Utility of pneumonia severity assessment tools for mortality prediction in healthcare-associated pneumonia: a systematic review and meta-analysis

doi:10.21203/rs.3.rs-2883878/v1

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Utility of pneumonia severity assessment tools for mortality prediction in healthcare-associated pneumonia: a systematic review and meta-analysis

https://doi.org/10.21203/rs.3.rs-2883878/v1

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published 04 Jun, 2024

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Accurate prognostic tools for mortality in patients with healthcare-associated pneumonia (HCAP) are needed to provide appropriate medical care. Tools like PSI, A-DROP, I-ROAD, and CURB-65, widely used for predicting mortality in community-acquired and hospital-acquired pneumonia cases, remain controversial. We identified articles evaluating either PSI, A-DROP, I-ROAD, or CURB-65 and the mortality outcome in patients with HCAP and calculated the pooled sensitivities, specificities, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratios, and the overall summary area under the curves (AUCs) for mortality prediction. Additionally, the differences in prognostic power among these four assessment tools were evaluated using the mean differences of AUCs. Using a cut-off value of moderate, sensitivity, specificity, PLR, and NLR were found to be 0.91-0.97, 0.15-0.53, 1.14-1.66, and 0.18-0.33. respectively. Upon using a cut-off value of severe, sensitivity, specificity, PLR, and NLR were 0.63-0.70, 0.53-0.66, 1.49-2.03, and 0.47-0.58, respectively. Overall AUCs were 0.70 (0.68-0.72), 0.71 (0.63-0.78), 0.68 (0.63-0.73), and 0.67 (0.63-0.71), respectively, for PSI, A-DROP, I-ROAD, and CURB-65 (p=0.63). In conclusions, these severity assessment tools do not have enough power to predict mortality in HCAP patients. Furthermore, there are no significant differences in predictive performance among these four severity assessment tools.

Biological sciences/Microbiology/Bacteriology

Health sciences/Diseases/Infectious diseases

Health sciences/Diseases/Respiratory tract diseases

mortality

nursing- and healthcare-associated pneumonia

nursing home-acquired pneumonia

prognosis

severity score

Healthcare-associated pneumonia (HCAP) is a type of pneumonia that was described in the American thoracic society/infectious diseases society of America (ATS/IDSA) guidelines in 2005¹. In Japan, a similar category was proposed in 2011 as nursing- and healthcare-associated pneumonia (NHCAP)². Additionally, the ATS/IDSA 2019 guidelines recommended abandoning the category of HCAP and combining it with community acquired pneumonia (CAP) to avoid unnecessary selection of extended antibiotic coverage³. However, the characteristics of the patients, the detection rate of multi-drug resistant pathogens, and pneumonia mortality rates are different between CAP and HCAP in some countries, including Japan^4–7. It is important to evaluate the severity of elderly pneumonia patients such as the HCAP category in aging societies such as Japan. Therefore, proper prognostic tools are required to follow the appropriate clinical practice.

Severity assessment tools such as the pneumonia severity index (PSI) and CURB-65 (confusion, urea, respiration rate, blood pressure, age more than or equal to 65) find clinical use worldwide. In addition, tools like A-DROP (age, dehydration, respiration, orientation, pressure) and I-ROAD (immunodeficiency, respiration, orientation, age, dehydration) are widely used to predict mortality in Japan. PSI ^8,9, CURB-65 ^9–11, and A-DROP ^8–11 are shown to have good predictive efficacies for mortality in CAP and I-ROAD¹² in hospital-acquired pneumonia (HAP). But the utility of these tools for predicting mortality in patients with HCAP has been controversial because of conflicting reports ^7,13−21. The efficacy of these tools previously reported in patients with HCAP may be primarily influenced by the participants' social backgrounds, underlying diseases, and comorbidities. Therefore, studies targeting large populations are required.

We previously conducted a systematic review and meta-analysis on the utility of PSI and CURB-65 for predicting mortality in patients with HCAP. We showed that these tools lacked significant power in HCAP, though PSI may be slightly more useful than CURB-65²². However, only a few studies were included in the meta-analysis (seven for PSI and eight for CURB-65). There was insufficient data for A-DROP and I-ROAD (accessed on July 16, 2015).

In this systematic review and meta-analysis, we re-validate the significance of PSI and CURB-65 and evaluate the usefulness of A-DROP and I-ROAD for predicting mortality in patients with HCAP, aiming to reveal the effectiveness of existing severity assessment tools.

Search and selection criteria. This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and the Meta-Analyses (PRISMA) statement and Meta-analysis of observational Studies in epidemiology (MOOSE) guidelines^23,24.

We searched for studies on the PubMed database, and the following search words were applied: “pneumonia [MeSH Terms]” AND (“healthcare associated pneumonia” OR “health-care-associated pneumonia” OR “healthcare-associated pneumonia” OR “nursing home acquired pneumonia” OR “nursing and healthcare associated pneumonia” OR “long term care facility” OR “extended-care facility”) AND (“severity score” OR “predict” OR “prognosis” OR “mortality score” OR “pneumonia severity index” OR “PORT score” OR “fine score” OR “A-DROP” OR “I-ROAD”) (accessed on Aug 22, 2022) as previously reported²².

Inclusion criteria. The inclusion criteria for eligible studies were as follows: prospective or retrospective studies targeting hospitalized patients with HCAP, nursing home-acquired pneumonia (NHAP) and/or NHCAP according to the 2005 ATS/IDSA guidelines ¹ and/or the 2017 Japanese Respiratory society (JRS) guidelines ²⁵, evaluating severity scores and reporting mortality outcomes and raw data for the number of patients and deaths for any item of each severity grade, written in English as original research articles.

Data extraction and quality assessments. Two reviewers (SN and MK) independently assessed all the articles. The non-relevant studies were excluded based on the titles and abstracts after the PubMed search using the keywords, and the full texts of potentially appropriate titles and abstracts were further reviewed. The following information was collected from the included studies: geographic location, design, sample size, the mean age of participants, type of severity score, a common outcome, and mortality rate. The QUADAS-2, which includes four risk-of-bias domains and three domains of applicability ²⁶, was used to evaluate the risk of bias.

Outcomes. The primary outcome in this study was short-term mortality (28-day, 30-day or in-hospital mortality).

Statistical analysis. Paired forest plots were described using the Reviewer Manager 5.4 software program (Cochrane Collaboration, Oxford, UK). The pooled sensitivities, specificities, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratios (DORs) were calculated using the STATA 14 software (StataCorp LP, College Station, TX, USA). To assess the overall accuracy, the summary area under the curves (AUCs) was calculated based on the summary receiver operator characteristic curves (sROCs) using the STATA 14 software, as previously reported ²². In addition, the mean difference of AUC in each study was calculated and compared with the Review Manager ver. 5.4 software. In studies where AUC was not described, the AUC was calculated from the number of patients and fatalities for each severity grade using STATA 14 software. Statistical significance was set at a p-value of < 0.05.

Database search and risk of bias assessment. A total of 2,276 articles were identified in the initial search through the PubMed database, and thirty-six articles were potentially eligible after the first screening of the titles and abstracts. Next, the full text was reviewed, and seventeen articles were excluded. Eventually, nineteen observational studies were selected for this study (Fig. 1). The summary of the risk of bias in the included studies is shown in Fig. 2. Most studies were judged to be a “low” risk of bias.

Included studies. The characteristics of the included studies are shown in Table 1. Among these nineteen studies, prospective and retrospective studies were five ^{6,13,18,27,28} and fourteen ^{4,7,14,19,29−38}, respectively. The category of pneumonia was 8, 5, and 6 for HCAP ^{7,19,28, 30–33,37}, NHCAP ^{13,18,34,36,38}, and NHAP ^{4,6,14,27,29,35}, respectively. Twelve studies for PSI ^{4,7,14,19,27,28,31,33,36−39}, ten for A-DROP ^{4,7,18,19,30,32,34,36,38,39}, six for I-ROAD ^{7,18,19,36,38,39}, and fourteen for CURB-65 ^{4,6,7,14,19,27–29,31,33,35–37,39} were included in this study, respectively.

Table 1

Characteristics of included studies
First author, year	Country	Design	Category	Severity scores assessed	Patients number	Mean age(years)	Outcome	Mortality rate (%)
El-Solh A, 2010	USA	Retrospective	NHAP	CURB-65	457	77.4	30-day mortality	23.4
Shindo Y, 2010	Japan	Retrospective	HCAP	A-DROP	141	81.3	In-hospital mortality	21.3
Fang WF, 2011	Taiwan	Retrospective	HCAP	PSI, CURB-65	444	72.1	30-day mortality	20.9
Man SY, 2011	Hong Kong	Prospective	NHAP	PSI, CURB-65	767	83.4	30-day mortality	12.4
Watanabe M, 2011	Japan	Retrospective	HCAP	A-DROP	117	-	30-day mortality	5.1
Carrabba M, 2012	Italy	Prospective	HCAP	PSI, CURB-65	307	72.8	30-day mortality	24.1
Jeong BH, 2013	Korea	Retrospective	HCAP	PSI, CURB-65	419	67.0	30-day mortality	15.8
Lee JC, 2013	Korea	Retrospective	NHAP	PSI, CURB-65	208	80.0	30-day mortality	22.1
Oshitani Y, 2013	Japan	Retrospective	NHCAP	A-DROP	477	84.0	30-day mortality	14.0
Matsunuma R, 2014	Japan	Retrospective	HCAP	PSI, CURB-65, A-DROP, I-ROAD	74	80.0	30-day mortality	19.0
Ugajin M, 2014	Japan	Retrospective	NHAP	PSI, CURB-65, A-DROP	138	85.0	28-day mortality	18.1
Ma HM, 2015	Hong Kong	Prospective	NHAP	CURB-65	464	85.2	30-day mortality	15.5
Pereira R, 2016	Portugal	Retrospective	NHAP	CURB-65	103	-	In-hospital mortality	46.6
Koizumi T, 2017	Japan	Retrospective	NHCAP	PSI, CURB-65, A-DROP, I-ROAD	144	81.5	30-day mortality	9.7
Murillo-Zamora E, 2018	Mexico	Retrospective	HCAP	PSI, CURB-65	109	68.2	30-day mortality	59.6
Noguchi S, 2018	Japan	Retrospective	NHCAP	PSI, A-DROP, I-ROAD	289	85.2	In-hospital mortality	6.9
Asai N, 2019	Japan	Retrospective	HCAP	PSI, CURB-65, A-DROP, I-ROAD	229	78.1	30-day mortality	7.0
Ito A, 2020	Japan	Prospective	NHCAP	PSI, CURB-65, A-DROP, I-ROAD	828	78.0	30-day mortality	12.8
Imamura Y, 2022	Japan	Prospective	NHCAP	A-DROP, I-ROAD	563	-	30-day mortality	11.9
HCAP, healthcare-associated pneumonia; NHAP, nursing home-acquired pneumonia; NHCAP, nursing and healthcare-associated pneumonia

PSI. Twelve studies ^{4,7,14,19,27,28,31,33,36−39} were included in the meta-analysis for the PSI score. Using a cut-off value of ≥ IV (moderate; n = 12), the pooled sensitivity, specificity, PLR, and NLR for mortality were calculated as 0.97 (0.94–0.98), 0.15 (0.10–0.21), 1.14 (1.08–1.20), and 0.22 (0.12–0.38), respectively. The DOR and the AUCs were 5.09 (2.95–8.78) and 0.72 (0.67–0.75), respectively (Table 2). Using a cut-off value of V (severe) (n = 11), the pooled sensitivity, specificity, PLR, and NLR for mortality were 0.69 (0.60–0.77), 0.66 (0.60–0.72), 2.03 (1.82–2.27) and 0.47 (0.38–0.58), respectively. The DOR and the AUCs were 4.32 (3.35–5.59) and 0.72 (0.68–0.76), respectively. The forest plots and estimated sensitivities and specificities from each study are shown in Figs. 2a and b.

Table 2

Pooled characteristics of severity scores for predicting mortality
	Sensitivity		Specificity		PLR		NLR		DOR		AUC
PSI score
≥Ⅳ (n = 12)	0.97	(0.94–0.98)	0.15	(0.10–0.21)	1.14	(1.08–1.20)	0.22	(0.12–0.38)	5.09	(2.95–8.78)	0.72	(0.67–0.75)
Ⅴ (n = 11)	0.69	(0.60–0.77)	0.66	(0.60–0.72)	2.03	(1.82–2.27)	0.47	(0.38–0.58)	4.32	(3.35–5.59)	0.72	(0.68–0.76)
A-DROP
≥Ⅲ (n = 9)	0.7	(0.61–0.78)	0.53	(0.43–0.62)	1.49	(1.30–1.72)	0.56	(0.47–0.67)	2.67	(2.03–3.51)	0.67	(0.63–0.71)
I-ROAD
≥moderate (n = 5)	0.92	(0.69–0.98)	0.44	(0.30–0.59)	1.66	(1.39–1.98)	0.18	(0.05–0.61)	9.32	(2.86–30.3)	0.66	(0.64–0.72)
≥severe (n = 6)	0.68	(0.53–0.80)	0.6	(0.46–0.73)	1.7	(1.37–2.12)	0.54	(0.40–0.71)	3.18	(2.15–4.70)	0.69	(0.65–0.73)
CURB-65
≥Ⅱ (n = 13)	0.91	(0.84–0.95)	0.28	(0.20–0.37)	1.26	(1.17–1.36)	0.33	(0.23–0.46)	3.86	(2.74–5.44)	0.65	(0.61–0.69)
≥Ⅲ (n = 14)	0.63	(0.52–0.73)	0.63	(0.53–0.71)	1.7	(1.52–1.90)	0.58	(0.49–0.70)	2.91	(2.34–3.62)	0.67	(0.63–0.71)
PLR, positive likelihood ratio; NLR, negative likelihood ratio; DOR, diagnostic odds ratio; AUC, are under the curve

A-DROP. Ten studies ^{4,7,18,19,30,32,34,36,38,39} were included in the meta-analysis for the A-DROP score. Using a cut-off value of ≥ III (severe; n = 9), the pooled sensitivity, specificity, PLR, and NLR for mortality were 0.70 (0.61–0.78), 0.53 (0.43–0.62), 1.49 (1.30–1.72), and 0.56 (0.47–0.67), respectively. The DOR and the AUCs were 2.67 (2.03–3.51) and 0.67 (0.63–0.71), respectively (Table 2). The forest plots using these cut-offs and estimated sensitivities and specificities from each study, except one ⁷, are shown in Figs. 2c and d. The excluded study ⁷ had an unknown death number for each severity grade was unknown.

I-ROAD. Six studies ^{7,18,19,36,38,39} were included in the meta-analysis for the I-ROAD score. Using a cut-off value of ≥ moderate (n = 5), the pooled sensitivity, specificity, PLR, and NLR for mortality were 0.92 (0.69–0.98), 0.44 (0.30–0.59), 1.66 (1.39–1.98), and 0.18 (0.05–0.61), respectively. The DOR and the AUCs were 9.32 (2.86–30.3) and 0.66 (0.64–0.72), respectively (Table 2). Using a cut-off value of severe (n = 6), the pooled sensitivity, specificity, PLR, and NLR for mortality were 0.68 (0.53–0.80), 0.60 (0.46–0.73), 1.70 (1.37–2.12), and 0.54 (0.40–0.71), respectively. The DOR and the AUCs were 3.18 (2.15–4.70) and 0.69 (0.65–0.73), respectively. The forest plots using these cut-offs and estimated sensitivities and specificities from each study are shown in Figs. 2e and f.

CURB-65. Fourteen studies ^{4,6,7,14,19, 27–29,31,33, 35–37,39} were included in the meta-analysis for the CURB-65. Using a cut-off value of ≥ II (moderate; n = 13), the pooled sensitivity, specificity, PLR, and NLR for mortality were 0.91 (0.84–0.95), 0.28 (0.20–0.37), 1.26 (1.17–1.36), and 0.33 (0.23–0.46), respectively. The DOR and the AUCs were 3.86 (2.74–5.44) and 0.65 (0.61–0.69), respectively (Table 2). Using a cut-off value of III (severe; n = 14), the pooled sensitivity, specificity, PLR, and NLR for mortality were 0.63 (0.52–0.73), 0.63 (0.53–0.70), 1.70 (1.52–1.90), and 0.58 (0.49–0.70), respectively. The DOR and the AUCs were 2.91 (2.34–3.62) and 0.67 (0.63–0.71), respectively. The forest plots using these cut-offs and estimated sensitivities and specificities from each study are shown in Figs. 2g and h.

Comparisons of overall AUC among PSI, A-DROP, I-ROAD, and CURB-65. The mean differences in overall AUC were calculated using the overall AUC (95% CI) of each study (Fig. 3). The mean differences were 0.70 (0.68–0.72), 0.71 (0.63–0.78), 0.68 (0.63–0.73), and 0.67 (0.63–0.71) for PSI, A-DROP, I-ROAD, CURB-65 scores, respectively (Fig. 4). No significant differences were observed (p = 0.63).

The present study evaluated the significance of PSI, A-DROP, I-ROAD, and CURB-65 for predicting mortality in HCAP patients. Our results indicate that these severity assessment tools cannot accurately predict mortality in HCAP patients. In addition, there were no significant differences between these severity assessment tools.

It has been shown that PSI, A-DROP and CURB-65 in CAP patients and I-ROAD in HAP patients have high AUCs, nearly 0.8, for predicting mortality ^8–12. In this meta-analysis, the overall AUCs for these severity assessment tools for predicting mortality are 0.67–0.71. although, only two reports showed high AUC values of over 0.8 for A-DROP ³² and CURB-65 ³⁶. AUC is often used to measure the accuracy in studies of severity assessment, and the discriminatory value based on AUC is evaluated as “poor” for 0.60–0.69, “moderate” for 0.70–0.79, “good” for 0.80–0.89, and “excellent” for 0.90-1.00, respectively ⁴⁰, although its criteria differ between studies ⁴¹. In our study, PSI and A-DROP had “moderate” discriminative ability, while I-ROAD and CURB-65 showed “poor” discriminative ability when we follow this criteria. Overall, our results showed no significant power for predicting mortality among the four assessment tools. Generally, patients with HCAP are highly heterogeneous, and their mortality is affected by various factors, including general conditions, laboratory data on admission to the hospital, comorbidities, antibiotic-resistant bacterial infections, and their social backgrounds. Thus, these severity assessment tools did not show enough predictive power for mortality in HCAP patients.

This meta-analysis found no significant differences in overall AUCs between PSI and the remaining tools. PSI includes some comorbidities and physical and laboratory parameters as evaluation items and might be the best score for predicting mortality in the patients’ group with comorbidities such as HCAP ⁴². In addition, the item “pH”, is included in PSI and the SCAP score, is known as an indicator of metabolic acidosis under sepsis, ³¹. The item “age”, included in all of the severity assessment tools evaluated in this study, occupies a relatively large weight in PSI score but was not a significant risk factor for in-hospital mortality in NHCAP⁵. Age and comorbidities may be overvalued in predicting pneumonia severity in elderly patients with HCAP ³³. This explains the low AUC value despite a large number of items. Similar to our results with low NLR, Calmers et al. reported that PSI might be superior for identifying low-risk patients with low NLR (0.2 for ≥ IV and 0.5 for ≥Ⅴ) in patients with CAP, although the AUC value in our results was low compared with that of CAP (0.82 for ≥ IV, 0.81 for ≥Ⅴ) ⁴³. Therefore, PSI may be useful for identifying low-risk patients in HCAP similar to CAP patients, and NLR below 0.1 is generally considered useful for diagnoses ⁴⁴.

A-DROP and CURB-65 are easy to use in daily clinical practice. However, these tools may not be ideal in patients with multiple comorbidities because these tools may underestimate the severity in the elderly patients with comorbidities ⁴⁵. In addition, most HCAP patients are over 65 years old, and the age index of A-DROP and CURB-65 might not be significant, although the utility of CURB, without the item “age”, was insignificant in patients with HCAP ^28,29. On the other hand, the results of this study showed that A-DROP and CURB-65 had almost similar predictive powers to PSI in the evaluation using overall AUC. However, the predictive abilities themselves of A-DROP and CURB-65 were not enough to predict mortality. PSI is relatively complex and often avoided in complicated environments such as an emergency room. Our results indicate that A-DROP and CURB-65 can be one of the choices, instead of PSI, in clinical practice for HCAP owing to their evaluation conveniences.

Our previous study could not evaluate the utility of I-ROAD for predicting mortality in HCAP ²² because there was only one report ¹⁹ (accessed July 16, 2015). However, this study analyzed reports on I-ROAD published after 2015 (all Japanese studies). I-ROAD includes immunodeficiency and radiological findings, a major difference from the other severity assessment tools, such as PSI, A-DROP and CURB-65. Indeed, it was reported that the prognostic ability of PSI and CURB-65 for mortality prediction in HCAP patients changed irrespective of immunosuppression ²⁸, consistent with our previous study ²². In addition, radiological characteristics such as bilateral pneumonia were reported as independent risk factors for mortality in NHAP ⁴⁶. Although I-ROAD is not widely used outside Japan, it might be a viable choice in patients with HCAP since there was no significant difference between I-ROAD and other severity assessment tools despite their low prognostic power.

In addition to the major evaluation method listed above, various severity assessment tools HCAP such as the IDSA/ATS severity criteria ^13,31, M-ATS ^27,31, NHAP index ¹⁶, NHAP model score ¹⁴, qSOFA ^7,38, R-ATS rules ²⁷ SCAP ^28,31, SMART-COP ^16,31, SOAR ^14,29,31 and SOFA⁷, but none of them showed adequate prognostic capability. In Japan, sepsis evaluation using qSOFA and SOFA was recommended as the initial evaluation in the 2017 JRS guidelines for managing pneumonia in adults, in addition to severity assessment by PSI, A-DROP, or CURB-65 ²⁵. We previously showed that qSOFA above 2 was as useful as PSI for predicting mortality in NHCAP ³⁸. Further, Asai et al. demonstrated that SOFA scores in combination with qSOFA might more accurately evaluate the severity of HCAP ⁷. On the other hand, it was reported that the evaluation based on clinical conditions such as malnutrition, acute mental status and deterioration and health conditions such as home care, recent hospitalization, and low BMI should be used for severity assessment ^46,47. We also showed the usefulness of combining hypoalbuminemia with PSI or qSOFA ³⁸. Thus, combining new items needs to be considered for predicting mortality in HCAP patients.

There were some limitations in this systematic review and meta-analysis. First, the included reports had a large heterogeneity—a common drawback in meta-analyses ⁴⁸. Second, this study had differences in each country, study design, category of pneumonia, study population, and outcome, which we could not assess due to limited accessible data and a relatively small sample size. However, the heterogeneity in the HCAP population makes our findings significant. Third, we could not evaluate A-DROP for scores more than “moderate” because many studies included in this analysis had a sensitivity of almost 100% and a specificity of 5% or less. But these results may indicate that the criteria of moderate grade in A-DROP do not have a mean for mortality prediction because most subjects, including those in the HCAP category, are adults aged 65 and above.

In conclusion, the predictive role of PSI, A-DROP, I-ROAD, and CURB-65 for mortality was insufficient for predicting mortality in HCAP patients. We have described useful prognostic factors for mortality in HCAP patients, hoping to establish a more useful severity assessment tool with highly accurate prediction ability while considering the existing tools.

Acknowledgements:

This systematic review and a meta-analysis were performed in the process of composing the Japanese Respiratory Society Practical Guidelines for pneumonia. The authors appreciate the contributions of all of the number of the Guidelines Committee.

Author contribution

S.N. and K.Y. prepared the manuscript and M.K., D.X., N.N., Y.F., Y.S., K.S., H.T., M.M. and H.M. revised the manuscript. Y.S., K.S., H.T., M.M. and H.M. contributed to the protocol design. S.N., M.K., K.Y., D.X., N.N. and Y.F. contributed to the data collection. S.N. and M.K. performed data extraction and analysis. All authors reviewed the manuscript.

Data Availability Statement:

The data that support the findings of this study are available from the corresponding authors upon reasonable request.

Human ethics approval declaration

Not applicable.

Conflict of Interest:

All authors declare no conflicts of interest.

Ethics Approval Statement: Not applicable.

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No competing interests reported.

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Utility of pneumonia severity assessment tools for mortality prediction in healthcare-associated pneumonia: a systematic review and meta-analysis

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