Stepwise model to differentiate pathogenic from non-pathogenic organisms in lower-respiratory isolates: Effectiveness and feasibility prospective cohort study

Abstract

BACKGROUND

Lower respiratory tract infections remain a major cause of morbidity and mortality among hospitalized patients. However, isolating organisms from respiratory samples often leads to diagnostic uncertainty due to the coexistence of colonizers, commensals, and contaminants. To address this challenge, this study employed a structured, stepwise exploratory model to differentiate true pathogens from non-pathogens in aerobic respiratory cultures and multiplex polymerase chain reaction (Biofire FilmArray) results.

AIM

To determine pathogen vs non-pathogen in lower respiratory tract isolates.

METHODS

This prospective, longitudinal time-bound study was conducted over three months (August 2024 to October 2024) at a tertiary care center in Northern India. Adult patients (≥ 18 years) with positive lower respiratory tract samples were enrolled. Each isolate was independently classified by the treating clinician, microbiologist, and study investigator using a six-step clinical-microbiological algorithm that incorporated clinical signs, Sequential Organ Failure Assessment score trends, alternative infection sources, host factors, and outcome data. The final classification was determined by the investigator. Outcomes, including treatment response and mortality at 28 days, were compared across pathogen and non-pathogen groups.

RESULTS

Of the 145 included patients, 131 (90.3%) were classified as pathogens and 14 (9.7%) as non-pathogens. Cohen’s Kappa between investigator and microbiologist classifications was 0.28, indicating fair agreement. Among pathogen cases, 68 (51.9%) responded to treatment. In contrast, 12 of 14 non-pathogen cases (85.7%) were not treated, with favorable outcomes in most, and only one unrelated death (7.1%).

CONCLUSION

The structured clinico-microbiological model strongly correlates with treatment outcomes, making it useful for differentiating infection from colonization. Crucially, microbiological detection alone doesn’t determine pathogenicity. Integrating clinical, laboratory, and outcome data is essential for rational antibiotic use and effective antimicrobial stewardship.

Key Words: Aerobic respiratory culture; Biofire FilmArray; Pathogen classification; Lower respiratory tract infections; Antimicrobial stewardship; Sequential Organ Failure Assessment score

Core Tip: This prospective cohort study developed a structured clinico-microbiological model to distinguish true pathogens from colonizers in lower respiratory tract infections. Among 145 cases, 90% were identified as pathogens, with treatment outcomes aligning closely with the model’s classifications. The findings highlight that microbiological detection alone is insufficient for diagnosing infection - integrating clinical context and outcome data is crucial for rational antibiotic use and effective antimicrobial stewardship.

INTRODUCTION

Lower respiratory tract infections impose a substantial clinical and economic burden on hospitals; however, accurately determining whether a detected organism is causing an active infection remains challenging[1,2]. Respiratory samples often contain commensals, colonizers, and misclassification can result in inappropriate antibiotic use, increased antimicrobial resistance, and unnecessary healthcare costs[3,4].

Numerous studies have highlighted the pitfalls of equating microbial growth with infection, especially in ventilated or critically ill patients. Labelle et al[5] observed that 34% of patients with healthcare-associated pneumonia grew only colonizing flora, yet over 71% still received antibiotics, with no significant difference in mortality between culture-positive and culture-negative groups.

The advent of rapid molecular diagnostics, such as multiplex polymerase chain reaction (PCR) (e.g., Biofire FilmArray), has improved organism detection and reduced turnaround time, enabling clinicians to initiate early, targeted therapy. However, their high analytical sensitivity also introduces diagnostic noise by detecting low-level colonizers, residual nucleic acids from non-viable organisms, and organisms of uncertain significance. Without a clinical context, such high-sensitivity tools risk overdiagnosis and the misuse of antibiotics[6,7].

While landmark studies support the use of invasive sampling to guide antibiotic therapy, confirming the pathogenicity of isolated organisms remains a challenge. In patients with prior antibiotic exposure, biofilm formation, or prolonged intubation, culture positivity may reflect colonization rather than true infection, raising concerns about over-reliance on microbiological results alone[8,9].

This study aimed to differentiate true respiratory pathogens from non-pathogens (colonizers, commensals, and contaminants) in hospitalized adults using a stepwise explorative model that incorporates clinical features, Sequential Organ Failure Assessment (SOFA) score trends, risk factors for colonization, and outcome data. By correlating pathogen classification with treatment response and 28-day outcomes, this approach aims to improve diagnostic accuracy and antimicrobial stewardship in resource-limited, high-burden settings. To systematically classify organisms isolated from lower respiratory tract samples as pathogenic or non-pathogenic using a structured stepwise clinico-microbiological model, and to evaluate the clinical outcomes, including treatment response and 28-day mortality, associated with these classifications.

MATERIALS AND METHODS

Study design and setting

This prospective observational cohort study was conducted at a 1000-bedded tertiary care teaching hospital in Northern India from August 1, 2024 to October 31, 2024, to classify organisms isolated from lower respiratory samples using a structured stepwise model. Consecutive positive lower respiratory isolates (sputum, bronchoalveolar lavage, and endotracheal/tracheal aspirates) were identified through daily screening rounds performed jointly by the investigator and microbiologist in the microbiology department. Patients’ SOFA scores were collected. Patients were followed until discharge or for a maximum of 28 days to determine the outcomes, including response to treatment and mortality. As the model required integration of clinical evolution, the investigator was not blinded, and final classification of pathogenicity was determined after reviewing the patient’s treatment response and 28-day outcomes. Institutional Ethics Committee approval was obtained prior to the initiation of the study.

Structured stepwise model for organism classification

Whenever a culture or Biofire PCR tested positive and met inclusion criteria, the organism underwent a six-step clinico-microbiological algorithm to determine its pathogenicity by the investigator team (Figure 1). The SOFA score was calculated using standard criteria based on six organ systems: Respiratory, cardiovascular, hepatic, coagulation, renal, and neurological parameters, as per established guidelines.

Figure 1 Structured stepwise model for organism classification (Supplementary Tables 1-6). LRTI: Lower respiratory tract infection; SOFA: Sequential Organ Failure Assessment; RTI: Respiratory tract infection; NP: Non-pathogenic.

Outcome measures

The primary outcome measure was to determine the proportion of pathogens and non-pathogens in aerobic respiratory culture/multiplex PCR in a stepwise model. The secondary outcome measure was to determine the identified organisms as pathogenic colonizers, non-pathogenic contaminants, or non-pathogenic colonizers. Once the pathogenicity of the isolated organism was determined, patients were followed prospectively until discharge or up to 28 days, whichever occurred earlier, to evaluate clinical outcomes. Based on the clinical course and treatment decisions, outcomes were classified into four categories: (1) Improved with treatment, applicable to patients with pathogenic organisms who showed clinical recovery following targeted antimicrobial therapy; (2) No response to treatment, representing pathogenic cases where appropriate therapy failed to produce clinical improvement; (3) Not treated, defined for non-pathogenic isolates where no specific antimicrobial therapy was initiated; and (4) Treatment initiated for non-pathogenic organisms. This categorization allowed objective assessment of the appropriateness of pathogen classification and its correlation with patient outcomes.

Statistical analysis

Data entry was performed using Microsoft Excel, and statistical analysis was conducted using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, United States). The collected data were analyzed using a combination of descriptive and inferential statistical techniques. Continuous variables were expressed as mean ± SD or median with interquartile range, depending on the distribution assessed. The normality of continuous variables was verified using both visual and statistical methods. Following this, either independent samples t-tests (for normally distributed variables) or Wilcoxon-Mann-Whitney U tests (for non-parametric comparisons) were employed to assess group differences. Categorical variables were summarized as n (%), and comparisons between groups were performed using the χ² test or Fisher’s exact test, as appropriate. To assess agreement between categorical variables, particularly in organism classification by microbiologists vs investigators, Cohen’s Kappa statistic was calculated. The strength of the associations was further evaluated using effect size metrics, such as Cramer’s V, Bias-corrected Cramer’s V, and point-biserial correlation, for appropriate pairings. P < 0.05 was considered statistically significant for all tests.

RESULTS

A total of 145 culture-positive or positive multiplex PCR events were included in the study and evaluated using the structured six-step clinico-microbiological algorithm (Figure 1). Each isolate was independently assessed by the microbiologist, treating physician, and the investigator for its pathogenic potential, incorporating clinical indicators, SOFA score dynamics, treatment response, and presence of foreign devices.

Baseline characteristics

The mean age of the participants was 50.4 ± 16.3 years, with 55.9% of participants being male. Most were from Uttar Pradesh (47.6%) or Uttarakhand (46.9%), and nearly half were admitted to the medical intensive care unit (ICU). Aerobic culture was the predominant identification method (90.3%), with endotracheal tube/tracheostomy tube as the most common sample type (66.2%). Acinetobacter baumannii (46.9%) and Klebsiella pneumoniae (42.1%) were frequently isolated. A total of 14 organisms were identified by multiplex PCR. The most frequently detected organisms were Acinetobacter baumannii (10, 5.1%), Pseudomonas aeruginosa (9, 4.6%), and Klebsiella pneumoniae (6, 3.1%). A majority had monomicrobial infections (75.9%), localized signs (95.2%), and elevated SOFA scores (76.6%) (Table 1). Common comorbidities were hypertension (45.1%) and diabetes (41.8%). Although the cohort comprised 145 samples, a total of 195 organisms were identified across these samples, as several specimens grew multiple organisms. However, the investigator model identified 145 primary organisms for 145 samples, categorizing each as either a pathogen or a non-pathogen.

Table 1 Association of demographic, microbiologic, and clinical variables associated with pathogen vs non-pathogen, n (%).

Category	Parameter	Total (n = 145)	Non-pathogen (n = 14)	Pathogen (n = 131)	P value
Demographic variables, mean ± SD	Age (years)	5042 ± 1635	5121 ± 1643	5034 ± 1640	-
Gender	Male	81 (559)	10 (714)	71 (542)	-
	Female	64 (441)	4 (286)	60 (458)
Site of clinical care	General medicine ward	20 (138)	5 (357)	15 (115)	-
	Medicine ICU	71 (490)	4 (286)	67 (511)
	Nephrology ward	5 (34)	0 (00)	5 (38)
	Pulmonary medicine	49 (338)	5 (357)	44 (336)
Microbiological variables					-
Sample	Aerobic culture	131 (903)	14 (1000)	117 (893)	-
	Multiplex PCR (Biofire FilmArray)	14 (97)	0 (00)	14 (107)
	Sputum	24 (166)	4 (28.6)	20 (15.3)	-
	ET/TT	96 (662)	8 (57.1)	88 (67.2)	-
	BAL	25 (172)	2 (14.3)	23 (17.6)	-
Organism (n = 195); aerobic culture (n = 158)	Acinetobacter baumannii	58 (29.7)	4 (25)	54 (30.2)	-
	Klebsiella pneumoniae	55 (28.2)	5 (31.3)	50 (27.9)	-
	Pseudomonas aeruginosa	23 (11.8)	2 (12.5)	21 (11.7)	-
	Escherichia coli	5 (2.6)	1 (6.3)	4 (2.2)
	Stenotrophomonas maltophilia	4 (2.1)	1 (6.3)	3 (1.7)
	Others	13 (6.6)	3 (18.8)	10 (5.6)
Multiplex PCR (n = 37)	Acinetobacter baumannii; genes - IMP, VIM, NDM	10 (5.1)	0 (0.0)	10 (5.6)	-
	Pseudomonas aeruginosa; genes - NDM, VIM, IMP	9 (4.6)	0 (0.0)	10 (5.0)
	Klebsiella pneumoniae; genes - KPC, OXA - 48	6 (3.1)	0 (0.0)	6 (3.4)
	Influenza virus	4 (2.1)	0 (0.0)	4 (2.2)
	Escherichia coli; genes - VIM, NDM	3 (1.5)	0 (0.0)	3 (1.6)
	Others	5 (2.5)	0 (0.0)	5 (2.7)
Number of organisms positive/sample (n = 195)	1	110 (759)	12 (857)	98 (748)	-
	2	27 (186)	2 (143)	25 (191)	-
	3	3 (21)	0 (00)	3 (23)	-
	4	4 (28)	0 (00)	4 (31)	-
	6	1 (07)	0 (00)	1 (08)	-
	Same organism isolated from the local site and blood	28 (193)	0 (00)	28 (214)	-
	Pathogen		1 (71)	131 (1000)	-
Clinical variables	Signs and symptoms of any localized site of infection	138 (95.2)	9 (643)	129 (985)	< 00013
	Fever	71 (51.4)	4 (444)	67 (519)	-
	Cough	88 (63.8)	7 (778)	81 (628)	-
	Expectoration	105 (76.1)	4 (444)	101 (783)	00363
	Increasing oxygen requirement	107 (77.5)	3 (333)	104 (806)	00043
	Tachypnoea	97 (70.3)	3 (333)	94 (729)	00203
	Tachycardia	68 (49.3)	2 (222)	66 (512)	-
	Chest pain	4 (2.9)	1 (111)	3 (23)	-
	Increase in Sequential Organ Failure Assessment Score ≥ 2 (48 hours)	111 (76.6)	3 (214)	108 (824)	< 00013
Risk factors for colonization	Invasive mechanical ventilation	103 (71.0)	7 (500)	96 (733)	-
	Recent hospitalization or antibiotic use within the past 90 days	86 (59.3)	8 (571)	78 (595)	-
	Poor oral hygiene due to tobacco chewing, smoking, alcoholism, dry mouth, or chronic bedridden status	56 (38.6)	5 (357)	51 (389)	-
	Chronic obstructive pulmonary disease	31 (21.4)	4 (286)	27 (206)	-
	Tracheostomy	25 (17.2)	1 (71)	24 (183)	-
	Prior isolation of the organism within one year of the incident	22 (15.2)	0 (00)	22 (168)	-
	History of lower respiratory tract infection	10 (6.9)	2 (143)	8 (61)	-
	Neuromuscular disorders	7 (4.8)	0 (00)	7 (53)	-
	Bronchiectasis	3 (2.1)	1 (71)	2 (15)	-
Complications	Multiple organ dysfunction syndrome	71 (49.0)	4 (286)	67 (511)	-
	Ventilator-associated pneumonia	43 (29.7)	2 (143)	55 (420)	00442
	Shock	51 (35.2)	0 (00)	51 (389)	00023
	Bloodstream infections	48 (33.1)	2 (143)	46 (351)	-
	Acute kidney injury	16 (11.0)	1 (71)	15 (115)	-
	Patients on hemodialysis	21 (14.5)	0 (00)	21 (160)	-
	Arrhythmias, including ventricular tachycardia and fibrillation	5 (3.4)	0 (00)	5 (38)	-
	Bedsores	7 (4.8)	0 (00)	7 (53)	-
Comorbidities	Diabetes	38 (418)	3 (429)	35 (417)	-
	Hypertension	41 (451)	2 (28.6)	39 (464)	-
	Chronic obstructive pulmonary disease	26 (286)	3 (42.9)	23 (274)	-
	Coronary artery disease	6 (66)	0 (0.0)	6 (71)	-
	Chronic liver disease	3 (33)	1 (14.3)	2 (24)	-
	Chronic kidney disease	20 (220)	0 (0.0)	20 (238)	-
	Malignancy	5 (55)	0 (0.0)	5 (60)	-

ICU: Intensive care unit; PCR: Polymerase chain reaction; ET/TT: Endotracheal tube/tracheostomy tube; BAL: Bronchoalveolar lavage; IMP: Imipenemase; VIM: Verona integron-encoded metallo-β-lactamase; NDM: New Delhi metallo-β-lactamase; KPC: Klebsiella pneumoniae carbapenemase; OXA: Oxacillinase-48.

Outcomes

Among 145 respiratory samples, characterization by the investigator classified 131 (90.3%) isolates as pathogens and 14 (9.7%) as non-pathogens. A comparison between the investigator’s and the microbiologist’s classification of organisms as pathogen or non-pathogen revealed a Cohen’s Kappa coefficient of 0.28, indicating fair agreement. The cross-tabulation shows that both parties agreed on 129 samples being pathogens, while agreement on non-pathogens occurred in only 3 cases (Table 2). The discordant note was noted in 13 cases. The majority of disagreements occurred when the microbiologist labelled the isolate as pathogenic (based on culture identification), while the investigator labelled it as non-pathogenic. The investigator’s decision was based on the lack of overt clinical features of infection, which outweighed the observation of a rise in SOFA score that would otherwise have suggested organ dysfunction. The mean age of patients was comparable between groups (P = 0.923). While the gender and state distribution did not differ significantly. Significantly fewer non-pathogen cases had signs of localized infection (64.3% vs 98.5%, P < 0.001), lower respiratory tract localization (57.1% vs 96.9%, P < 0.001), and SOFA ≥ 2 (21.4% vs 82.4%, P < 0.001). The distribution of organisms (e.g., Klebsiella pneumoniae, Acinetobacter baumannii) showed no significant variation between groups.

Table 2 Agreement for pathogen characterization between investigator and microbiologist, n (%).

Characterization		Characterization by investigator			Cohen’s Kappa
		Non-pathogen	Pathogen	Total	k	P value
Characterization by microbiologist	Non-pathogen	3 (2.1)	2 (1.4)	5 (3.4)	0.28	< 0.001
	Pathogen	11 (7.6)	129 (90.3)	140 (96.6)
	Total	14 (9.7)	131 (90.3)	145 (100.0)

A significantly greater number of pathogen cases exhibited respiratory symptoms, including expectoration (P = 0.036), increased oxygen requirements (P = 0.004), tachypnea (P = 0.020), and altered mental status (P = 0.011). Ventilator-associated pneumonia (VAP) (P = 0.044) and septic shock (P = 0.011) were significantly more common in pathogen cases (Table 1).

Out of 14 cases classified as non-pathogenic, 12 patients (85.7%) were not treated, and 2 patients (14.3%) were treated but remained clinically stable. None showed improvement attributable to antimicrobial therapy. One patient (7.1%) in this group died without receiving treatment; however, the mortality was attributed to ventricular arrhythmia and was unrelated to sepsis. Among 131 cases classified as pathogenic, 68 patients (51.9%) showed clinical improvement with treatment, while 63 patients (48.1%) showed no response despite therapy. A total of 48 patients (36.6%) in the pathogen group died, all of whom had failed to respond to treatment. Only 1 death (0.8%) occurred among those who improved with therapy secondary to another infection, VAP (Table 3).

Table 3 Comparison of variables across the investigator’s classification of respiratory isolates.

Parameters	Pathogenic commensal (n = 3)	Pathogenic colonizer (n = 103)	Pathogenic direct (n = 25)	Non-pathogenic commensal (n = 1)	Non-pathogenic colonizer (n = 5)	Non-pathogenic contaminants (n = 8)	P value
Age (years), mean ± SD	40.00 ± 19.52	50.67 ± 16.46	50.20 ± 16.10	30.00 ± 0	58.40 ± 5.90	49.38 ± 19.40	0.5591
Gender: Male (%)	33.3	49.5	76.0	100.0	80.0	62.5	0.0952
Gender: Female (%)	66.7	50.5	24.0	0.0	20.0	37.5	0.0952
Sample type: Sputum (n = 24)	100	0	68.0	100	0	37.5	< 0.001
Sample type: ET/TT (n = 96)	0	84.5	4.0	0	80.0	50.0	< 0.001
Sample type: BAL (n = 25)	0	15.5	28.0	0	20.0	12.5	< 0.001
Fever (%)	33.3	43.6	88	0	33.3	50	< 0.001
Cough (%)	66.7	56.4	88	0	66.7	83.3	0.0182
Increasing oxygen requirement, n (%)	1 (33.3)	87 (86.1)	16 (64.0)	0 (0)	1 (33.3)	2 (33.3)	< 0.0012
Increase in Sequential Organ Failure Assessment score ≥ 2 (%)	66.7	85.4	72.0	0	0	37.5	< 0.001
Shock (%)	0	43.7	24.0	0	0	0	0.009
Multiple organ dysfunction syndrome (%)	0	55.3	40.0	0	40.0	25.0	0.122
VAP (%)	0	48.5	20.0	0	20.0	12.5	0.011
Risk factors
Mechanical ventilation (%)	0	93.2	0	0	100.0	25.0	< 0.001
Recent hospitalizations/antibiotics (%)	66.7	69.9	16.0	0	100.0	37.5	< 0.001
Poor oral hygiene (%)	66.7	45.6	8.0	0	60.0	25.0	0.001
Chronic obstructive pulmonary disease (%)	33.3	24.3	4.0	0	60.0	12.5	0.030
Bronchiectasis (%)	66.7	0	0	0	20.0	0	< 0.001
Tracheostomy (%)	0	23.3	0	0	20.0	0	0.030
Response to treatment							< 0.001
Improved	100	43.70	80.00	-	-	-
No response	0	56.30	20.00	-	-	-
Not treated	-	-	-	100.00	80.00	87.50
Stable (non-pathogen) with treatment				0	20.00	12.50
Outcome: Mortality (%)	0	50.6	16.0	0	0	14.3	0.001

ET/TT: Endotracheal tube/tracheostomy tube; BAL: Bronchoalveolar lavage; VAP: Ventilator-associated pneumonia.

Among 145 respiratory isolates, pathogenic colonizers were most frequent (n = 103, 71.0%), followed by pathogenic direct pathogens (n = 25, 17.2%) and pathogenic commensals (n = 3, 2.1%), while non-pathogenic isolates comprised commensals (n = 1, 0.7%), colonizers (n = 5, 3.4%), and contaminants (n = 8, 5.5%). The mean age and gender distribution were comparable across groups. However, sample type distribution differed significantly (P < 0.001), with sputum predominant among direct pathogens and endotracheal aspirates among colonizers. Fever, cough, and increasing oxygen requirement were significantly more common in pathogenic isolates (P < 0.001). Pathogenic colonizers exhibited the highest proportion of patients with SOFA score ≥ 2 (85.4%), shock (43.7%), and VAP (48.5%). Risk factors such as mechanical ventilation (93.2%) and recent antibiotic exposure (69.9%) were strongly associated with colonizers (P < 0.001). Treatment response was best among pathogenic commensals (100%) and direct pathogens (80%), while most non-pathogenic isolates were either untreated or clinically stable. Mortality was highest among pathogenic colonizers (50.6%) compared with direct pathogens (16%) and non-pathogenic groups (0%) (P = 0.001) (Table 3).

DISCUSSION

This study addresses a critical diagnostic challenge in respiratory medicine: Differentiating true infection from colonization or contamination in lower respiratory tract samples. By utilizing a structured, stepwise clinico-microbiological model, our research bridges the gap between microbiological identification and clinical relevance, particularly in high-burden, resource-constrained ICUs. Among the 145 samples evaluated, the majority (90.3%) were classified as pathogenic by the investigator. While this may reflect the predominance of serious infections in ICU patients, our analysis showed that a proportion of organisms (9.7%) did not meet criteria for pathogenicity despite being isolated from clinically ill individuals.

The fair agreement (κ = 0.28) between microbiologists and investigators underscores a persistent issue in practice: Microbiological positivity does not equate to clinical infection. Data from the VAPrapid2 trial further support this, highlighting that only 30% of patients were microbiologically confirmed to have VAP despite clinical suspicion, reinforcing the importance of integrating microbiological, clinical, and SOFA score parameters[10]. Labelle et al[5] reported that among patients with healthcare-associated pneumonia (n = 870), approximately 34% had growth of non-pathogenic flora; yet, a substantial proportion still received antibiotics, highlighting a mismatch between microbiological findings and clinical decision-making. Our study provides additional empirical evidence that reinforces the necessity of integrating host and clinical parameters into diagnostic and therapeutic decision-making, rather than relying solely on microbial isolation.

The strength of our study lies in correlating organism classification with clinical outcomes. Non-pathogen cases were largely untreated, yet 85.7% recovered uneventfully, and only one unrelated death was observed. This suggests that refraining from antibiotic escalation when non-pathogenic status is appropriately identified is both safe and cost-effective. Conversely, among those classified as pathogens, 68 (51.9%) showed clinical improvement with treatment, while 63 (48.1%) did not respond, of whom 48 (76.2%) died. This strong association between investigator classification and treatment response (P < 0.001) supports the validity of the algorithm.

Our findings also reaffirm the clinical value of integrating the SOFA score, symptomatology, and radiologic correlation. Patients with pathogenic organisms were significantly more likely to exhibit features such as expectoration, tachypnea, oxygen requirement, and altered mentation (P < 0.05 for all), in line with classical presentations of lower respiratory tract infections. Additionally, a rise in SOFA score of ≥ 2 within 48 hours was found in 82.4% of pathogen cases vs 21.4% in non-pathogens (P < 0.001), highlighting its utility as a discriminator. These findings are consistent with the 2016 Infectious Diseases Society of America/American Thoracic Society guidelines for healthcare-associated pneumonia - hospital-acquired pneumonia/VAP, which emphasize the diagnostic value of clinical features such as new or worsening cough, purulent secretions, leukocytosis, and hypoxia[3,11,12].

The multiplex PCR, which offers rapid and broad-spectrum pathogen detection -including viruses, atypical bacteria, and other pathogens - was selectively used in patients with suspected respiratory infections, often in critically ill or diagnostically challenging cases. In our study, multiplex PCR accounted for 9.7% (n = 14) of the total positive samples and was able to detect a total of 37 organisms, comprising 28 bacteria and 9 viruses, along with clinically relevant antimicrobial resistance genes. Notably, all multiplex PCR-positive samples were ultimately classified as pathogenic by the stepwise model, likely reflecting the high pre-test probability of infection in these cases. However, the high sensitivity of multiplex PCR poses a diagnostic dilemma, as it may detect nucleic acids from non-viable organisms or colonizers, particularly in patients undergoing recent or ongoing antibiotic therapy[7,13].

Núñez et al[14] found that each 1-point increase in SOFA score was associated with a 30% increased hazard of 30-day mortality (hazard ratio = 1.30; 95% confidence interval: 1.12-1.52; P < 0.001), Raveendra et al[15] reported significantly higher SOFA scores in non-survivors at VAP diagnosis (P = 0.005), and Kumar et al[16] highlighted the survival benefit of early combination antibiotic therapy in septic shock, reinforcing the role of early clinical-based decisions. The most frequently isolated organisms were Acinetobacter baumannii (46.9%), Klebsiella pneumoniae (42.1%), and Pseudomonas aeruginosa (22.1%), mirroring patterns reported in national ICU surveillance data (ICMR-AMRSN, 2021) with 30.4%, 25.3%, and 19.7% respiratory pathogens respectively[17]. Their predominance among pathogen-classified cases highlights the model’s strength in identifying clinically relevant, multidrug-resistant organisms.

Colonization factors, such as invasive mechanical ventilation and tracheostomy, while more prevalent in pathogen cases, did not reach statistical significance (P = 0.116 and 0.465, respectively). Established literature has linked several host-related and environmental factors to increased risk of airway colonization, including recent hospitalization or antibiotic use, poor oral hygiene due to tobacco use or chronic bedridden status, chronic obstructive pulmonary disease, prior lower respiratory tract infections, bronchiectasis, and neuromuscular disorders[18-23]. Intubation with mechanical ventilation increases the risk of bacterial pneumonia by 6-20-fold due to disruption of natural host defenses, biofilm formation in endotracheal tubes, and promotion of airway colonization. Opportunistic organisms such as Acinetobacter baumannii, Pseudomonas aeruginosa, and Staphylococcus aureus can initially colonize the endotracheal tube and subsequently progress to VAP[2,24,25]. Candida colonization of the respiratory tract is a well-recognized phenomenon in ICU patients. In a multicenter study, Timsit et al[26] reported that in patients undergoing mechanical ventilation for more than 4 days, bronchial colonization with Candida species was not independently associated with the development of VAP (adjusted cause-specific hazard ratio = 0.98; 95% confidence interval: 0.59-1.65; P = 0.95). However, Azoulay et al[27] found that Candida colonization of the respiratory tract did not correlate with increased mortality; however, it did increase the risk of VAP with Pseudomonas.

Such objective evidence supports the rationale for employing integrative clinico-microbiological approaches like our model to guide targeted antimicrobial use. Preventive strategies - including chlorhexidine oral care, semi-recumbent positioning, and subglottic suctioning - have also been shown to reduce the risk of VAP by limiting the colonization and aspiration of oropharyngeal secretions[28,29]. Such objective evidence supports the rationale for employing integrative clinico-microbiological approaches like our model to guide targeted antimicrobial use. The study’s structured framework demonstrated alignment with clinical response and mortality patterns, reinforcing its utility in antimicrobial stewardship. Importantly, the model enabled withholding antibiotics in non-pathogen cases without adverse effects - a key stewardship goal. Given the global crisis of antimicrobial resistance, such structured, evidence-based approaches are increasingly essential.

Our study has several limitations. First, it was conducted at a single center over a limited 3-month period, which may limit the generalizability of the findings. Second, although clinical features and SOFA scores were systematically assessed using a structured checklist, there remains the potential for interobserver variability in classification. Third, we did not perform economic or cost analyses, and laboratory inflammatory markers, such as C-reactive protein and procalcitonin, were not analyzed, which could have further strengthened pathogen differentiation and prognostic assessment. Therefore, conclusions regarding resource utilization must be interpreted cautiously. Fourth, the follow-up period was limited to 28 days, and long-term outcomes such as readmissions or recurrent infections were not assessed. Fifth, a few cases had multiple organism growth in a single sample, which was not analyzed or correlated.

CONCLUSION

This prospective study evaluated 145 lower respiratory tract isolates using a structured six-step clinico-microbiological algorithm, classifying 90.3% as pathogens and 9.7% as non-pathogens. The model showed a strong association between classification and clinical outcomes: 85.7% of non-pathogen cases were safely managed without antibiotics, while among pathogen-classified cases, 51.9% improved with treatment and 36.6% succumbed despite therapy. Key predictors of pathogenicity included localized respiratory symptoms, a SOFA score of 2 or higher, and radiologic features. Cohen’s Kappa between the investigator and microbiologist classification was 0.28, indicating fair agreement. This model effectively differentiated between infection and colonization, allowing for rational antibiotic use without compromising patient safety, and reinforcing its potential utility in guiding antimicrobial stewardship efforts in high-burden clinical settings.