Furthermore, depressive symptoms can adversely affect treatment adherence and recovery, exacerbating the patient's condition [9]. The WHO-5 Well-Being Index, recognized for its simplicity and effectiveness, is widely used in clinical and research settings to evaluate emotional states and life satisfaction. Its five straightforward questions provide insights into an individual's overall sense of well-being, with higher scores typically indicating better mental health [10]. Recent studies suggest that improvements in mental health can enhance immune function and lower infection risk, highlighting the importance of mental health management in elderly CAP care [11].
Secondly, nutritional status is critical in determining the prognosis of elderly CAP patients. Malnutrition is prevalent in this population, particularly among hospitalized individuals, where malnourished patients exhibit significantly higher mortality rates than their well-nourished counterparts [12]. Additionally, research has linked malnutrition to worsened inflammatory responses and compromised immune function, increasing vulnerability to infections [13]. While existing studies have addressed the impact of nutritional status on the prognosis of elderly CAP patients, limited research has focused on the interactions among nutritional status, mental health, and comorbidities, providing a crucial foundation for the present study.
Moreover, the Charlson Comorbidity Index (CCI) is commonly employed to assess the burden of comorbidities, with findings indicating that higher CCI scores correlate with poorer prognoses. However, there is a scarcity of research investigating the interactions among mental health, nutritional status, and comorbidities, thus lacking a comprehensive theoretical framework [14–16]. Consequently, examining how these factors collectively influence the prognosis of elderly CAP patients can establish a robust basis for clinical interventions.
This study aims to explore the relationships among the WHO-5 Well-Being Index, mental health, nutritional status, and inflammatory responses, alongside their effects on the prognosis of elderly CAP patients. Through comparative analyses of different prognostic groups, this research aspires to provide valuable insights for the personalized treatment of elderly CAP patients, ultimately enhancing management strategies and potentially reducing mortality rates.
Methods
Study design and patients
This study employed a retrospective cohort design to investigate the clinical characteristics and prognostic factors of elderly patients diagnosed with community-acquired pneumonia (CAP), following the STROBE cohort reporting guidelines. Patients aged 75 years or older who were hospitalized for CAP in the Department of Geriatrics at the Second People's Hospital of Lianyungang from February 1, 2020, to February 1, 2024, were included.
Community-acquired pneumonia (CAP) was defined according to the American Thoracic Society (ATS) and Infectious Diseases Society of America (IDSA) guidelines as an acute infection of the pulmonary parenchyma acquired outside of a healthcare setting. Severe CAP (SCAP) was defined using the ATS/IDSA criteria, which include the presence of at least one major criterion (e.g., septic shock requiring vasopressors or respiratory failure requiring mechanical ventilation) or three or more minor criteria (e.g., confusion, uremia, respiratory rate ≥ 30 breaths/min, hypotension requiring aggressive fluid resuscitation) [17]. Severe community-acquired pneumonia (SCAP) was classified based on established clinical guidelines, which include the need for mechanical ventilation (invasive or non-invasive) or the use of vasopressors for septic shock. In this study, SCAP classification was primarily determined using available clinical data, including mechanical ventilation status and hemodynamic instability requiring vasopressors. In cases where data on vasopressor use or arterial blood gas analysis were unavailable, SCAP classification was supported by surrogate markers, such as clinical documentation of respiratory failure (e.g., need for oxygen supplementation beyond nasal cannula) or hemodynamic instability (e.g., systolic blood pressure < 90 mmHg despite fluid resuscitation). Additionally, mechanical ventilation status (invasive or non-invasive) was used as a primary criterion for SCAP classification.
Inclusion criteria
Patients aged 75 years or older.
Confirmed diagnosis of CAP according to the Infectious Diseases Society of America/American Thoracic Society (IDSA/ATS) guidelines for the diagnosis and treatment of adult community-acquired pneumonia [17].
Exclusion criteria
Patients diagnosed with hospital-acquired pneumonia.
Patients with immunocompromised states (e.g., due to HIV, chemotherapy, or long-term corticosteroid use).
Patients with terminal malignancies or other life-limiting illnesses.
Patients diagnosed with tuberculosis.
Patients with major depressive disorder or significant mental health disorders that could interfere with well-being assessments.
Patients exhibiting severe cognitive impairment or dementia as determined by clinical assessments.
Patients with incomplete medical records preventing accurate assessment of clinical characteristics and outcomes.
The study was approved by the Ethics Committee of the Second People's Hospital of Lianyungang (No. 2022K040) and adheres to the ethical principles outlined in the Declaration of Helsinki. Given the study's retrospective nature, no direct interventions were made regarding the clinical diagnosis or treatment of patients; the focus was solely on pre-existing clinical data. Therefore, the requirement for informed consent was waived by the Ethics Committee.
Data collection
Clinical data were extracted from electronic medical records, including demographic information (age, gender, educational level), comorbidities, and clinical outcomes at 28-day and 90-day follow-ups. Laboratory test results obtained within 24 h of admission were also recorded, including hemoglobin (Hb), platelet count (PLT), white blood cell count (WBC), serum albumin (Alb), blood urea nitrogen (BUN), blood glucose (Glu), and C-reactive protein (CRP). In addition to demographic information, comorbidities, and laboratory results, data on microbiological findings were collected from medical records. Microbiological findings included results from sputum cultures, blood cultures, and other diagnostic tests for pathogens. We also collected important data related to patient treatment, including the use of mechanical ventilation (non-invasive or invasive). Nutritional status, mental health, and overall well-being were assessed using standardized tools, as detailed below. Follow-ups for mortality due to CAP at 28 and 90 days post-discharge were conducted utilizing data from the hospital's medical records system and included a telephone follow-up strategy, reaching out to patients or their relatives to assess health status.
Handling missing data
To ensure the validity and reliability of the analysis, missing data were systematically addressed. Variables with more than 30% missing data were excluded from the study to minimize potential bias and inaccuracies in the results. For variables with 30% or less missing data, multiple imputation by chained equations (MICE) was employed. The imputation model included all relevant covariates, such as demographic information, clinical characteristics, and outcome variables, to ensure robust estimation of the missing values. The multiple imputation process was conducted using the "mice" package in R software (version 4.4.1), generating five imputed datasets (m = 5). The results from these datasets were pooled using Rubin's rules for subsequent statistical analyses. This approach was chosen to maximize data utilization while minimizing the impact of missing data on the study's findings.
Charlson comorbidity index assessment
The Charlson Comorbidity Index (CCI) was employed to evaluate the comorbidity burden in patients. The CCI comprises 14 specific diseases, each assigned a score from 1 to 6 based on its impact on survival [18]. The diseases included are heart disease, stroke, peripheral vascular disease, chronic obstructive pulmonary disease, diabetes, kidney disease, liver disease, cancer, and other relevant conditions. Patient medical histories were reviewed to identify the presence of these diseases, and the total CCI score was calculated according to the scoring criteria for each condition.
Nutritional assessment
The Mini Nutritional Assessment Short Form (MNA-SF) was used to evaluate patients' nutritional status [19]. The MNA-SF includes six questions assessing factors such as dietary intake, weight loss, mobility, and psychological stress. Each question is scored, yielding a total score of 0 to 14, with higher scores indicating better nutritional status. Patients were classified into three categories based on MNA-SF scores: well-nourished (12–14 points), at risk of malnutrition (8–11 points), and malnourished (0–7 points). This assessment was conducted within 48 h of admission.
WHO-5 well-being index assessment
The WHO-5 Well-Being Index is a self-reported questionnaire consisting of five items that assess mental well-being over the past two weeks [20]. In this study, the questionnaire was administered as a structured interview by trained healthcare professionals during the patients’ hospital admission. This approach ensured that patients with cognitive or physical impairments could participate and minimized potential biases associated with self-administration.
The WHO-5 Well-Being Index was utilized to assess patients' mental well-being [20]. It consists of five simple questions evaluating emotional state and life satisfaction over the past two weeks, with each question scored from 0 to 5, resulting in a total score ranging from 0 to 25. Higher scores indicate better mental well-being. Based on total WHO-5 scores, patients were categorized into three groups: Low Well-Being (0–12 points, indicating significant impairment and a higher likelihood of depressive symptoms), Moderate Well-Being (13–18 points, reflecting a moderate level of well-being), and High Well-Being (19–25 points, representing a high level of well-being). This assessment was conducted within 48 h of admission.
Statistical analysis
Statistical analyses were performed using IBM SPSS Statistics version 21 and R version 4.4.1. Descriptive statistics were computed for all variables, with continuous variables expressed as mean ± standard deviation (SD) and categorical variables presented as frequencies and percentages. Comparisons between groups were conducted using independent t-tests for continuous variables and chi-square tests for categorical variables. For non-normally distributed data, the non-parametric Mann–Whitney U test was utilized, with results expressed as median (interquartile range). Normality and homogeneity of variances were assessed using the Shapiro–Wilk test and Levene's test, respectively. The chi-square test examined relationships between categorical variables, facilitating the evaluation of associations in frequency data. Spearman's correlation analysis assessed relationships among MNA-SF scores, WHO-5 Well-Being Index scores, CCI, and CRP. This non-parametric method is suitable for evaluating the strength and direction of associations between ranked variables without the assumption of normal distribution. All statistical tests were two-tailed, and a p-value of < 0.05 was deemed statistically significant.
Binary logistic regression analyses identified significant predictors of 28-day and 90-day mortality, chosen for its effectiveness in modeling binary outcome variables with multiple predictor variables. Odds ratios (ORs) for each predictor were provided with 95% confidence intervals (CIs) to indicate the precision and reliability of the estimates. Three models were constructed: Model 1 adjusted for age and gender; Model 2 adjusted for educational level, severity of CAP, demographic characteristics, and clinical characteristics; Model 3 further adjusted for clinically relevant variables, including WBC, BUN, diabetes, CKD stage 3 or above, and COPD, regardless of their univariate statistical significance. These adjustments aimed to mitigate potential confounding effects, ensuring that identified relationships accurately reflected specific associations being studied. The model fit was assessed using: (1) Nagelkerke R2, which provides an estimate of the proportion of variance explained by the model, where higher values indicate better explanatory power; (2) Hosmer–Lemeshow Test, which evaluates the goodness-of-fit between observed and predicted values, with p > 0.05 indicating acceptable fit; and (3) p-value (Model), which tests the overall significance of the regression model by assessing whether the predictors collectively explain a significant portion of the variability in the outcome variables (p < 0.05 indicates significance).
Survival analysis was conducted using the Kaplan–Meier method to assess the association between psychological state and survival outcomes among elderly patients with CAP. The survival curves were compared using the log-rank test to determine statistically significant differences between the survival distributions of groups categorized by psychological status.
The mediating effects of mental well-being (WHO-5) and nutritional status (MNA-SF) on the relationship between comorbidity (CCI) and 28-day and 90-day mortality were analyzed using the Bootstrap approach. Bootstrap is a non-parametric resampling method that generates multiple samples (5000 iterations in this study) from the original dataset to estimate the sampling distribution of the indirect effects [21]. This method is particularly suitable for small sample sizes and does not require the assumption of normality. The indirect effects were calculated for each resampled dataset, and 95% confidence intervals (CIs) were generated. A mediating effect was considered statistically significant if the 95% CI did not include zero. This approach provides robust estimates of mediation effects and allows for the simultaneous evaluation of multiple mediators.
Results
Patient characteristics
This study ultimately included 455 elderly patients diagnosed with community-acquired pneumonia (CAP), selected based on established inclusion and exclusion criteria (Fig. 1). The mean age of participants was 84.40 ± 5.14 years, with 53.80% (n = 245) being male. At the 28-day follow-up, 44 patients had died, resulting in a 28-day mortality rate of 9.67%. An additional 12 patients had died by the 90-day follow-up, leading to a 90-day mortality rate of 12.31%. Table 1 presents participant characteristics stratified by different prognostic groups. Both the 28-day and 90-day assessments indicated that age, Charlson Comorbidity Index (CCI), and the proportion of patients with severe community-acquired pneumonia (SCAP) and mechanical ventilation were significantly higher in the deceased group compared to survivors (P < 0.001).
Fig. 1.
Flow diagram displaying the progress of all participants through the study
Table 1.
Comparison between different prognostic groups
| Variables | Total (n = 455) | 28-day Prognosis | P | 90-day Prognosis | P | ||
|---|---|---|---|---|---|---|---|
| Survival (n = 411) | Mortality (n = 44) | Survival (n = 399) | Mortality (n = 56) | ||||
| Gender | 0.153 | 0.107 | |||||
| Male, n (%) | 245(53.8) | 226(55.0) | 19(43.2) | 221(55.4) | 24(42.9) | ||
| Female, n (%) | 210(46.2) | 185(45.0) | 25(56.8) | 178(44.6) | 32(57.1) | ||
| Age, mean (SD) | 84.40(5.14) | 84.00(4.94) | 88.14(5.53) | < 0.001 | 83.90(4.92) | 87.96(5.27) | < 0.001 |
| Educational levels | 0.408 | 0.190 | |||||
| Primary school or below, n (%) | 221(48.6) | 197(47.9) | 24(54.5) | 191(47.9) | 30(53.6) | ||
| Secondary school, n (%) | 176(38.7) | 163(39.7) | 13(29.5) | 160(40.1) | 16(28.6) | ||
| Above secondary school, n (%) | 58(12.7) | 51(12.4) | 7(15.9) | 48(12.0) | 10(17.9) | ||
| Pneumonia | < 0.001 | < 0.001 | |||||
| CAP, n (%) | 372(81.8) | 354(86.1) | 18(40.9) | 348(87.2) | 24(42.9) | ||
| SCAP, n (%) | 83(18.2) | 57(13.9) | 26(59.1) | 51(12.8) | 32(57.1) | ||
| Etiology | 0.148 | 0.276 | |||||
| Influenza virus, n (%) | 46 (10.1) | 41 (10.0) | 5(11.4) | 40(10.0) | 6(10.7) | ||
| COVID-19, n (%) | 36 (7.9) | 31 (7.5) | 5 (11.4) | 29(7.3) | 7(12.5) | ||
| Bacterial pneumonia, n (%) | 188 (41.3) | 173 (42.1) | 15 (34.1) | 168(42.1) | 20(35.7) | ||
| Mixed infections, n (%) | 39 (8.6) | 30 (7.3) | 9(20.5) | 29(7.3) | 10(17.9) | ||
| Unidentified, n (%) | 146 (32.1) | 136 (33.1) | 10 (22.7) | 133(33.3) | 13(23.2) | ||
| Mechanical Ventilation | < 0.001 | < 0.001 | |||||
| Any mechanical ventilation, n (%) | 79(17.4) | 50(12.2) | 29(65.9) | 45(11.3) | 34(60.7) | ||
| Noninvasive ventilation, n (%) | 40 (8.8) | 30 (7.3) | 10 (22.7) | 28(7.0) | 12(21.4) | ||
| Invasive ventilation, n (%) | 39(8.6) | 20(4.9) | 19(43.2) | 17(4.3) | 22(39.3) | ||
| Nutritional status | < 0.001 | < 0.001 | |||||
| MNA-SF ≤ 7 points, n (%) | 123(27.0) | 88(21.4) | 35(79.5) | 79(19.8) | 44(78.6) | ||
| MNA-SF8-11 points, n (%) | 212(46.6) | 205(49.9) | 7(15.9) | 203(50.9) | 9(16.1) | ||
| MNA-SF12-14points, n (%) | 120(26.4) | 118(28.7) | 2(4.5) | 117(29.3) | 3(5.4) | ||
| WHO-5 Well-Being Index | < 0.001 | < 0.001 | |||||
| Low well-being0-9, n (%) | 57(12.5) | 33(8.0) | 24(54.5) | 26(6.5) | 31(55.4) | ||
| Moderate well-being10-19, n (%) | 208(45.7) | 191(46.5) | 17(38.6) | 187(46.9) | 21(37.5) | ||
| High well-being20-25, n (%) | 190(41.8) | 187(45.5) | 3(6.8) | 186(46.6) | 4(7.1) | ||
| CCI,mean (SD) | 2.80(1.30) | 2.67(1.26) | 3.96(1.06) | < 0.001 | 2.63(1.25) | 3.95(1.02) | < 0.001 |
| Smoking | 0.184 | 0.372 | |||||
| NO, n (%) | 294(64.6) | 270(65.7) | 24(54.5) | 261(65.4) | 33(58.9) | ||
| Yes, n (%) | 161(35.4) | 141(34.3) | 20(45.5) | 138(34.6) | 23(41.1) | ||
| Underlying diseases | |||||||
| Hypertension, n (%) | 159(34.9) | 145(35.3) | 14(31.8) | 0.740 | 142(35.6) | 17(30.4) | 0.550 |
| Diabetes, n (%) | 165(36.3) | 147(35.8) | 18(40.9) | 0.513 | 142(35.6) | 23(41.1) | 0.459 |
| Chronic kidney disease ≥ stage 3, n (%) | 88(19.3) | 77(18.7) | 11(25.0) | 0.318 | 77(19.3) | 12(21.4) | 0.912 |
| Cerebrovascular disease, n (%) | 110(24.2) | 97(23.6) | 13(29.5) | 0.361 | 94(23.6) | 16(28.6) | 0.408 |
| Chronic heart disease, n (%) | 161(35.4) | 143(34.8) | 18(40.9) | 0.413 | 140(35.1) | 21(37.5) | 0.766 |
| COPD, n (%) | 104(22.9) | 91(22.1) | 13(29.5) | 0.262 | 89(22.3) | 15(26.8) | 0.497 |
| HR,beats/min, mean (SD) | 79.93(9.58) | 79.73(9.68) | 81.75(8.48) | 0.184 | 79.67(9.70) | 81.77(8.54) | 0.125 |
| SBP,mmHg, mean (SD) | 137.52(16.90) | 137.79(16.74) | 135.02(18.35) | 0.303 | 137.76(16.54) | 135.82(19.35) | 0.422 |
| DBP,mmHg, mean (SD) | 70.38(10.02) | 70.13(9.64) | 72.71(12.96) | 0.105 | 70.12(9.63) | 72.20(12.39) | 0.147 |
| WBC, × 109/L, mean (SD) | 9.90(2.13) | 9.87(2.11) | 10.18(2.35) | 0.361 | 9.88(2.12) | 10.09(2.23) | 0.486 |
| HB,g/L, mean (SD) | 126.00(17.95) | 126.24(17.86) | 123.80(18.82) | 0.392 | 126.39(17.94) | 123.20(17.90) | 0.212 |
| PLT, × 109/L,mean (SD) | 207.51(66.78) | 206.78(67.50) | 214.30(59.95) | 0.479 | 207.80(68.00) | 205.45(57.91) | 0.805 |
| ALB,g/dL, mean (SD) | 3.39(0.29) | 3.41(0.29) | 3.20(0.21) | < 0.001 | 3.41(0.29) | 3.23(0.24) | < 0.001 |
| BUN,mg/dL,mean (SD) | 25.43(11.78) | 25.26(11.52) | 27.08(13.98) | 0.331 | 25.21(11.61) | 27.01(12.88) | 0.286 |
| Glu,mg/dL,mean (SD) | 138.65(46.63) | 138.64(47.21) | 138.77(41.28) | 0.986 | 139.06(47.51) | 135.78(40.07) | 0.623 |
| CRP,mg/L,median (IQR) | 76.20(44.40,117.30) | 73.25(42.50,117.80) | 109.00(66.00,133.25) | 0.002 | 72.70(41.40,116.90) | 98.25(64.18,129.05) | 0.002 |
Abbreviations: SCAP Severe community-acquired pneumonia, MNA-SF Mini Nutritional Assessment Short-Form, CCI Charlson Comorbidity Index, COPD Chronic Obstructive Pulmonary Disease, HR Heart rate, SBP Systolic blood pressure, DBP Diastolic blood pressure, WBC White blood cell, PLT Platelet, HB Hemoglobin, ALB Albumin, BUN Blood urea nitrogen, Glu Glucose, CRP C-reactive protein, SD Standard deviation, IQR Interquartile range
Nutritional status, as assessed using the Mini Nutritional Assessment Short Form (MNA-SF), showed that the proportion of malnourished patients (MNA-SF ≤ 7 points) was significantly greater in the deceased group at both follow-up points (P < 0.001). Moreover, well-being, evaluated via the WHO-5 Well-Being Index, revealed a significantly higher proportion of patients with low well-being in the deceased group at both the 28- and 90-day follow-ups (P < 0.001). Albumin levels were significantly lower in the deceased group at both time points (P < 0.001), whereas C-reactive protein (CRP) levels were significantly higher in the deceased group compared to survivors (P < 0.05).
Comparison of GDS, WHO-5, MNA-SF, and CCI among prognostic groups
After 28 days, the CCI was significantly higher in the deceased group (3.96 ± 1.06) compared to survivors (2.67 ± 1.26, P < 0.001). Conversely, MNA-SF scores were significantly lower in the deceased group (6.80 ± 1.98) than in survivors (9.91 ± 2.15, P < 0.001), and the WHO-5 Well-Being Index scores were also lower in the deceased group (9.30 ± 4.23) compared to survivors (18.25 ± 5.00, P < 0.001). For the 90-day prognosis, CCI scores in the deceased group remained significantly higher (10.14 ± 2.65 vs. 5.34 ± 2.51, P < 0.001). MNA-SF scores and WHO-5 Well-Being Index scores were also lower in the deceased group (MNA-SF: 6.80 ± 2.02 vs. 10.00 ± 2.08, P < 0.001; WHO-5: 9.57 ± 4.73 vs. 18.48 ± 4.78, P < 0.001).
Correlations among the variables
Spearman's correlation analysis revealed a positive correlation between the WHO-5 Well-Being Index and MNA-SF scores (r = 0.560) as well as albumin levels (r = 0.245), both statistically significant (P < 0.001). In contrast, the WHO-5 Well-Being Index exhibited negative correlations with CCI (r = −0.202), CRP levels (r = −0.242), and age (r = −0.181), all significant at P < 0.001.
Binary logistic regression analysis
The binary logistic regression analysis identified significant predictors of 28-day and 90-day mortality across three models (Table 2). Model 1, adjusted for age and gender as baseline demographic factors, showed that higher comorbidity (CCI), poorer nutritional status (MNA-SF), and lower mental well-being (WHO-5) were all significantly associated with worse outcomes. Increasing age was also identified as a significant risk factor in both models. Model 2, which further adjusted for educational levels, etiology, CAP severity (SCAP), mechanical ventilation and other statistically significant variables, confirmed the importance of comorbidity, nutritional status, mental well-being, CAP severity, and mechanical ventilation as key predictors of mortality risk. Model 3, incorporating additional clinically relevant variables (e.g., WBC, BUN, diabetes, CKD stage 3 or above, and COPD) regardless of their statistical significance in univariate analysis, reaffirmed that comorbidity, nutritional status, mental well-being, CAP severity, and mechanical ventilation remained significant predictors of both 28-day and 90-day mortality.
Table 2.
Regression analyses across different models
| 28-day Prognosis | Hosmer–Lemeshow Test p-value | Nagelkerke R2 | p-value (Model) | Variables | β | P-value | OR | 95%CI Lower | 95%CI Upper |
| Model 1 | 0.518 | 0.368 | < 0.001 | CCI | 0.573 | 0.003 | 1.774 | 1.208 | 2.605 |
| MNA-SF Score | −0.269 | 0.032 | 0.764 | 0.598 | 0.976 | ||||
| WHO-5 | −0.277 | < 0.001 | 0.758 | 0.684 | 0.841 | ||||
| Age | 0.095 | 0.017 | 1.100 | 1.017 | 1.189 | ||||
| Model 2 | 0.879 | 0.445 | < 0.001 | CCI | 0.530 | 0.007 | 1.698 | 1.154 | 2.499 |
| MNA-SF Score | −0.260 | 0.048 | 0.771 | 0.596 | 0.998 | ||||
| WHO-5 | −0.258 | < 0.001 | 0.772 | 0.694 | 0.859 | ||||
| SCAP | 1.161 | 0.009 | 3.193 | 1.334 | 7.644 | ||||
| Age | 0.082 | 0.047 | 1.085 | 1.001 | 1.176 | ||||
| Mechanical ventilation | 1.445 | 0.001 | 4.236 | 1.737 | 10.333 | ||||
| Model 3 | 0.731 | 0.485 | < 0.001 | CCI | 0.554 | 0.007 | 1.740 | 1.161 | 2.609 |
| MNA-SF Score | −0.287 | 0.042 | 0.751 | 0.569 | 0.990 | ||||
| WHO-5 | −0.257 | < 0.001 | 0.773 | 0.695 | 0.861 | ||||
| SCAP | 1.039 | 0.023 | 2.827 | 1.155 | 6.922 | ||||
| Age | 0.092 | 0.035 | 1.096 | 1.006 | 1.194 | ||||
| Mechanical ventilation | 1.356 | < 0.001 | 3.880 | 1.823 | 8.361 | ||||
| 90-day Prognosis | Hosmer–Lemeshow Test p-value | Nagelkerke R2 | p-value (Model) | Variables | β | P-value | OR | 95%CI Lower | 95%CI Upper |
| Model 1 | 0.626 | 0.398 | < 0.001 | CCI | 0.693 | < 0.001 | 2.356 | 1.613 | 3.442 |
| MNA-SF Score | −0.346 | 0.004 | 0.708 | 0.559 | 0.895 | ||||
| WHO-5 | −0.296 | < 0.001 | 0.743 | 0.671 | 0.823 | ||||
| Age | 0.101 | 0.009 | 1.124 | 1.025 | 1.194 | ||||
| Model 2 | 0.788 | 0.465 | < 0.001 | CCI | 0.651 | 0.001 | 1.918 | 1.318 | 2.793 |
| MNA-SF Score | −0.342 | 0.007 | 0.711 | 0.553 | 0.913 | ||||
| WHO-5 | −0.281 | < 0.001 | 0.755 | 0.679 | 0.839 | ||||
| SCAP | 1.375 | 0.002 | 3.956 | 1.661 | 9.422 | ||||
| Age | 0.092 | 0.023 | 1.096 | 1.013 | 1.187 | ||||
| Mechanical ventilation | 1.167 | 0.005 | 2.906 | 1.583 | 6.101 | ||||
| Model 3 | 0.672 | 0.502 | < 0.001 | CCI | 0.676 | 0.001 | 1.967 | 1.323 | 2.924 |
| MNA-SF Score | −0.407 | 0.004 | 0.665 | 0.503 | 0.879 | ||||
| WHO-5 | −0.287 | < 0.001 | 0.751 | 0.674 | 0.835 | ||||
| SCAP | 1.477 | 0.002 | 4.381 | 1.726 | 11.122 | ||||
| Age | 0.108 | 0.012 | 1.114 | 1.024 | 1.212 | ||||
| Mechanical ventilation | 1.399 | < 0.001 | 4.052 | 1.849 | 8.882 |
Model 1: adjusted for age and gender
Model 2: further adjusted for educational levels, etiology, severity of community-acquired pneumonia, mechanical ventilation and other statistically significant variables from Table 1
Model 3: further adjusted for clinically relevant variables, including WBC, BUN, diabetes, CKD stage 3 or above, and COPD, regardless of their univariate statistical significance
For 28-day mortality, all three models demonstrated good explanatory power and adequate fit. The final model (Model 3) explained 48.5% of the variance (Nagelkerke R2 = 0.485) and demonstrated good fit with observed data (Hosmer–Lemeshow Test p = 0.731). Similarly, for 90-day mortality, the addition of SCAP, age, and mechanical ventilation in Model 3 further improved the model's predictive power, explaining 50.2% of the variance (Nagelkerke R2 = 0.520) and showing adequate fit (Hosmer–Lemeshow Test p = 0.672). All three models for both mortality outcomes were statistically significant (p-value < 0.001).
Kaplan–Meier survival analysis
Among the 455 older CAP patients, the 28-day prognosis showed that the high well-being group (WHO 20–25 points) experienced 3 deaths out of 190 patients, with a median survival time of 27.82 ± 0.12 days and a cumulative survival rate of 98.4%. In the mild well-being group (WHO 10–19 points), 17 deaths occurred among 208 patients, resulting in a median survival time of 27.11 ± 0.23 days and a cumulative survival rate of 91.8%. In the low well-being group (WHO 0–9 points), there were 24 deaths out of 57 patients, leading to a median survival time of 22.04 ± 1.03 days and a cumulative survival rate of 57.9%. Statistically significant differences in cumulative survival were noted among these groups (Log-rank test: χ2 = 100.826, P < 0.001).
For the 90-day prognosis, the high well-being group (WHO 20–25 points) had 4 deaths out of 190 patients, with a median survival time of 88.62 ± 0.70 days and a cumulative survival rate of 97.9%. In the moderate well-being group (WHO 10–19 points), there were 21 deaths among 208 patients, resulting in a median survival time of 83.61 ± 1.40 days and a cumulative survival rate of 89.9%. In the low well-being group (WHO 0–9 points), there were 31 deaths out of 57 patients, yielding a median survival time of 54.60 ± 4.77 days and a cumulative survival rate of 45.6%. Statistically significant differences in cumulative survival were identified among these groups (Log-rank test: χ2 = 43.139, P < 0.001). Figure 2A and B depicts the Kaplan–Meier survival curves for different well-being groups.
Fig. 2.
Kaplan–Meier Survival Curves by Well-Being (A, B) and Nutritional Status (C, D)
The Kaplan–Meier survival analysis also revealed significant differences in survival probabilities based on nutritional status, as assessed by the Mini Nutritional Assessment-Short Form (MNA-SF). For the 28-day prognosis, the well-nourished group (MNA-SF 12–14) had a cumulative survival rate of 98.3%, with 2 deaths out of 120 patients. The at-risk group (MNA-SF 8–11) showed a survival rate of 96.7%, with 7 deaths among 212 patients. The malnourished group (MNA-SF 0–7) had a significantly lower survival rate of 72.4%, with 34 deaths out of 123 patients (Log-rank test: χ2 = 83.214, P < 0.001). For the 90-day prognosis, the well-nourished group (MNA-SF 12–14) had a survival rate of 97.5%, with 3 deaths. The at-risk group (MNA-SF 8–11) had a survival rate of 94.3%, with 12 deaths. The malnourished group (MNA-SF 0–7) showed a survival rate of 53.7%, with 44 deaths (Log-rank test: χ2 = 92.547, P < 0.001). Figure 2C and D illustrates the Kaplan–Meier survival curves for different nutritional status groups.
Multiple mediational analyses
In the analysis of 28-day mortality, the total indirect effect was estimated at 0.379 (95% CI: 0.211 to 0.667), accounting for 34.80% of the total effect. This indicates that mental well-being serves as a protective factor against 28-day mortality, suggesting a partial mediation effect. The mediating effects of the pathways "CCI → WHO-5 → 28-day mortality," "CCI → MNA-SF → 28-day mortality," and "CCI → WHO-5 → MNA-SF → 28-day mortality" were 0.230 (95% CI: 0.115 to 0.401), 0.089 (95% CI: 0.014 to 0.231), and 0.060 (95% CI: 0.009 to 0.162), respectively.
For 90-day mortality, the total indirect effect was 0.423 (95% CI: 0.243 to 0.743), which constituted 34.20% of the overall effect. The mediating effects for the pathways "CCI → WHO-5 → 90-day mortality," "CCI → MNA-SF → 90-day mortality," and "CCI → WHO-5 → MNA-SF → 90-day mortality" were 0.243 (95% CI: 0.120 to 0.426), 0.108 (95% CI: 0.038 to 0.249), and 0.073 (95% CI: 0.023 to 0.175), respectively. The Bootstrap 95% CI did not include zero, indicating a statistically significant mediation effect.
Discussion
This study provides novel insights into the clinical characteristics and prognostic factors associated with community-acquired pneumonia (CAP) in elderly patients. Specifically, we highlight the critical role of mental health, as measured by the WHO-5 Well-Being Index, in predicting 28-day and 90-day mortality. While previous research has explored the impact of comorbidities and nutritional status on CAP outcomes, our study uniquely demonstrates how mental health and nutritional status mediate the relationship between comorbidities and mortality. These findings underscore the importance of integrating mental health and nutritional assessments into routine care for elderly CAP patients, offering a more holistic approach to improving outcomes in this vulnerable population. Notably, the deceased group exhibited significantly higher Charlson Comorbidity Index (CCI) scores, lower Mini Nutritional Assessment Short Form (MNA-SF) scores, and poorer WHO-5 Well-Being Index scores compared to survivors. After adjusting for additional clinically relevant variables, including WBC, BUN, diabetes, CKD stage 3 or above, and COPD, the results of the logistic regression analysis remained consistent, demonstrating that comorbidities, nutritional status, and mental well-being are independently associated with 28-day and 90-day mortality in elderly CAP patients. This adjustment enhances the robustness and clinical relevance of our findings, as these additional variables, though not significant individually, are well-established factors in the prognosis of CAP.
Mental health and prognosis
The significant association between the WHO-5 Well-Being Index and mortality outcomes reinforces the essential role of mental health in predicting outcomes for elderly patients with CAP. The WHO-5 Well-Being Index evaluates general emotional well-being and life satisfaction rather than serving as a diagnostic tool for clinical depression. While depressive symptoms and well-being are related, they represent distinct constructs. For example, the WHO-5 focuses on positive aspects of mental health, whereas depression scales such as the Geriatric Depression Scale (GDS) assess negative symptoms. This distinction is critical in interpreting our findings. Previous studies, such as Fung et al. (2022), have validated the WHO-5 as a reliable tool for assessing mental well-being in elderly populations, particularly in clinical and community settings [20]. However, further research is needed to explore the nuanced relationship between well-being, depressive symptoms, and clinical outcomes in elderly CAP patients.
Our findings are consistent with those of Wei et al., who demonstrated that depressive symptoms can substantially increase mortality risk in hospitalized elderly patients, underscoring the necessity of incorporating mental health assessments into routine care for this demographic [22]. This association indicates that mental well-being is not merely a quality-of-life indicator but a vital component of clinical prognosis. Further supporting our results, Kline et al. reported that depression negatively impacts treatment adherence and rehabilitation, exacerbating health declines in elderly patients [23]. Mechanistically, mental health, particularly stress and depression, has been shown to suppress immune function through dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis, leading to increased susceptibility to infections and impaired recovery [24, 25]. Chronic stress and depressive symptoms can also elevate systemic inflammation, as evidenced by higher levels of C-reactive protein (CRP), which has been linked to worse outcomes in CAP patients [26]. Furthermore, poor mental health may reduce treatment adherence and delay rehabilitation, compounding the negative effects on prognosis.
These findings highlight the importance of integrating mental health assessments into the holistic management of elderly CAP patients. By addressing mental well-being alongside other clinical factors, healthcare providers can improve treatment adherence, enhance recovery, and ultimately reduce mortality rates in this vulnerable population.
Nutritional status and comorbidity burden
The prevalence of malnutrition among elderly patients with CAP, as indicated by lower MNA-SF scores in the deceased group, aligns with previous research by Soysal et al., which highlighted that malnutrition is prevalent and correlates with increased mortality rates among hospitalized elderly patients [27]. Furthermore, Schuetz et al. demonstrated that malnutrition exacerbates inflammatory responses and diminishes immune function, heightening susceptibility to infections [28]. Our findings support these conclusions, emphasizing the necessity of nutritional assessments in managing elderly CAP patients.
Vitamin deficiencies, particularly in antioxidants such as vitamin C and fat-soluble vitamins A, D, and E, have been associated with heightened inflammatory responses and worse clinical outcomes in CAP patients. These deficiencies may exacerbate immune dysfunction and inflammation, further complicating recovery in elderly patients. Future studies should explore the role of specific micronutrient deficiencies in the prognosis of CAP to inform targeted nutritional interventions [12, 13].
The use of the CCI in our study to evaluate comorbidity burden is consistent with literature highlighting the impact of comorbidities on survival rates in elderly CAP patients. Hespanhol et al. found that higher CCI scores correlate with poorer prognoses, reinforcing our results that indicate a significant correlation between increased CCI scores and mortality [29]. Luna et al. (2016) also reported that comorbidities significantly influence pneumonia prognosis in older adults [30].
The key role of mediation
Our study employed multiple mediation analyses to explore how mental well-being and nutritional status mediate the relationship between comorbidity burden and mortality outcomes. The findings suggest that mental well-being and nutritional status may mediate the relationship between comorbidity burdens, as measured by the Charlson Comorbidity Index (CCI), and both 28-day and 90-day mortality. However, these results are preliminary and derived from a retrospective analysis, which is subject to potential biases such as unmeasured confounding and selection bias. Prospective cohort studies are essential to validate these findings and further elucidate the mechanisms driving these associations. The use of the Bootstrap approach allowed for robust estimation of these mediation effects, even in the presence of small sample sizes and non-normal data distributions. By generating 5000 resamples, we were able to calculate precise confidence intervals for the indirect effects, confirming the statistical significance of the mediating roles of mental well-being and nutritional status. This method provides a reliable framework for understanding complex causal pathways in clinical research [21].
These findings highlight the potential importance of addressing both mental health and nutritional status in clinical practice, as these factors may influence the effects of comorbidities on mortality outcomes. These findings highlight the potential importance of addressing mental health and nutritional status in elderly patients with community-acquired pneumonia. While these factors appear to have significant associations with mortality, their direct impact on patient outcomes remains to be proven. Prospective interventional studies are required to determine whether targeted interventions designed to improve mental well-being and nutritional status can lead to better clinical outcomes. Until then, these results should be interpreted as hypotheses rather than conclusive evidence.
Mechanistically, mental health, particularly stress and depression, has been shown to suppress immune function through dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis, leading to increased susceptibility to infections and impaired recovery [24]. Chronic stress and depressive symptoms can also elevate systemic inflammation, as evidenced by higher levels of C-reactive protein (CRP), which has been linked to worse outcomes in CAP patients. Furthermore, poor mental health may reduce treatment adherence and delay rehabilitation, compounding the negative effects on prognosis.
Nutritional status plays a critical role in modulating immune function and inflammatory responses. Malnutrition, characterized by deficiencies in essential nutrients such as proteins, vitamins, and antioxidants, can impair the production of immune cells and cytokines, weakening the body's ability to combat infections. Additionally, malnutrition exacerbates systemic inflammation, creating a vicious cycle that further compromises immune function [28].
Comorbidities, as measured by the Charlson Comorbidity Index (CCI), contribute to a chronic pro-inflammatory state and immune dysfunction, which may synergize with the effects of poor mental health and malnutrition [5]. For example, conditions such as chronic kidney disease (CKD) and chronic obstructive pulmonary disease (COPD) are associated with heightened inflammatory markers and reduced physiological reserves, making patients more vulnerable to severe CAP outcomes. The interplay among these factors highlights the need for a holistic approach to managing elderly CAP patients, addressing not only the physical but also the psychological and nutritional aspects of care.
Holistic assessment
This study underscores the multifaceted nature of health outcomes in elderly patients with community-acquired pneumonia (CAP). Navarrete-Villanueva et al. emphasized the importance of a holistic approach to patient assessment, suggesting that addressing both physiological and non-physiological factors can improve outcomes [31]. Our study supports this perspective, advocating for a comprehensive approach to managing elderly patients with CAP. By integrating assessments of mental health, nutritional status, and comorbidities, we provide a more comprehensive understanding of the factors influencing short-term (28-day) and medium-term (90-day) mortality. These findings highlight the importance of adopting a holistic approach to patient management, which considers not only physical health but also psychological and nutritional factors. Such an approach could help healthcare providers identify high-risk patients more effectively and tailor interventions to address their specific needs. However, further research is needed to validate these findings and explore their applicability in diverse clinical settings.
Inflammatory responses
The correlation between CRP levels and mortality outcomes in our study corresponds with findings from Thiem et al., who noted that elevated inflammatory markers are associated with poorer pneumonia outcomes in elderly patients [32]. This underscores the necessity of monitoring inflammatory responses as part of managing CAP in older adults.
The proposed mechanisms—stress-related immune suppression, malnutrition-induced inflammation, and comorbidity-driven immune dysfunction—offer valuable insights into how these factors collectively influence prognosis. Understanding these pathways not only advances theoretical knowledge but also informs the development of targeted interventions. For example, integrating psychological support and nutritional therapy into routine CAP management may address these mechanisms and potentially improve patient outcomes, though further validation is required. Future research should further investigate these pathways in larger, prospective cohorts to validate and refine this framework.
Pharmacological and non-pharmacological interventions
Pharmacological therapies, particularly those targeting mental health, may significantly influence well-being and recovery in elderly CAP patients. For instance, antidepressants and anxiolytics have been shown to improve mental health outcomes, which may, in turn, enhance treatment adherence and physical recovery. Furthermore, anti-inflammatory drugs and vitamin supplementation could mitigate the inflammatory burden associated with CAP, potentially improving clinical outcomes. Future research should explore the integration of pharmacological and non-pharmacological interventions to optimize patient care [11–13].
In summary, our findings are consistent with existing literature that emphasizes the multifaceted nature of health outcomes in elderly CAP patients. By integrating assessments of mental health, nutritional status, and comorbidities, healthcare providers can enhance patient management strategies, ultimately improving outcomes for this vulnerable population.
Theoretical and practical implications
The implications of these findings are significant for both theoretical understanding and clinical practice. Theoretically, this study enhances the understanding of the complex nature of health outcomes in elderly patients with CAP. By elucidating the relationships among mental health, nutritional status, and comorbidities, this research provides a framework for future studies to further explore these interactions. Practically, the results suggest that healthcare providers may benefit from adopting a more comprehensive approach to managing elderly CAP patients, incorporating mental health and nutritional assessments into routine clinical practice. However, further research is needed to validate the effectiveness of such interventions. This could lead to tailored interventions that address the specific needs of this vulnerable population, ultimately improving patient outcomes and reducing mortality rates.
Theoretical framework for prognostic interactions
The interplay between CAP severity, mental health, and nutritional status can be conceptualized through a biopsychosocial model. Biological factors: CAP severity directly impacts mortality risk through physiological mechanisms, such as respiratory failure, septic shock, and multi-organ dysfunction. Psychological factors: Mental health, particularly stress and depression, influences immune function and inflammation, indirectly affecting recovery and prognosis. Social factors: Nutritional status reflects both biological and social determinants of health, such as access to adequate nutrition and support systems, which modulate the body's ability to combat infections and recover from illness. These factors interact dynamically, with severe CAP exacerbating psychological distress and nutritional deficits, while poor mental health and malnutrition amplify the physiological burden of severe CAP. This integrated framework underscores the need for a holistic approach to CAP management that addresses both physiological and psychosocial determinants of health.
Limitations
This study has several limitations that should be considered when interpreting the results. First, as a retrospective analysis, the study was constrained by incomplete and inconsistently recorded clinical data in the medical records. Specifically, certain critical clinical parameters, such as CAP severity scores (e.g., CURB-65 and PSI), were not systematically documented. Additionally, information on specific treatment measures, including antibiotic regimens was missing for a significant proportion of the patients. As a result, these variables were excluded from the analysis to avoid introducing bias due to missing or inconsistent data.
Second, while the study primarily focused on the associations among comorbidities, nutritional status, and mental health, the absence of CAP severity scores and treatment-specific data limits the interpretation of how these factors interact with clinical interventions or CAP severity in influencing patient outcomes. This missing data highlights a critical limitation of retrospective studies and underscores the importance of comprehensive data collection in future prospective research. Future studies should aim to systematically record CAP severity scores and treatment measures to provide a more robust and complete analysis of the factors influencing prognosis in elderly CAP patients.
This study used the standard Charlson Comorbidity Index (CCI) to assess comorbidity burden. While the modified CCI, which adjusts for patient age, may provide additional insights, its use in this cohort of patients aged 75 years or older would have introduced limited variability. Future studies could explore the impact of using the modified CCI in similar populations to further refine prognostic assessments.
The classification of severe community-acquired pneumonia (SCAP) in this study relied primarily on mechanical ventilation status and surrogate markers, such as documented hemodynamic instability (e.g., systolic blood pressure < 90 mmHg). However, the absence of key clinical data, including vasopressor use and arterial blood gas analysis (e.g., PaO2/FiO2 ratio), may have introduced misclassification bias. These variables are critical for defining septic shock and severe respiratory failure, which are integral components of SCAP classification. Consequently, the reliance on incomplete data may have underestimated or overestimated the prevalence of SCAP in our cohort.
Despite these limitations, the use of mechanical ventilation as a primary criterion for SCAP classification is supported by its strong association with severe respiratory failure and poor outcomes in CAP patients. Additionally, surrogate markers, such as systolic blood pressure < 90 mmHg and the need for high-flow oxygen supplementation, have been shown in previous studies to correlate with the need for intensive care and higher mortality risk. These findings provide indirect support for the validity of SCAP classification in this study. Future studies should aim to systematically record all key variables required for SCAP classification, including vasopressor use and arterial blood gas analysis, to improve the accuracy and reliability of SCAP classification. Prospective data collection would allow for more standardized definitions and reduce the risk of misclassification bias, ultimately enhancing the robustness of prognostic models.
This study is retrospective in nature, inherently carrying limitations such as potential selection bias and the inability to determine causality. Our findings remain observational and cannot definitively establish causal links between comorbidity, mental health, nutritional status, and mortality. It is crucial to consider that retrospective analyses are limited by their reliance on pre-existing records, possibly affecting the study’s validity. Prospective studies would be beneficial to delve deeper into causal mechanisms and verify these associations under controlled conditions. Additionally, the current study is confined to elderly patients with community-acquired pneumonia in a single hospital setting. This limited context may restrict the generalizability of our findings to broader populations. Consequently, multicenter studies are necessary to validate our results and potentially reveal varying patterns in different healthcare settings. A more diverse and comprehensive sampling would strengthen the external validity and applicability of the conclusions drawn from our analysis.
Future research directions
To address these limitations and advance the field, future research should consider prospective cohort designs that allow for comprehensive data collection and the inclusion of broader variables. Multi-center studies could enhance the generalizability of findings across diverse populations.
Future studies should investigate the mechanisms underlying the relationships among mental health, nutritional status, and comorbidities in elderly CAP patients. Exploring how interventions targeting mental health and nutrition can improve clinical outcomes would provide valuable insights for practice. Furthermore, developing a theoretical framework that integrates these factors could guide future research and inform clinical guidelines for managing elderly patients with CAP.
Conclusion
This study highlights the critical roles of comorbidities, nutritional status, and mental well-being in predicting mortality among elderly patients with CAP. The results demonstrate that higher comorbidity burden (CCI), poorer nutritional status (MNA-SF), and lower mental well-being (WHO-5) are independently associated with increased 28-day and 90-day mortality. Our study suggests that for predicting the prognosis of elderly patients with CAP, mental health, nutritional status, and comorbidity burden serve as important supplements to traditional physiological indicators. By adopting a holistic approach to patient management, healthcare providers may improve treatment adherence and recovery, though further research is needed to validate the impact on mortality rates.
In summary, this study provides a foundation for understanding the complex interplay between physical, nutritional, and mental health factors in elderly CAP patients. Future research should aim to refine prognostic models and develop evidence-based strategies that incorporate these factors into routine care, ultimately improving outcomes for this vulnerable population.
Abbreviations
- CAP
Community-acquired pneumonia
- SCAP
Severe community-acquired pneumonia
- MNA-SF
Mini Nutritional Assessment Short-Form
- CCI
Charlson Comorbidity Index
- WHO-5
WHO-5 Well-Being Index
- CRP
C-reactive protein
Authors’ contributions
LJ, HJ, and ML contributed to the conception and design of this study. LJ, HJ, ML, SX and XC contributed to data acquisition. LJ and ML performed the statistical analysis. LJ and ML interpreted the data and drafted the manuscript. All authors contributed to the critical revision of the manuscript and approved the version for publication.
Funding
This work was supported by Lianyungang Aging Health Research Project (L202308), and Science and Technology Project of Lianyungang Health Commission (QN202210).
Data availability
The data that support the findings of this study are available on request from the corresponding author.
Declarations
Ethics approval and consent to participate
This study followed the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the Second People's Hospital of Lianyungang (approval number 2022K040), which waived the requirement for informed consent due to the retrospective, non-interventional, and non-intrusive nature of the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jingxian Liao and Jingjing Hong contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author.