The use of multiplex PCR for the detection of respiratory pathogens in hospitalised adults with community-acquired pneumonia during the pre-COVID era in South Africa: A retrospective cohort

The use of multiplex PCR for the detection of respiratory pathogens in hospitalised adults with community-acquired pneumonia during the pre-COVID era in South Africa: A retrospective cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The use of multiplex PCR for the detection of respiratory pathogens in hospitalised adults with community-acquired pneumonia during the pre-COVID era in South Africa: A retrospective cohort Shahed Omar, Taskeen Meeran, Tumelo Moloantoa, Pattamukkil Abraham, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9003553/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Community-acquired pneumonia (CAP) is a major cause of hospitalisation. In South Africa 74% of adults hospitalised with CAP are patients living with HIV (PLWH). Streptococcus(S) pneumoniae remains a leading cause of CAP in PLWH. Rapid and accurate pathogen identification has significant management implications. The objectives of the study is to: describe respiratory pathogens detected by the BioFire® FilmArray® multiplex PCR pneumonia panels (MPPP) and compare S. pneumoniae detection with standard diagnostics in adults admitted with CAP. Secondary objectives were to stratify pathogens by HIV status and assess length of stay (LOS) and mortality risk. Methods Stored sputum specimens from the PotPrev trial collected between August to December 2019, pre-covid era, were analysed using MPPP. S. pneumoniae identification was made by three separate methods: multiplex PCR (mPCR) on sputum, single-plex PCR on nasopharyngeal swab (NPS) and blood. Results In 146 samples analysed, MPPP detected 275 pathogens in 119 patients (81.5%). Seven organisms accounted for 80% of pathogens: Streptococcus pneumoniae (24.7%), Haemophilus influenzae (17.8%), Staphylococcus aureus (9.1%), Mycobacterium tuberculosis (9.1%), Moraxella catarrhalis (8.4%), Rhinovirus (5.5%) and Influenza virus (4.4%). Among 68 S. pneumoniae cases, NPS lytA PCR had the highest sensitivity (91.2%), followed by mPCR (67.6%) and blood lytA PCR (27.9%). Of note, mPCR detected 7.4% additional cases. Concordance between NPS lytA PCR and mPCR was 61.3%, and blood lytA PCR 24.6%. Among PLWH, a wider spectrum of pathogens was observed, with Pneumocystis jirovecii pneumonia accounting for a high proportion of cases (8.7%). There were no differences in bacterial (p = 0.37), viral (p = 0.5), mortality (p = 0.6), or length of stay (median six days, p = 0.96) in PLWH. Survivors and non-survivors had similar LOS (p = 0.68). Low oxygen saturation was associated with reduced survival (p = 0.039). Conclusion MPPP in addition to standard diagnostics increases pathogen detection in hospitalized CAP. PLWH had distinct aetiology with a higher pathogen burden. These findings support the value of molecular diagnostics and context-specific approaches in high HIV-burden settings. Community- acquired pneumonia Multiplex PCR BioFire® FilmArray® Pneumonia Panel Molecular diagnostics Respiratory Pathogens Streptococcus Pneumonia HIV- associated pneumonia Hospitalised adults Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Community acquired pneumonia (CAP) is a major cause of hospitalization and mortality worldwide. 1 _ 4 According to World Health Organisation (WHO), lower respiratory tract infections were responsible for 2,5 million deaths in 2021, ranking among the top 5 causes of death. 5 Other important complications in addition to mortality include acute respiratory distress syndrome, sepsis, and multiorgan failure, all requiring additional resources in the intensive care unit. 6 _ 7 CAP is a major contributor to the burden of disease in patients living with HIV (PLWH) 4 and in South Africa, PLWH are hospitalized with CAP. 8 Streptococcus (S) pneumoniae is the leading cause of CAP worldwide and in PLWH who are at increased risk of severe pneumococcal disease, it remains an important cause of increased morbidity and mortality despite antiretroviral therapy. 1 2 4 Rapid, accurate identification of the causative pathogen may improve patient management and outcome and reduce the burden of disease in low-middle income countries (LMIC). The current epidemiology of hospitalised CAP is based on non- specific, low sensitivity sputum gram stain and standard culture based techniques with turnaround times of 48-72hours. 10 _ 12 By that time, patients may already have died, deteriorated or been discharged. Less than 40% hospitalized CAP patients have a microbiological diagnosis. 13 Empiric antibiotic therapy has been shown to be associated with a significant reduction in mortality, 14 but improved diagnostic testing for CAP allows switching to pathogen specific targeted treatment. 13 15 _ 17 Antimicrobial appropriateness was found to be significantly better, with 80% of patients receiving result driven therapy in 2.3 hours compared to 29% in 46 hours with standard care and 42% of the patients had antibiotic de-escalation compared to 8% without MPPP, for hospitalised CAP. 13 16 17 Recent advances in molecular diagnostics, such as MPPP have demonstrated superior sensitivity (96.3%) and specificity (97.2%) with rapid turnaround times as little as 1 hour compared to 48–72 hours for standard diagnostic methods in hospitalized CAP. 10 _ 12 MPPP allows for simultaneous detection of multiple respiratory pathogens from a single easy-to-collect respiratory sample, including pathogens that are difficult to culture or require specialized testing, 9 identifying bacterial agents in 50% of samples compared to only 14% with traditional methods. 11 12 The applicability and utility of these molecular diagnostics in a hospital setting with high HIV burden, where patients present with broader and more complex range of pathogens, remain underexplored. In this sub- study of the PotPrev trial, 8 we aimed to describe the spectrum of respiratory pathogens identified by MPPP in a cohort of adult patients with hospitalized CAP prior to the COVID pandemic. We further assessed the pathogen distribution by HIV status: patients living with HIV(PLWH) and patients without HIV (PWoH) and explored the association with in hospital length of stay (LOS) and mortality. METHODS Study Design and Setting This study used specimens collected in 2019 from a prospective cohort study which recruited patients at two teaching hospitals in two provinces of South Africa: Gauteng and North West, with varying HIV prevalence. 8 The hospitals included Chris Hani Baragwanath Academic Hospital and Klerksdorp/Tshepong Hospital Complex. Ethical considerations Approval for the study was obtained from the Human Research Ethics Committee (Medical) [HREC] affiliated to the University of Witwatersrand, approval number [M171009] . Written informed consent was obtained from the patient or legal surrogate before enrolment. Participants We included 146 specimens collected from adults aged ≥ 18 years; 59 females and 87 males who were admitted to the study hospitals for presumptive CAP between August 2019 and December 2019. The majority of the participants (107/146) were PLWH (73.3%), (23/87) were virally suppressed (26%) and of the 64% with non-supressed HIV viral load (VL), the median (IQR) VL was 50018(2498 − 175 750) copies/ml. Overall, the median (IQR) CD4 + count for the PLWH was 159(37–346) cells/mm 3 . Eligible patients had at least two of the following signs and symptoms: temperature > 38°C (oral) or > 38.5°C (tympanic), hypothermia 20/min, abnormal auscultatory findings or clinical/radiological evidence of lung consolidation, and resided in the relevant catchment area. Exclusion criteria included enrolment in a prior or current CAP vaccine or pneumonia treatment trials, absence of valid contact details, or inability to attend follow up after discharge. Study Procedure and Variables During their admission, consented participants had detailed socio-demographic data collected via patient interview and clinical data relating to their admission abstracted from medical records. They inter-alia provided two sputum specimens. One was tested for tuberculosis on liquid mycobacterial culture and Xpert MTB/RIF Ultra and the second was stored in a repository at -80°C for future testing with the approval from the local Institutional Review Board. Whole blood (lytA Blood PCR) and nasopharyngeal swabs (lytA NPS PCR) were also collected for detection of S. pneumonia . Convenient repository expectorated sputum samples were retrieved, defrosted to room temperature and analysed by the study team with BioFire® FilmArray® Pneumonia Plus Panel, following the manufacturer’s protocol. This assay utilizes nested PCR to identify 27 bacterial and viral species, along with 7 antimicrobial resistance genes, delivering results in roughly 60 minutes (Table sl 1). 20 Study variables included, age and demographic data, admission vital signs, mean arterial pressure, room air and on oxygen saturation on pulse oximetry, SF ratio (Saturation to fraction of inspired oxygen), CURB score, HIV status at admission, prior antibiotic use before admission, microbiological results from routine diagnostic investigations during admission, additional PCR for S. pneumoniae on NPS and blood and finally results of MPPP performed on stored sputum. Length of hospital stay as well as outcome of the admission (diagnosis at discharge and death) are reported. Aim The primary objective was a description of the respiratory pathogens detected by routine investigation and MPPP in adult patients admitted with CAP. Secondary objectives: to stratify pathogen distribution by HIV status, and to assess length of stay in hospital and mortality risk. RESULTS We used convenient samples of the first 146 patients recruited in the PotPrev study based on availability of BioFire® FilmArray® Pneumonia Plus Panel (one panel per patient) kits and the patients baseline characteristics on admission and CURB score are provided (Table 1 and Figure 1). Table 1: Baseline characteristics on admission Variable All, N=146 Median (IQR) HIV-, N=39 Median (IQR) HIV+, N=107 Median (IQR) P (Mann Whitney U test) Median Age(years) 45 (35-58) 61 (50-67) 43 (34-52) 0.00 Median admission Temp ° C 36.8 (36.4-37.4) 36.6(36.2-37.3) 36.8 (36.5-37.5) 0.12 RR/min 20 (18-22) 20 (20-22) 20 (18-22) 0.17 HR/min 115 (101-129) 107 (92-119) 116 (106-130) 0.005 Systolic Blood Pressure 117 (104-135) 129 (110-143) 115 (102-131) 0.007 Mean Arterial Pressure 85 (75-97) 91 (81-103) 83 (74-96) 0.03 Room Air Sp02 90 (86-95) 86 (80-93) 91 (87-95) 0.009 SF ratio 429 (405-452) 407(381-445) 433(419-452) 0.01 CURB SCORE 1(0-1) 1(0-1) 1(0-3) 0.537 We identified 275 pathogens among 119 of 146 patients included in our study, identifying a pathogen in 81.5% of hospitalized CAP (Figure 2). Twenty-seven (n= 27/146) patients had no pathogen detected on any of the test methodology. There were no gender differences in pathogen positivity rates, females 83.05% (n=49/59) and males 80.45% (n=70/87), p=0.692 (Table sI 2: Pathogens detected on multiplex PCR (MPPP) based on gender). Eighty percent of all positive results were due eight micro-organisms- 5 bacteria, 2 viruses and 1 fungus. The 5 most prevalent bacterial pathogens included Streptococcus (S) pneumoniae, Hemophilus (H) influenzae, Staphylococcus aureus, Mycobacterium tuberculosis and Moraxella Catarrhalis , which together represented 70% of all micro-organisms detected. Fungi such as Pneumocystis Jirovecii Pneumonia (PJP) accounted for 7.3% of the micro-organisms found with Beta D glucan and mono-plex PCR for PJP. Two viruses Influenza A and Rhinovirus together made up 9.9% of pathogens detected. S. pneumoniae was the single most prevalent pathogen detected with 68 cases identified by at least one of the three methods used. The highest sensitivity was observed with lytA NPS PCR 91.2% (n=62/68). Multiplex PCR (mPCR) on sputum detected 67.6%(n=46/68), while lytA PCR from blood samples detected only 27.9% (n=19/68). Notably, mPCR contributed to the detection of 5 additional cases (7.4%) that were missed by NPS lytA PCR alone. Concordance analysis showed that 38 of the 62 NPS lytA-positive cases were also positive by mPCR, giving a concordance rate of 61.3%, while only 15 of the 61 NPS lytA-positive cases were also detected by blood lytA PCR (24.6%). These findings underscore the utility of lytA NPS PCR for detecting S. pneumoniae , while also highlighting the complementary role of mPCR in identifying additional cases not captured by single-target PCR methods. The addition of blood lytA PCR for S. pneumoniae detected only one additional case. Among the 27 participants, 14 were clinically diagnosed with pneumonia and 13 having no pneumonia. PLWH comprised 57% (8/14) of the pneumonia group compared to 38.5% (5/13) of the non-pneumonia group. All participants in both groups tested negative for GeneXpert MTB/RIF and were clinically assessed as negative for Pneumocystis jirovecii pneumonia. Urine LAM testing was negative in all tested participants, occurring in 10 individuals in the pneumonia group and 5 in the non-pneumonia group. No deaths occurred in the pneumonia group, whereas mortality was observed in 15.4% (2/13) of those without pneumonia. Alternative diagnoses in the non-pneumonia group included chronic lung disease (38.5%), cardiac disease (23%), acute asthma (7.7%), restrictive lung disease (7.7%), toxin-induced lung disease (7.7%), cervical cancer (7.7%), and no identifiable cause in 7.7% of cases. Secondary objectives: Pathogen distribution and HIV status Two hundred and twenty-eight pathogens were identified in 107 PLWH, while only 47 pathogens were identified in 39 PWoH. One or more pathogens were detected in 94/107 (87.85%) PLWH, compared to 25/39 (64,10%) PWoH, resulting in a relative risk of pathogen detection in PLWH of 1.64 (95% CI 1.22-2.21), p= 0.001 (Chi squared 10.6) (Figure 3 and 4). We compared PLWH and PWoH with respect to proportions of bacteria, viruses and PJP and MTB . Only PJP was significantly higher in PLWH compared to PWoH. Organisms causing opportunistic infections were more commonly picked up in PLWH as expected (Table 2). Table 2: Pathogen groups among patients stratified by HIV status. Pathogens HIV positive HIV negative P (Chi-square) Bacteria 160(70.1%) 36(76.5%) 0.376 Tuberculosis (TB) 20(8.7%) 5(10.6%) 0.685 PJP 20(8.7%) 0 #0.03 (Fisher exact) Virus 28(12.2%) 6(12.7%) 0.5 Total 228 47 Length of stay, HIV status and mortality One patient had no LOS and mortality data recorded and was excluded. The median length of stay (LOS) for the remaining cohort was six-days. There were no significant differences in LOS between PLWH and PWoH. There was also no significant difference in LOS between survivors and non-survivors (Table 3). Table 3: Length of Stay Group Median days (IQR) P (Mann Whitney U) All 6 (4-11) PLWH 6 (4-11) PWoH 6 (4-11) 0.96 Survivors n=132 6 (4-11) Non-Survivors n= 12 5.5 (4-13.5) 0.68 Overall mortality was 8.21%, with no significant difference between PLWH and PWoH, Chi squared test = 0.28 (Table 4). Table 4: HIV status and mortality HIV TEST RESULT Survivors Non -Survivors p Negative 35 (89.7%) 4 (10.3%) Positive 98 (92.5%) 8 (7.5%) 0.6 Eleven of the twelve non-survivors died in hospital, one patient with chronic lung disease died out of hospital. There were different causes of death amongst PLWH and PWoH. Amongst PLWH, three patients died of PJP , three due to pneumonia unspecified, one to MTB and one to sepsis. For PWoH, one died from chronic lung disease, one from a malignancy and one from pneumonia unspecified. Table 5 shows clinical characteristics between survivors and non survivors. Table 5: Clinical characteristics of survivors and non survivors Variable All, N=146 Median (IQR) Survivors, N=133 Median (IQR) Non survivors, N=12 Median (IQR) P (Mann Whitney U test) Age(years) 45 (35-58) 45 (35-58) 51.5 (34-55) 0.94 Temp ° C 36.8 (36.4-37.4) 36.8 (36.4-37.5) 36.8 (36.4-37.5) 0.13 RR/min 20 (18-22) 20 (18-22) 21 (20-25) 0.03* Delta Sp02 4.5 (0-9), n=46 5 (0-10), n=41 0 (-23-9), n=5 0.08 Delta Sp02 refers to the increase in Sp02 once oxygen therapy was provided. RA Sp02 90 (86-95) 90 (86-95) 89 (89-95) 0.24 Sp02 on 0 2 94 (91-98), n=49 94 (91-98) n=44 89 (89-95), n=5 0.59 SF ratio 429 (405-452) 429 (405-452), n=101 424 (400-438), n=7 0.21 HR/min 115 (101-129) 115 (101-129) 111 (107-119.5) 0.7 SBP 117 (104-135) 117 (103-135) 116 (109.5-132) 0.93 MAP 85 (75-97) 85 (74-98) 84 (78-87) 0.73 We found no significant differences in pathogen groups (bacteria, MTB , PJP and viruses) identified between survivors and non-survivors. Risk of death from PJP compared to all other pathogens was 3.98(95% CI 1.63-9.72) p=0.002 (Table 6). Table 6: Pathogen groups among survivors and non-survivors Type of organism Survivors, n=134 Non-Survivors n=12 p Bacteria -N, % 184 (66%) 12 (57.1%) 0.137 Tuberculosis- N, % 24 (9.4%) 1 (4.7%) 0.473 Pneumocystis Jirovecii -N, % 15 (5.9%) 5 (23,8%) 0.002 X 2 = 9.22 Virus 31 (12.2%) 3 (14.2%) 0.781 Total 254 21 Using logistic regression analysis to predict survival, we included variables from the univariate analysis including CURB 65 score, saturation on oxygen, respiratory rate, mean arterial pressure and presence of Klebsiella pneumoniae (Table 7) and PJP with a p<0.2 and included CURB 65 and saturation on oxygen at admission into the final model. Only the saturation of oxygen was associated with mortality (OR 0.876, 95% CI 0.773-0.993) p=0.039. An improved saturation above 94(91-98) % after administration of oxygen was associated with reduced mortality. Among the non-survivors, one patient had MERS CoV (middle east respiratory system coronavirus). Table 7: Individual pathogens associated with survival and non-survival. Survivors Non survivors P, X 2 or Fisher exact Klebsiella pneumonia + 5 (9.5%) 2 (1,96%) p=0,035, X2= 4.8 Fisher exact 0.093 Klebsiella pneumonia - 249 (98%) 19 (90.5%) MERS Corona virus + 0 1 (100%) 0.07 DISCUSSION Our findings demonstrate a high pathogen detection rate from a single sputum specimen in ~ 80% of patients hospitalized with presumptive CAP in South Africa, with the use of MPPP in addition to routine diagnostic testing. This highlights MPPP enhanced sensitivity (96.3%) 11_12 20 and specificity (97.2%) 11_12 20 and rapid turnaround time of about one hour with two minutes of hands-on time, compared to 48–72 hours for standard-of-care results 10 _ 12 . Similar high detection rates have been reported in retrospective cohorts globally, from high to low income countries (HLIC) including 98% in Turkey, 21 87% in United Kingdom(UK), 19 83% in South Africa, 22 60–70% increase in pathogen detection in Uganda, 9 Germany, 23 China, 24 and in USA. 18 These findings have consistently outperformed standard culture methods, 11 which are typically associated with significantly lower detection rates(23.4%-60%), 19 21 22 detecting bacterial pathogens in only 14% of patient samples compared to 50% with MPPP. 11 12 19 Our data, in keeping with other MPPP studies, support this improved performance compared to standard culture in our setting. Variations in MPPP detection rates on sputum globally may reflect differences in HIV prevalence, presence of other co-morbidities, sputum quality, study design and sample size. Among the 228 pathogens identified in our study, as expected, S. pneumoniae (SP) and H. influenzae (HI) accounted for more than one-third of all pathogens detected. Other studies from HLIC similarly highlight the predominance of these pathogens. In the UK and in Turkey, SP and HI co-infection occurred in 40.2% 19 and 36% 21 of patients respectively. A South African study in 2022 also reported these two predominant pathogens as possible aetiology of CAP (35%). 22 Our findings confirm that SP and HI remain the major contributors to CAP- related hospitalizations and deaths globally. 18 8 Ten percent of all detected pathogens were viruses in our study. A Kenyan study reported viral pathogens in nearly half of the CAP cases (48%) with Influenza A being the most commonly identified virus, 15 while a study from Turkey and USA reported rhinovirus as the most frequent respiratory virus (20%). 21 25 Our data is in keeping with these studies identifying rhinovirus and Influenza A as the two common viral pathogens, underscoring the value of MPPP for comprehensive pathogen detection in LMIC. We detected Mycoplasma pneumoniae (MP) in about 1% of cases. Higher detection rates for MP were found in Turkey (6%) 21 and in a large multicentre study from China with almost 11% of cases. 24 It is possible that the small sample size in our study may have resulted in an underestimation, however given that the study done in Turkey was half the size of our study, regional differences cannot be discounted. Stratification by HIV status further revealed significant differences in pathogen profiles, emphasizing the impact of immunosuppression on aetiology of CAP in PLWH. 9 Venturas et al. reported similar poly-microbe profiles in PLWH at another academic hospital in South Africa. 4 As shown by our study, MPPP provide insight into the diagnostic value of these molecular diagnostics in a high HIV prevalent setting. A recent Ugandan study on 107 PLWH admitted with hospitalised CAP, also demonstrated a high pathogen detection rate (83.2% bacterial) and (49,5% viral) using MPPP. 9 No significant differences in bacterial (non-MTB) and viral pathogen groups were found between PLWH and PWoH. Unsurprisingly, opportunistic infections such as PJP(7.3%) and M tuberculosis(MTB) (9.1%) were more prevalent in PLWH compared to PWoH as causes of CAP leading to admission. The underlying immune status profoundly influences the infectious aetiology of respiratory illness. The higher frequency of PJP and MTB among PLWH suggests a potential need to expand MPPP to include MTB and PJP in LMICs with high HIV prevalence, particularly among severely immunocompromised individuals. Implementing such targeted diagnostics may enable timely and precise treatment decisions. Despite the difference in pathogen distribution, we found no significant difference in the LOS or in- hospital mortality between PLWH and PWoH, reinforcing prior findings from international CAPO cohorts that clinical outcomes, including time to clinical stability, LOS, and mortality are comparable across HIV status. 26 Co- infection with viral pathogens has been suggested to be associated with longer LOS, 24 but this was not demonstrated. Only the presence of low saturation on oxygen was independently associated with reduced survival. Multidrug resistant pathogen, hypoxia (a condition characterized by an insufficient supply of oxygen to body tissues) and prior antibiotic use was associated with 30-day mortality. 9 27 CONCLUSION Multiplex PCR pneumonia panels in addition to standard diagnostic methods results in enhanced respiratory pathogen detection in adults hospitalized with CAP. PLWH had a higher pathogen burden with PJP and MTB significantly contributing as causes of CAP. Our findings highlight the diagnostic value of expanded molecular tests on different specimens and the need for specific approaches adapted to managing hospitalised community acquired pneumonia especially with a resource constrained setting with a high HIV burden. STRENGTHS AND LIMITATIONS Strengths : The use of samples from a rigorously conducted clinical trial, allowing for integration of detailed clinical data with advanced molecular diagnostics. Additionally, stratification by HIV status provides important epidemiological insights. Limitations : Our study was limited to CAP patients able to produce sputum, which may be contaminated by oropharyngeal flora. Less contamination-prone specimens such as bronchoalveolar lavage require specialized expertise and are not routinely obtained in non-intubated patients. Interpretation of MPPP results from non-sterile, stored sputum samples is further complicated by potential contamination and nucleic acid degradation, highlighting the need for prospective studies using fresh specimens. Finally, because clinical management was not guided by MPPP results, this retrospective study cannot assess the impact of these diagnostics on patient outcomes. RECOMMENDATIONS FOR FUTURE RESEARCH Prospective studies evaluating real-time MPPP use on fresh sputum samples and the impact on clinical decision-making and patient outcomes. Investigations into pathogen load, co-infection dynamics, and host immune response, particularly in PLWH. Abbreviations CAP community acquired pneumonia PLWH patients living with HIV PWOH patients without HIV HIV human immunodeficiency virus S Streptococcus H haemophilus MPPP multiplex PCR pneumonia panels PCR polymerase chain reaction LOS length of stay mPCR Multiplex polymerase chain reaction NPS nasopharyngeal swab WHO World Health Organisation LMIC low-middle income countries LOS length of stay VL viral load MTB/RIF Mycobacterium tuberculosis / resistance to rifampicin PJP Pneumocystis Jirovecii Pneumonia HLIC high to low income country UK United Kingdom Declarations Ethics approval and consent to participate This study protocol was reviewed and approved by the following Ethical review board: Human Research Ethics Committee (Medical) [HREC] affiliated to the University of Witwatersrand, approval number [M171009] . The approval date was 27 Oct 2017 (reference: 171009). The University of the Witwatersrand (Wits) Human Research Ethics Committee (HREC), both Medical and Non-Medical, operates in accordance with the Declaration of Helsinki. Its Standard Operating Procedures (SOPs) expressly incorporate the Declaration of Helsinki, alongside ICH-GCP and South African national guidelines, to ensure ethical research standards. Written informed consent was obtained from the patient or legal surrogate before enrolment. Clinical trial number: not applicable.’ Consent for publication N/A Availability of data and materials An electronic copy of de-identified data is available on request from the corresponding author. Competing interests The authors declare that they have no competing interests Funding BioMerieux provided theBiofire pneumonia panel kits required for the study. Authors' contributions SO conceived the study, designed the protocol, data analysis, and co-drafted the manuscript. TMee assisted with data analysis and co-drafted the manuscript. TMol, PA, EV, and MW made substantial contributions to the work and critically reviewed the manuscript. FN contributed to the ethics application and manuscript preparation. NM contributed to protocol design and manuscript preparation. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work. Acknowledgements We thank the PotPrev Trial team for making this sub-study possible and acknowledge all members that assisted with the co-ordination of activities during the study. References Zar HJ, Madhi SA, Aston SJ, Gordon SB. Pneumonia in low- and middle-income countries: progress and challenges. Thorax. 2013 Nov;68(11):1052–6. Troeger C, Forouzanfar M, Rao PC, Khalil I, Brown A, Swartz S, et al. Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory tract infections in 195 countries: a systematic analysis for the Global Burden of Disease Study 2015. 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Moleleki M, du Plessis M, Ndlangisa K, Reddy C, Hellferscee O, Mekgoe O, et al. Pathogens detected using a syndromic molecular diagnostic platform in patients hospitalized with severe respiratory illness in South Africa in 2017. Int J Infect Dis. 2022 Sep;122:389–97. Waldeck F, Lemmel S, Panning M, Käding N, Essig A, Rohde G, et al. Comparing viral, bacterial, and coinfections in community-acquired pneumonia: a retrospective cohort study. Int J Infect Dis. 2025 May;154:107841. Zhang L, Xiao Y, Zhang G, Li H, Zhao J, Chen M, et al. Identification of priority pathogens for aetiological diagnosis in adults with community-acquired pneumonia in China: a multicentre prospective study. BMC Infect Dis. 2023 Apr 14;23(1):231. Jain S, Self WH, Wunderink RG, Fakhran S, Balk R, Bramley AM, et al. Community-acquired pneumonia requiring hospitalization among U.S. adults. N Engl J Med. 2015 Jul 30;373(5):415–27. Malinis M, Myers J, Bordon J, Peyrani P, Kapoor R, Nakamatzu R, et al. Clinical outcomes of HIV-infected patients hospitalized with bacterial community-acquired pneumonia. Int J Infect Dis. 2010 Jan;14(1):e22–7. Prina E, Ranzani OT, Polverino E, Cillóniz C, Ferrer M, Fernandez L, et al. Risk factors associated with potentially antibiotic-resistant pathogens in community-acquired pneumonia. Ann Am Thorac Soc. 2015 Feb;12(2):153–60. Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYDOCUMENTS.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9003553","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607014375,"identity":"3e2720d1-ba5e-4887-9271-3547b95e45d1","order_by":0,"name":"Shahed Omar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACCQY2BgY2CQYG9gYgz8CCFC08B0BaJIjWAmIkQLgEgfzs5mcPPpRZyBvcfH51w48CCQb+9u4EvFoM7hwzN5xxTsJww+2csps9QIdJnDm7Ab8WiQQzad42iQSD2zlpN3iAWgwkcvFrkZ+R/k36L0jLzTNpN/8Qo4XhRo6ZNCNIyw32Y7eJssXgRk6ZZA/QLzPP5LDdljGQ4CHoF6DDtkn8KKuT5zt+/NnNN39s5Pjbewk4DAF4DMAkscpBgP0BKapHwSgYBaNgBAEAogpF33WVH3UAAAAASUVORK5CYII=","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":true,"prefix":"","firstName":"Shahed","middleName":"","lastName":"Omar","suffix":""},{"id":607014376,"identity":"77602ea7-e95e-4f97-b2ca-9ad8a149f69f","order_by":1,"name":"Taskeen Meeran","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Taskeen","middleName":"","lastName":"Meeran","suffix":""},{"id":607014377,"identity":"59d9a8de-5ba6-4a9a-809f-0b85964d6a4b","order_by":2,"name":"Tumelo Moloantoa","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Tumelo","middleName":"","lastName":"Moloantoa","suffix":""},{"id":607014378,"identity":"cd28bf1b-a160-41b1-b1b5-f7faf3a2133c","order_by":3,"name":"Pattamukkil Abraham","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Pattamukkil","middleName":"","lastName":"Abraham","suffix":""},{"id":607014379,"identity":"1c71a370-1a2f-4779-abb1-8a48de2ff896","order_by":4,"name":"Ebrahim Variava","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Ebrahim","middleName":"","lastName":"Variava","suffix":""},{"id":607014380,"identity":"86df55c2-70af-40d5-a636-00f258203913","order_by":5,"name":"Michelle Wong","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"","lastName":"Wong","suffix":""},{"id":607014381,"identity":"4468f685-f6df-4abf-ad8b-ad93bd6c6955","order_by":6,"name":"Firdaus Nabeemeeah","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Firdaus","middleName":"","lastName":"Nabeemeeah","suffix":""},{"id":607014382,"identity":"febb7894-0df3-4ec8-9b2c-e748556c1fc9","order_by":7,"name":"Neil Martinson","email":"","orcid":"","institution":"University of the Witwatersrand","correspondingAuthor":false,"prefix":"","firstName":"Neil","middleName":"","lastName":"Martinson","suffix":""}],"badges":[],"createdAt":"2026-03-01 19:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9003553/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9003553/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104994138,"identity":"698e2168-9a17-40b0-8ab6-c5a726c63da2","added_by":"auto","created_at":"2026-03-19 15:59:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63144,"visible":true,"origin":"","legend":"\u003cp\u003eCURB score distribution\t16% of the population met criteria for severe pneumoniae based on the CURB score ≥3.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9003553/v1/ef2dbbe3a4c0ce58ecae9a49.png"},{"id":104994137,"identity":"9d20bf1d-b693-45ab-85b3-4e24c3ca2065","added_by":"auto","created_at":"2026-03-19 15:59:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134199,"visible":true,"origin":"","legend":"\u003cp\u003ePathogen identification among all patients\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9003553/v1/ec29366ca0eedaada4f52077.png"},{"id":105035122,"identity":"8bed166a-e530-492d-835c-275dc805e577","added_by":"auto","created_at":"2026-03-20 07:25:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":120666,"visible":true,"origin":"","legend":"\u003cp\u003ePathogen identification PLWH.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9003553/v1/ce97530793d9ac377c980210.png"},{"id":104994135,"identity":"7eb32373-b290-4580-85d4-74ea00ce3d24","added_by":"auto","created_at":"2026-03-19 15:59:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":102470,"visible":true,"origin":"","legend":"\u003cp\u003ePathogen identification PWoH.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9003553/v1/c93fa9f9fb5efb6603404266.png"},{"id":108394897,"identity":"597c22d2-9d59-4746-97be-c93feec46e3c","added_by":"auto","created_at":"2026-05-04 07:40:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":798052,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9003553/v1/0beb2c0c-54a0-4758-bfb9-5aa1eefcd048.pdf"},{"id":105035183,"identity":"f54163a8-94ac-4343-9676-291ccff92680","added_by":"auto","created_at":"2026-03-20 07:25:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20473,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYDOCUMENTS.docx","url":"https://assets-eu.researchsquare.com/files/rs-9003553/v1/c73acbe3c77a597250da7576.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The use of multiplex PCR for the detection of respiratory pathogens in hospitalised adults with community-acquired pneumonia during the pre-COVID era in South Africa: A retrospective cohort","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eCommunity acquired pneumonia (CAP) is a major cause of hospitalization and mortality worldwide.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e_\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e According to World Health Organisation (WHO), lower respiratory tract infections were responsible for 2,5\u0026nbsp;million deaths in 2021, ranking among the top 5 causes of death.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Other important complications in addition to mortality include acute respiratory distress syndrome, sepsis, and multiorgan failure, all requiring additional resources in the intensive care unit.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e_\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCAP is a major contributor to the burden of disease in patients living with HIV (PLWH)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and in South Africa, PLWH are hospitalized with CAP.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eStreptococcus (S) pneumoniae\u003c/em\u003e is the leading cause of CAP worldwide and in PLWH who are at increased risk of severe pneumococcal disease, it remains an important cause of increased morbidity and mortality despite antiretroviral therapy.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Rapid, accurate identification of the causative pathogen may improve patient management and outcome and reduce the burden of disease in low-middle income countries (LMIC). The current epidemiology of hospitalised CAP is based on non- specific, low sensitivity sputum gram stain and standard culture based techniques with turnaround times of 48-72hours.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e_\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e By that time, patients may already have died, deteriorated or been discharged. Less than 40% hospitalized CAP patients have a microbiological diagnosis.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Empiric antibiotic therapy has been shown to be associated with a significant reduction in mortality,\u003csup\u003e14\u003c/sup\u003e but improved diagnostic testing for CAP allows switching to pathogen specific targeted treatment.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e_\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Antimicrobial appropriateness was found to be significantly better, with 80% of patients receiving result driven therapy in 2.3 hours compared to 29% in 46 hours with standard care and 42% of the patients had antibiotic de-escalation compared to 8% without MPPP, for hospitalised CAP.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRecent advances in molecular diagnostics, such as MPPP have demonstrated superior sensitivity (96.3%) and specificity (97.2%) with rapid turnaround times as little as 1 hour compared to 48\u0026ndash;72 hours for standard diagnostic methods in hospitalized CAP.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e_\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e MPPP allows for simultaneous detection of multiple respiratory pathogens from a single easy-to-collect respiratory sample, including pathogens that are difficult to culture or require specialized testing,\u003csup\u003e9\u003c/sup\u003e identifying bacterial agents in 50% of samples compared to only 14% with traditional methods. \u003csup\u003e11 12\u003c/sup\u003e The applicability and utility of these molecular diagnostics in a hospital setting with high HIV burden, where patients present with broader and more complex range of pathogens, remain underexplored.\u003c/p\u003e \u003cp\u003eIn this sub- study of the PotPrev trial,\u003csup\u003e8\u003c/sup\u003e we aimed to describe the spectrum of respiratory pathogens identified by MPPP in a cohort of adult patients with hospitalized CAP prior to the COVID pandemic. We further assessed the pathogen distribution by HIV status: patients living with HIV(PLWH) and patients without HIV (PWoH) and explored the association with in hospital length of stay (LOS) and mortality.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis study used specimens collected in 2019 from a prospective cohort study which recruited patients at two teaching hospitals in two provinces of South Africa: Gauteng and North West, with varying HIV prevalence.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e The hospitals included Chris Hani Baragwanath Academic Hospital and Klerksdorp/Tshepong Hospital Complex.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eApproval for the study was obtained from \u003cem\u003ethe Human Research Ethics Committee (Medical) [HREC] affiliated to the University of Witwatersrand, approval number [M171009]\u003c/em\u003e. Written informed consent was obtained from the patient or legal surrogate before enrolment.\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eWe included 146 specimens collected from adults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years; 59 females and 87 males who were admitted to the study hospitals for presumptive CAP between August 2019 and December 2019. The majority of the participants (107/146) were PLWH (73.3%), (23/87) were virally suppressed (26%) and of the 64% with non-supressed HIV viral load (VL), the median (IQR) VL was 50018(2498\u0026thinsp;\u0026minus;\u0026thinsp;175 750) copies/ml. Overall, the median (IQR) CD4\u0026thinsp;+\u0026thinsp;count for the PLWH was 159(37\u0026ndash;346) cells/mm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEligible patients had at least two of the following signs and symptoms: temperature\u0026thinsp;\u0026gt;\u0026thinsp;38\u0026deg;C (oral) or \u0026gt;\u0026thinsp;38.5\u0026deg;C (tympanic), hypothermia\u0026thinsp;\u0026lt;\u0026thinsp;35.5\u0026deg;C, pleuritic pain, cough, productive sputum, dyspnoea, respiratory rate\u0026thinsp;\u0026gt;\u0026thinsp;20/min, abnormal auscultatory findings or clinical/radiological evidence of lung consolidation, and resided in the relevant catchment area. Exclusion criteria included enrolment in a prior or current CAP vaccine or pneumonia treatment trials, absence of valid contact details, or inability to attend follow up after discharge.\u003c/p\u003e\n\u003ch3\u003eStudy Procedure and Variables\u003c/h3\u003e\n\u003cp\u003eDuring their admission, consented participants had detailed socio-demographic data collected via patient interview and clinical data relating to their admission abstracted from medical records. They inter-alia provided two sputum specimens. One was tested for tuberculosis on liquid mycobacterial culture and Xpert MTB/RIF Ultra and the second was stored in a repository at -80\u0026deg;C for future testing with the approval from the local Institutional Review Board. Whole blood (lytA Blood PCR) and nasopharyngeal swabs (lytA NPS PCR) were also collected for detection of \u003cem\u003eS. pneumonia\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eConvenient repository expectorated sputum samples were retrieved, defrosted to room temperature and analysed by the study team with BioFire\u0026reg; FilmArray\u0026reg; Pneumonia Plus Panel, following the manufacturer\u0026rsquo;s protocol. This assay utilizes nested PCR to identify 27 bacterial and viral species, along with 7 antimicrobial resistance genes, delivering results in roughly 60 minutes (Table sl 1). \u003csup\u003e20\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eStudy variables included, age and demographic data, admission vital signs, mean arterial pressure, room air and on oxygen saturation on pulse oximetry, SF ratio (Saturation to fraction of inspired oxygen), CURB score, HIV status at admission, prior antibiotic use before admission, microbiological results from routine diagnostic investigations during admission, additional PCR for \u003cem\u003eS. pneumoniae\u003c/em\u003e on NPS and blood and finally results of MPPP performed on stored sputum. Length of hospital stay as well as outcome of the admission (diagnosis at discharge and death) are reported.\u003c/p\u003e\n\u003ch3\u003eAim\u003c/h3\u003e\n\u003cp\u003eThe primary objective was a description of the respiratory pathogens detected by routine investigation and MPPP in adult patients admitted with CAP. Secondary objectives: to stratify pathogen distribution by HIV status, and to assess length of stay in hospital and mortality risk.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eWe used convenient samples of the first 146 patients recruited in the PotPrev study based on availability of BioFire\u0026reg; FilmArray\u0026reg; Pneumonia Plus Panel (one panel per patient) kits and the patients baseline characteristics on admission and CURB score are provided (Table 1 and Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: Baseline characteristics on admission\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll, N=146\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV-, N=39 Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV+, N=107 Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP (Mann Whitney U test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian Age(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e45 (35-58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e61 (50-67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e43 (34-52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian admission Temp\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026deg;\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e36.8 (36.4-37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e36.6(36.2-37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e36.8 (36.5-37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRR/min\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e20 (18-22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e20 (20-22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e20 (18-22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR/min\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e115 (101-129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e107 (92-119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e116 (106-130)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystolic Blood Pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e117 (104-135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e129 (110-143)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e115 (102-131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean Arterial Pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e85 (75-97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e91 (81-103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp; 83 (74-96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRoom Air Sp02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e90 (86-95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e86 (80-93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp; 91 (87-95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSF ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e429 (405-452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e407(381-445)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e433(419-452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCURB SCORE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1(0-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1(0-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1(0-3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe identified 275 pathogens among 119 of 146 patients included in our study, identifying a pathogen in 81.5% of hospitalized CAP (Figure 2). Twenty-seven (n= 27/146) patients had no pathogen detected on any of the test methodology. There were no gender differences in pathogen positivity rates, females 83.05% (n=49/59) and males 80.45% (n=70/87), p=0.692 (Table sI 2: Pathogens detected on multiplex PCR (MPPP) based on gender).\u003c/p\u003e\n\u003cp\u003eEighty percent of all positive results were due eight micro-organisms- 5 bacteria, 2 viruses and 1 fungus. The 5 most prevalent bacterial pathogens included \u003cem\u003eStreptococcus (S) pneumoniae, Hemophilus (H) influenzae, Staphylococcus aureus, Mycobacterium tuberculosis and Moraxella Catarrhalis\u003c/em\u003e, which together represented 70% of all micro-organisms detected. Fungi such as \u003cem\u003ePneumocystis Jirovecii Pneumonia (PJP)\u003c/em\u003e accounted for 7.3% of the micro-organisms found with Beta D glucan and mono-plex PCR for PJP. Two viruses \u003cem\u003eInfluenza A\u003c/em\u003e and \u003cem\u003eRhinovirus\u003c/em\u003e together made up 9.9% of pathogens detected.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eS. pneumoniae\u003c/em\u003e was the single most prevalent pathogen detected with 68 cases identified by at least one of the three methods used. The highest sensitivity was observed with lytA NPS PCR 91.2% (n=62/68). Multiplex PCR (mPCR) on sputum detected 67.6%(n=46/68), while lytA PCR from blood samples detected only 27.9% (n=19/68). Notably, mPCR contributed to the detection of 5 additional cases (7.4%) that were missed by NPS lytA PCR alone. Concordance analysis showed that 38 of the 62 NPS lytA-positive cases were also positive by mPCR, giving a concordance rate of 61.3%, while only 15 of the 61 NPS lytA-positive cases were also detected by blood lytA PCR (24.6%). These findings underscore the utility of lytA NPS PCR for detecting \u003cem\u003eS. pneumoniae\u003c/em\u003e, while also highlighting the complementary role of mPCR in identifying additional cases not captured by single-target PCR methods. The addition of blood lytA PCR for S. pneumoniae detected only one additional case.\u003c/p\u003e\n\u003cp\u003eAmong the 27 participants, 14 were clinically diagnosed with pneumonia and 13 having no pneumonia. PLWH comprised 57% (8/14) of the pneumonia group compared to 38.5% (5/13) of the non-pneumonia group. All participants in both groups tested negative for GeneXpert MTB/RIF and were clinically assessed as negative for Pneumocystis jirovecii pneumonia. Urine LAM testing was negative in all tested participants, occurring in 10 individuals in the pneumonia group and 5 in the non-pneumonia group. No deaths occurred in the pneumonia group, whereas mortality was observed in 15.4% (2/13) of those without pneumonia. Alternative diagnoses in the non-pneumonia group included chronic lung disease (38.5%), cardiac disease (23%), acute asthma (7.7%), restrictive lung disease (7.7%), toxin-induced lung disease (7.7%), cervical cancer (7.7%), and no identifiable cause in 7.7% of cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary objectives:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathogen distribution and HIV status\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo hundred and twenty-eight pathogens were identified in 107 PLWH, while only 47 pathogens were identified in 39 PWoH. One or more pathogens were detected in 94/107 (87.85%) PLWH, compared to 25/39 (64,10%) PWoH, resulting in a relative risk of pathogen detection in PLWH of 1.64 (95% CI 1.22-2.21), p= 0.001 (Chi squared 10.6) (Figure 3 and 4).\u003c/p\u003e\n\u003cp\u003eWe compared PLWH and PWoH with respect to proportions of bacteria, viruses and \u003cem\u003ePJP\u0026nbsp;\u003c/em\u003eand \u003cem\u003eMTB\u003c/em\u003e. Only PJP was significantly higher in PLWH compared to PWoH. Organisms causing opportunistic infections were more commonly picked up in PLWH as expected (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2: Pathogen groups among patients stratified by HIV status.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathogens\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV positive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV negative\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP (Chi-square)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eBacteria\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e160(70.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e36(76.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eTuberculosis (TB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e20(8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e5(10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003ePJP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e20(8.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e#0.03 (Fisher exact)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eVirus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e28(12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e6(12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 147px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eLength of stay, HIV status and mortality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne patient had no LOS and mortality data recorded and was excluded. The median length of stay (LOS) for the remaining cohort was six-days. There were no significant differences in LOS between PLWH and PWoH. There was also no significant difference in LOS between survivors and non-survivors (Table 3).\u003c/p\u003e\n\u003cp\u003eTable 3: Length of Stay\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Median days (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP (Mann Whitney U)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e6 (4-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePLWH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e6 (4-11)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePWoH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e6 (4-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvivors n=132\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e6 (4-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Survivors n= 12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 196px;\"\u003e\n \u003cp\u003e5.5 (4-13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOverall mortality was 8.21%, with no significant difference between PLWH and PWoH, Chi squared test = 0.28 (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: HIV status and mortality\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV TEST RESULT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvivors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon -Survivors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e35 (89.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e4 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e98 (92.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e8 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eEleven of the twelve non-survivors died in hospital, one patient with chronic lung disease died out of hospital. There were different causes of death amongst PLWH and PWoH. Amongst PLWH, three patients died of \u003cem\u003ePJP\u003c/em\u003e, three due to pneumonia unspecified, one to \u003cem\u003eMTB\u003c/em\u003e and one to sepsis. For PWoH, one died from chronic lung disease, one from a malignancy and one from pneumonia unspecified. Table 5 shows clinical characteristics between survivors and non survivors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5: Clinical characteristics of survivors and non survivors\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll, N=146\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvivors, N=133 Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon survivors, N=12 Median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP (Mann Whitney U test)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e45 (35-58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e45 (35-58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e51.5 (34-55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemp\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026deg;\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e36.8 (36.4-37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e36.8 (36.4-37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e36.8 (36.4-37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRR/min\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e20 (18-22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e20 (18-22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e21 (20-25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.03*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDelta Sp02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e4.5 (0-9), n=46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e5 (0-10), n=41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0 (-23-9), n=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 602px;\"\u003e\n \u003cp\u003eDelta Sp02 refers to the increase in Sp02 once oxygen therapy was provided.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA Sp02\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e90 (86-95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e90 (86-95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e89 (89-95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSp02 on 0\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e94 (91-98), n=49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e94 (91-98) n=44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e89 (89-95), n=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSF ratio\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e429 (405-452)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e429 (405-452), n=101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e424 (400-438), n=7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR/min\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e115 (101-129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e115 (101-129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e111 (107-119.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSBP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e117 (104-135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e117 (103-135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e116 (109.5-132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e85 (75-97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e85 (74-98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e84 (78-87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe found no significant differences in pathogen groups (bacteria, \u003cem\u003eMTB\u003c/em\u003e, \u003cem\u003ePJP\u003c/em\u003e and viruses) identified between survivors and non-survivors. Risk of death from \u003cem\u003ePJP\u003c/em\u003e compared to all other pathogens was 3.98(95% CI 1.63-9.72) p=0.002 (Table 6).\u003c/p\u003e\n\u003cp\u003eTable 6: Pathogen groups among survivors and non-survivors\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of organism\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvivors, n=134\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Survivors n=12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBacteria -N, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e184 (66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e12 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTuberculosis- N, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e24 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e1 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePneumocystis Jirovecii -N, %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e15 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e5 (23,8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003cp\u003eX\u003csup\u003e2 =\u0026nbsp;\u003c/sup\u003e9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVirus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e31 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 149px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eUsing logistic regression analysis to predict survival, we included variables from the univariate analysis including CURB 65 score, saturation on oxygen, respiratory rate, mean arterial pressure and presence of \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e (Table 7) and \u003cem\u003ePJP\u003c/em\u003e with a p\u0026lt;0.2 and included CURB 65 and saturation on oxygen at admission into the final model. Only the saturation of oxygen was associated with mortality (OR 0.876, 95% CI 0.773-0.993) p=0.039. An improved saturation above 94(91-98) % after administration of oxygen was associated with reduced mortality. Among the non-survivors, one patient had \u003cem\u003eMERS CoV\u003c/em\u003e (middle east respiratory system coronavirus).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 7: Individual pathogens associated with survival and non-survival.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvivors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon survivors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP, X\u003csup\u003e2\u003c/sup\u003e or Fisher exact\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKlebsiella pneumonia +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e5 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e2 (1,96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003ep=0,035, X2= 4.8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFisher exact 0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKlebsiella pneumonia -\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e249 (98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e19 (90.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMERS Corona virus +\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 139px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e1 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 169px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur findings demonstrate a high pathogen detection rate from a single sputum specimen in ~\u0026thinsp;80% of patients hospitalized with presumptive CAP in South Africa, with the use of MPPP in addition to routine diagnostic testing. This highlights MPPP enhanced sensitivity (96.3%)\u003csup\u003e11_12 20\u003c/sup\u003e and specificity (97.2%)\u003csup\u003e11_12 20\u003c/sup\u003e and rapid turnaround time of about one hour with two minutes of hands-on time, compared to 48\u0026ndash;72 hours for standard-of-care results\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e_\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Similar high detection rates have been reported in retrospective cohorts globally, from high to low income countries (HLIC) including 98% in Turkey, \u003csup\u003e21\u003c/sup\u003e 87% in United Kingdom(UK),\u003csup\u003e19\u003c/sup\u003e 83% in South Africa,\u003csup\u003e22\u003c/sup\u003e 60\u0026ndash;70% increase in pathogen detection in Uganda,\u003csup\u003e9\u003c/sup\u003e Germany,\u003csup\u003e23\u003c/sup\u003e China, \u003csup\u003e24\u003c/sup\u003e and in USA.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e These findings have consistently outperformed standard culture methods,\u003csup\u003e11\u003c/sup\u003e which are typically associated with significantly lower detection rates(23.4%-60%),\u003csup\u003e19 21 22\u003c/sup\u003e detecting bacterial pathogens in only 14% of patient samples compared to 50% with MPPP.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Our data, in keeping with other MPPP studies, support this improved performance compared to standard culture in our setting. Variations in MPPP detection rates on sputum globally may reflect differences in HIV prevalence, presence of other co-morbidities, sputum quality, study design and sample size.\u003c/p\u003e \u003cp\u003eAmong the 228 pathogens identified in our study, as expected, \u003cem\u003eS. pneumoniae\u003c/em\u003e (SP) and \u003cem\u003eH. influenzae\u003c/em\u003e (HI) accounted for more than one-third of all pathogens detected. Other studies from HLIC similarly highlight the predominance of these pathogens. In the UK and in Turkey, SP and HI co-infection occurred in 40.2% \u003csup\u003e19\u003c/sup\u003e and 36% \u003csup\u003e21\u003c/sup\u003e of patients respectively. A South African study in 2022 also reported these two predominant pathogens as possible aetiology of CAP (35%).\u003csup\u003e22\u003c/sup\u003e Our findings confirm that SP and HI remain the major contributors to CAP- related hospitalizations and deaths globally.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eTen percent of all detected pathogens were viruses in our study. A Kenyan study reported viral pathogens in nearly half of the CAP cases (48%) with Influenza A being the most commonly identified virus,\u003csup\u003e15\u003c/sup\u003e while a study from Turkey and USA reported rhinovirus as the most frequent respiratory virus (20%).\u003csup\u003e21 25\u003c/sup\u003e Our data is in keeping with these studies identifying rhinovirus and Influenza A as the two common viral pathogens, underscoring the value of MPPP for comprehensive pathogen detection in LMIC.\u003c/p\u003e \u003cp\u003eWe detected \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e (MP) in about 1% of cases. Higher detection rates for MP were found in Turkey (6%) \u003csup\u003e21\u003c/sup\u003e and in a large multicentre study from China with almost 11% of cases.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e It is possible that the small sample size in our study may have resulted in an underestimation, however given that the study done in Turkey was half the size of our study, regional differences cannot be discounted.\u003c/p\u003e \u003cp\u003eStratification by HIV status further revealed significant differences in pathogen profiles, emphasizing the impact of immunosuppression on aetiology of CAP in PLWH.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Venturas et al. reported similar poly-microbe profiles in PLWH at another academic hospital in South Africa.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e As shown by our study, MPPP provide insight into the diagnostic value of these molecular diagnostics in a high HIV prevalent setting. A recent Ugandan study on 107 PLWH admitted with hospitalised CAP, also demonstrated a high pathogen detection rate (83.2% bacterial) and (49,5% viral) using MPPP.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e No significant differences in bacterial (non-MTB) and viral pathogen groups were found between PLWH and PWoH.\u003c/p\u003e \u003cp\u003eUnsurprisingly, opportunistic infections such as PJP(7.3%) and M tuberculosis(MTB) (9.1%) were more prevalent in PLWH compared to PWoH as causes of CAP leading to admission. The underlying immune status profoundly influences the infectious aetiology of respiratory illness. The higher frequency of PJP and MTB among PLWH suggests a potential need to expand MPPP to include MTB and PJP in LMICs with high HIV prevalence, particularly among severely immunocompromised individuals. Implementing such targeted diagnostics may enable timely and precise treatment decisions.\u003c/p\u003e \u003cp\u003eDespite the difference in pathogen distribution, we found no significant difference in the LOS or in- hospital mortality between PLWH and PWoH, reinforcing prior findings from international CAPO cohorts that clinical outcomes, including time to clinical stability, LOS, and mortality are comparable across HIV status. \u003csup\u003e26\u003c/sup\u003e Co- infection with viral pathogens has been suggested to be associated with longer LOS,\u003csup\u003e24\u003c/sup\u003e but this was not demonstrated. Only the presence of low saturation on oxygen was independently associated with reduced survival. Multidrug resistant pathogen, hypoxia (a condition characterized by an insufficient supply of oxygen to body tissues) and prior antibiotic use was associated with 30-day mortality.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eMultiplex PCR pneumonia panels in addition to standard diagnostic methods results in enhanced respiratory pathogen detection in adults hospitalized with CAP. PLWH had a higher pathogen burden with PJP and MTB significantly contributing as causes of CAP. Our findings highlight the diagnostic value of expanded molecular tests on different specimens and the need for specific approaches adapted to managing hospitalised community acquired pneumonia especially with a resource constrained setting with a high HIV burden.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSTRENGTHS AND LIMITATIONS\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003eStrengths\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe use of samples from a rigorously conducted clinical trial, allowing for integration of detailed clinical data with advanced molecular diagnostics. Additionally, stratification by HIV status provides important epidemiological insights.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eLimitations\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eOur study was limited to CAP patients able to produce sputum, which may be contaminated by oropharyngeal flora. Less contamination-prone specimens such as bronchoalveolar lavage require specialized expertise and are not routinely obtained in non-intubated patients. Interpretation of MPPP results from non-sterile, stored sputum samples is further complicated by potential contamination and nucleic acid degradation, highlighting the need for prospective studies using fresh specimens. Finally, because clinical management was not guided by MPPP results, this retrospective study cannot assess the impact of these diagnostics on patient outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRECOMMENDATIONS FOR FUTURE RESEARCH\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eProspective studies evaluating real-time MPPP use on fresh sputum samples and the impact on clinical decision-making and patient outcomes.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInvestigations into pathogen load, co-infection dynamics, and host immune response, particularly in PLWH.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecommunity acquired pneumonia\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLWH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epatients living with HIV\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePWOH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epatients without HIV\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman immunodeficiency virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eStreptococcus\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003ehaemophilus\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eMPPP\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emultiplex PCR pneumonia panels\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epolymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elength of stay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emPCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiplex polymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enasopharyngeal swab\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organisation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-middle income countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elength of stay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eviral load\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTB/RIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e/ resistance to rifampicin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePJP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e \u003cem\u003ePneumocystis Jirovecii Pneumonia\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHLIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh to low income country\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited Kingdom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was reviewed and approved by the following Ethical review board: \u0026nbsp;\u003cem\u003eHuman Research Ethics Committee (Medical) [HREC] affiliated to the University of Witwatersrand, approval number [M171009]\u003c/em\u003e.\u0026nbsp;The approval date was 27 Oct 2017\u0026nbsp;(reference: 171009). The University of the Witwatersrand (Wits) Human Research Ethics Committee (HREC), both Medical and Non-Medical, operates in accordance with the Declaration of Helsinki. Its Standard Operating Procedures (SOPs) expressly incorporate the Declaration of Helsinki, alongside ICH-GCP and South African national guidelines, to ensure ethical research standards.\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from the patient or legal surrogate before enrolment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.’\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn electronic copy of de-identified data is available on request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBioMerieux provided theBiofire pneumonia panel kits required for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSO conceived the study, designed the protocol, data analysis, and co-drafted the manuscript. TMee assisted with data analysis and co-drafted the manuscript. TMol, PA, EV, and MW made substantial contributions to the work and critically reviewed the manuscript. FN contributed to the ethics application and manuscript preparation. NM contributed to protocol design and manuscript preparation. All authors approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the PotPrev Trial team for making this sub-study possible and acknowledge all members that assisted with the co-ordination of activities during the study. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZar HJ, Madhi SA, Aston SJ, Gordon SB. Pneumonia in low- and middle-income countries: progress and challenges. \u003cstrong\u003eThorax.\u003c/strong\u003e 2013 Nov;68(11):1052\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eTroeger C, Forouzanfar M, Rao PC, Khalil I, Brown A, Swartz S, et al. Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory tract infections in 195 countries: a systematic analysis for the Global Burden of Disease Study 2015. \u003cstrong\u003eLancet Infect Dis.\u003c/strong\u003e 2017 Nov;17(11):1133\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003ePalomeque A, Cilloniz C, Soler-Comas A, Canseco-Ribas J, Rovira-Ribalta N, Motos A, et al. A review of the value of point-of-care testing for community-acquired pneumonia. \u003cstrong\u003eExpert Rev Mol Diagn.\u003c/strong\u003e 2024 Aug;24(8):729\u0026ndash;42.\u003c/li\u003e\n\u003cli\u003eVenturas J, Titus A, Richards G, Feldman C. Severe community-acquired pneumonia: impact of HIV on clinical presentation, microbiological and laboratory findings, and outcome. \u003cstrong\u003eJ Intensive Care Med.\u003c/strong\u003e 2025 Jul 16;08850666251359546.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. The top 10 causes of death [Internet]. Geneva: WHO; 2024 [cited 2025 Jul 29]. Available from: https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death\u003c/li\u003e\n\u003cli\u003eVaughn VM, Dickson RP, Horowitz JK, Flanders SA. Community-acquired pneumonia: a review. \u003cstrong\u003eJAMA.\u003c/strong\u003e 2024 Sep 16;332(15):1282\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eCohen C, Walaza S, Moyes J, Groome M, Tempia S, Pretorius M, et al. Epidemiology of severe acute respiratory illness among adults and children aged \u0026ge;5 years in a high HIV-prevalence setting, 2009\u0026ndash;2012. \u003cstrong\u003ePLoS One.\u003c/strong\u003e 2015 Feb 23;10(2):e0117716.\u003c/li\u003e\n\u003cli\u003eNabeemeeah F, Sabet N, Otwombe K, Hlongwane K, Mlambo LM, Moloantoa T, et al. Cohort profile: the potentially preventable burden of community-acquired pneumonia in South African adults in the era of widespread PCV13 immunisation and antiretroviral therapy rollout, before and during the COVID-19 pandemic\u0026mdash;the multicentre, multimethod PotPrev study. \u003cstrong\u003eBMJ Open.\u003c/strong\u003e 2024 Dec;14(12):e080553.\u003c/li\u003e\n\u003cli\u003eWorodria W, Andama A, Sanyu I, Orit D, Kwizera R, Sessolo A, et al. Molecular diagnostics improve the yield of diagnosis of community-acquired pneumonia and multidrug-resistant pathogens in hospitalised patients with HIV in a low-income setting. \u003cstrong\u003eAfr J Thorac Crit Care Med.\u003c/strong\u003e 2025 Jun 4; e2415.\u003c/li\u003e\n\u003cli\u003eEl-Nawawy AA, Antonios MA, Tawfik ME, Meheissen MA. Comparison of a point-of-care FilmArray test to standard-of-care microbiology testing in the diagnosis of healthcare-associated infections in a tertiary care paediatric intensive care unit. \u003cstrong\u003eAntibiotics (Basel).\u003c/strong\u003e 2022 Mar 27;11(4):453.\u003c/li\u003e\n\u003cli\u003eMurphy CN, Fowler R, Balada-Llasat JM, Carroll A, Stone H, Akerele O, et al. Multicenter evaluation of the BioFire FilmArray Pneumonia/Pneumonia Plus Panel for detection and quantification of agents of lower respiratory tract infection. \u003cstrong\u003eJ Clin Microbiol.\u003c/strong\u003e 2020 Jul;58(7):e00128-20.\u003c/li\u003e\n\u003cli\u003eMitton B, Rule R, Said M. Laboratory evaluation of the BioFire FilmArray Pneumonia Plus Panel compared with conventional methods for identification of bacteria in lower respiratory tract specimens: a prospective cross-sectional study from South Africa. \u003cstrong\u003eDiagn Microbiol Infect Dis.\u003c/strong\u003e 2021 Feb;99(2):115236.\u003c/li\u003e\n\u003cli\u003eDessajan J, Timsit JF. Impact of multiplex PCR in the therapeutic management of severe bacterial pneumonia. \u003cstrong\u003eAntibiotics (Basel).\u003c/strong\u003e 2024 Jan 18;13(1):95.\u003c/li\u003e\n\u003cli\u003eSeo C, Corrado M, Lim R, Thornton CS. Guideline-concordant therapy for community-acquired pneumonia in the hospitalized population: a systematic review and meta-analysis. \u003cstrong\u003eOpen Forum Infect Dis.\u003c/strong\u003e 2024 Jun 28;11(7):ofae336.\u003c/li\u003e\n\u003cli\u003eNambafu J, Achakolong M, Mwendwa F, Bwika J, Riunga F, Gitau S, et al. A prospective observational study of community-acquired pneumonia in Kenya: the role of viral pathogens. \u003cstrong\u003eBMC Infect Dis.\u003c/strong\u003e 2021 Dec;21(1):639.\u003c/li\u003e\n\u003cli\u003ePoole S, Tanner AR, Naidu VV, Borca F, Phan H, Saeed K, et al. Molecular point-of-care testing for lower respiratory tract pathogens improves safe antibiotic de-escalation in patients with pneumonia in the ICU: results of a randomised controlled trial. \u003cstrong\u003eJ Infect.\u003c/strong\u003e 2022 Dec;85(6):625\u0026ndash;33.\u003c/li\u003e\n\u003cli\u003eZacharioudakis IM, Zervou FN, Dubrovskaya Y, Inglima K, See B, Aguero-Rosenfeld M. Evaluation of a multiplex PCR panel for the microbiological diagnosis of pneumonia in hospitalized patients: experience from an academic medical center. \u003cstrong\u003eInt J Infect Dis.\u003c/strong\u003e 2021 Mar;104:354\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eVirk A, Strasburg AP, Kies KD, Donadio AD, Mandrekar J, Harmsen WS, et al. Rapid multiplex PCR panel for pneumonia in hospitalised patients with suspected pneumonia in the USA: a single-centre, open-label, pragmatic, randomised controlled trial. \u003cstrong\u003eLancet Microbe.\u003c/strong\u003e 2024 Dec;5(12):e100928.\u003c/li\u003e\n\u003cli\u003eGadsby NJ, Russell CD, McHugh MP, Mark H, Conway Morris A, Laurenson IF, et al. Comprehensive molecular testing for respiratory pathogens in community-acquired pneumonia. \u003cstrong\u003eClin Infect Dis.\u003c/strong\u003e 2016 Apr 1;62(7):817\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003ebioM\u0026eacute;rieux. BIOFIRE\u0026reg; FILMARRAY\u0026reg; Pneumonia (PN) Panel [Internet]. Marcy-l\u0026rsquo;\u0026Eacute;toile: bioM\u0026eacute;rieux; 2025 [cited 2025 Oct 15]. Available from: https://www.biomerieux.com/us/en/our-offer/clinical-products/biofire-pneumonia-panel.html\u003c/li\u003e\n\u003cli\u003e\u0026Ccedil;ağlayan Serin D, Pulluk\u0026ccedil;u H, \u0026Ccedil;i\u0026ccedil;ek C, Sipahi OR, Taşbakan S, Atalay S. Bacterial and viral etiology in hospitalized community-acquired pneumonia evaluated with molecular methods. \u003cstrong\u003eJ Infect Dev Ctries.\u003c/strong\u003e 2014 Apr 15;8(4):510\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eMoleleki M, du Plessis M, Ndlangisa K, Reddy C, Hellferscee O, Mekgoe O, et al. Pathogens detected using a syndromic molecular diagnostic platform in patients hospitalized with severe respiratory illness in South Africa in 2017. \u003cstrong\u003eInt J Infect Dis.\u003c/strong\u003e 2022 Sep;122:389\u0026ndash;97.\u003c/li\u003e\n\u003cli\u003eWaldeck F, Lemmel S, Panning M, K\u0026auml;ding N, Essig A, Rohde G, et al. Comparing viral, bacterial, and coinfections in community-acquired pneumonia: a retrospective cohort study. \u003cstrong\u003eInt J Infect Dis.\u003c/strong\u003e 2025 May;154:107841.\u003c/li\u003e\n\u003cli\u003eZhang L, Xiao Y, Zhang G, Li H, Zhao J, Chen M, et al. Identification of priority pathogens for aetiological diagnosis in adults with community-acquired pneumonia in China: a multicentre prospective study. \u003cstrong\u003eBMC Infect Dis.\u003c/strong\u003e 2023 Apr 14;23(1):231.\u003c/li\u003e\n\u003cli\u003eJain S, Self WH, Wunderink RG, Fakhran S, Balk R, Bramley AM, et al. Community-acquired pneumonia requiring hospitalization among U.S. adults. \u003cstrong\u003eN Engl J Med.\u003c/strong\u003e 2015 Jul 30;373(5):415\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eMalinis M, Myers J, Bordon J, Peyrani P, Kapoor R, Nakamatzu R, et al. Clinical outcomes of HIV-infected patients hospitalized with bacterial community-acquired pneumonia. \u003cstrong\u003eInt J Infect Dis.\u003c/strong\u003e 2010 Jan;14(1):e22\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003ePrina E, Ranzani OT, Polverino E, Cill\u0026oacute;niz C, Ferrer M, Fernandez L, et al. Risk factors associated with potentially antibiotic-resistant pathogens in community-acquired pneumonia. \u003cstrong\u003eAnn Am Thorac Soc.\u003c/strong\u003e 2015 Feb;12(2):153\u0026ndash;60.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Community- acquired pneumonia, Multiplex PCR, BioFire® FilmArray® Pneumonia Panel, Molecular diagnostics, Respiratory Pathogens, Streptococcus Pneumonia, HIV- associated pneumonia, Hospitalised adults","lastPublishedDoi":"10.21203/rs.3.rs-9003553/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9003553/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCommunity-acquired pneumonia (CAP) is a major cause of hospitalisation. In South Africa 74% of adults hospitalised with CAP are patients living with HIV (PLWH). \u003cem\u003eStreptococcus(S) pneumoniae\u003c/em\u003e remains a leading cause of CAP in PLWH. Rapid and accurate pathogen identification has significant management implications. The objectives of the study is to: describe respiratory pathogens detected by the BioFire\u0026reg; FilmArray\u0026reg; multiplex PCR pneumonia panels (MPPP) and compare \u003cem\u003eS. pneumoniae\u003c/em\u003e detection with standard diagnostics in adults admitted with CAP. Secondary objectives were to stratify pathogens by HIV status and assess length of stay (LOS) and mortality risk.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eStored sputum specimens from the PotPrev trial collected between August to December 2019, pre-covid era, were analysed using MPPP. \u003cem\u003eS. pneumoniae\u003c/em\u003e identification was made by three separate methods: multiplex PCR (mPCR) on sputum, single-plex PCR on nasopharyngeal swab (NPS) and blood.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn 146 samples analysed, MPPP detected 275 pathogens in 119 patients (81.5%). Seven organisms accounted for 80% of pathogens: \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e (24.7%), \u003cem\u003eHaemophilus influenzae\u003c/em\u003e (17.8%), \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (9.1%), \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (9.1%), \u003cem\u003eMoraxella catarrhalis\u003c/em\u003e (8.4%), Rhinovirus (5.5%) and Influenza virus (4.4%). Among 68 \u003cem\u003eS. pneumoniae\u003c/em\u003e cases, NPS lytA PCR had the highest sensitivity (91.2%), followed by mPCR (67.6%) and blood lytA PCR (27.9%). Of note, mPCR detected 7.4% additional cases. Concordance between NPS lytA PCR and mPCR was 61.3%, and blood lytA PCR 24.6%. Among PLWH, a wider spectrum of pathogens was observed, with Pneumocystis jirovecii pneumonia accounting for a high proportion of cases (8.7%). There were no differences in bacterial (p\u0026thinsp;=\u0026thinsp;0.37), viral (p\u0026thinsp;=\u0026thinsp;0.5), mortality (p\u0026thinsp;=\u0026thinsp;0.6), or length of stay (median six days, p\u0026thinsp;=\u0026thinsp;0.96) in PLWH. Survivors and non-survivors had similar LOS (p\u0026thinsp;=\u0026thinsp;0.68). Low oxygen saturation was associated with reduced survival (p\u0026thinsp;=\u0026thinsp;0.039).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMPPP in addition to standard diagnostics increases pathogen detection in hospitalized CAP. PLWH had distinct aetiology with a higher pathogen burden. These findings support the value of molecular diagnostics and context-specific approaches in high HIV-burden settings.\u003c/p\u003e","manuscriptTitle":"The use of multiplex PCR for the detection of respiratory pathogens in hospitalised adults with community-acquired pneumonia during the pre-COVID era in South Africa: A retrospective cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 15:59:03","doi":"10.21203/rs.3.rs-9003553/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"23af7843-f440-4a09-b7c2-8da02f71b71f","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T07:40:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 15:59:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9003553","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9003553","identity":"rs-9003553","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}