A novel nomogram for the early identification of coinfections in elderly patients with COVID-19

doi:10.21203/rs.3.rs-4951568/v1

Background

This study aimed to establish a novel, precise, and practical nomogram for use upon hospital admission to identify coinfections among elderly patients with coronavirus disease 2019 (COVID-19) to provide timely intervention, limit antimicrobial agent overuse and hospitalisation costs, finally reduce unfavourable outcomes.

Methods

This prospective cohort study included COVID-19 patients consecutively admitted at multicenter medical facilities in a two-stage process. The nomogram was built on the multivariable logistic regression analysis. The performance of the nomogram was assessed for discrimination and calibration using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) in rigorous internal and external validation settings.

Results

Between 7 December 2022 and 1 February 2023, in the first stage of this study, 916 COVID-19 patients were included. The coinfection rates in non-elderly and elderly patients determined to be 16.22% and 26.61%, respectively. Pneumonia caused by other pathogens (85.45%) was the most common coinfection-associated illness in the elderly group. Bacteria were the most common pathogens associated with coinfections in the elderly, especially gram-negative bacteria (48%) of Acinetobacter baumanii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. Fungi (38%) were the second most common pathogens isolated from coinfections in elderly patients with COVID-19. The nomogram was developed with the parameters of diabetes comorbidity, previous invasive procedure, and procalcitonin (PCT) level, which together showed areas under the curve of 0.86, 0.82, and 0.83 in the training, internal validation, and external validation cohorts, respectively. The nomogram outperformed both PCT or C-reactive protein level alone in detecting coinfections in elderly patients with COVID-19; in addition, we found the nomogram was specific for the elderly compared to non-elderly group. Calibration plots of the nomogram revealed excellent agreement between the predicted and actual probabilities of coinfection occurrence, and the DCA indicated favourable clinical consistency of nomogram results.

Conclusions

This novel nomogram will assist in the early identification of coinfections in elderly patients with COVID-19.

Trial registration:

This study was registered at https://ClinicalTrials.gov, with the registration NCT06321367 (registration Date: 2024-03-20).

Biological sciences/Microbiology

Health sciences/Risk factors

COVID-19

coinfections

early identification

elderly

nomogram

The pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has devastated human life, healthcare systems, and the world economy(1). As China abandoned "zero COVID-19" strategies in December 2022, it has witnessed a surge in COVID-19 infections both at home and abroad(2, 3). Coinfections with other pathogens are the most common complications of COVID-19. Coinfections have been extensively described in the context of a previous respiratory virus pandemic(4), and they have the potential to elevate COVID-19 cases to critical, rapidly developing cases of acute respiratory distress syndrome, especially in elderly patients(5).

The incidence rate of coinfections in hospitalised COVID-19 patients of all ages is approximately 7–8%(6, 7), while it is up to 27.3% among the elderly (8). However, early diagnosis of coinfections in elderly individuals with COVID-19 is challenging, which may be because of the atypical clinical presentation of acute infections due to immune senescence(9) and characteristics of conventional microbiological tests (time-consuming, expensive, and inefficient) that limit their availability(10). For these reasons, and the uncertainty about coinfections, almost three-quarters of patients with COVID-19 receive antimicrobial agents such as antibiotics, which is significantly higher than the estimated prevalence of bacterial coinfections(11). Overuse of antimicrobial agents for viral pneumonia leads to high hospitalisation costs, potential side effects, and increased infections of multidrug-resistant organisms (MDROs), especially in the elderly (12). Therefore, deployment of a model based on easily accessible data will be a valuable asset in the diagnosis of coinfections that can guide microbiological workups and use of antimicrobials in elderly patients with COVID-19.

Currently, most clinical research related to COVID-19 is focused on clinical features and risk factors for poor outcomes of COVID-19 alone in the elderly(13, 14). A previous study showed that an existing predictive model can help stratify bacterial coinfection risk in adult (≥ 18 years) patients with COVID-19(15), but no studies have shown similar models focused specifically on the elderly. Therefore, we aimed to develop a nomogram to assist in the early identification of coinfections in elderly patients with COVID-19.

2.1 Study design and data collection

This clinical investigation was prospectively conducted at Xiangya Hospital of Central South University in Changsha, Hunan, Jiangxi Provincial People's Hospital in Nanchang, Jiangxi, the Affiliated Nanhua Hospital of University of South China in Hengyang, Hunan, and the First Affiliated Hospital of Xinxiang Medical University in Weihui, Henan. The study comprised two distinct stages. The initial stage involved the identification of adult patients with COVID-19 (aged ≥18 years) at Xiangya Hospital of Central South University between December 7, 2022, and February 1, 2023, aiming to build a model. Subsequently, the second stage collected an independent external validation cohort of elderly COVID-19 patients (aged ≥65 years) from all four medical facilities, spanning from February 2, 2023, to June 30, 2023, aiming to validate the model.

The inclusion criteria were: (1) age greater than or equal to 18 years (for the first stage) or 65 years (for the second stage); (2) COVID-19 was confirmed diagnosed with SARS-CoV-2 reverse transcription-polymerase chain reaction positive; (3) hospitalization more than one day, including patients in emergency. The exclusion criteria were: (1) unconfirmed SARS-CoV-2 infection; (2) lack of data at baseline or hospital discharge; (3) age less than 18 years old (for the first stage) or 65 years old (for the second stage); (4) pregnancy; (5) hospitalisation less than one day; (6) outpatients (Figure 1).

We collected data on demographics, clinical symptoms, comorbidities, laboratory results, and clinical outcomes of patients with COVID-19 upon admission. Two independent researchers reviewed and verified all information to ensure accuracy.

2.2 Definitions

Coinfections were defined as coinfected bacteria, fungi, or other viruses with SARS-CoV-2 within 48 hours of hospital admission, considering clinical, etiology, and imaging evidence. Coinfections that occurred later than 48 hours after this admission were not considered in the present analysis. The positive microbiologic diagnostic criteria for different coinfections were as follows. Coinfected with other types of pneumonia were diagnosed by (1) one or more positive cultures of bacteria and fungi obtained from good-quality sputum (>25 polymorphonuclear leukocytes and <25 epithelial cells), tracheal aspirate, or bronchoalveolar lavage; (2) positive testing of specific rapid reverse transcription-polymerase chain reaction (PCR) or multiplex PCR for other respiratory viruses. Bloodstream coinfection was defined as the growth of a non-skin colonizing pathogen in two or more blood cultures from different sites. Urinary tract coinfection was defined as the growth of pathogens in a cultured cleaning middle urine sample from a patient with clinical symptoms and/or considering such urinary infection clinically significant by our researchers(16). Intestinal coinfection was diagnosed in patients with diarrhea (>3 times/ 24h) who had positive cultures for one or more enteric pathogens obtained from stool or anal swabs(17). Skin and soft tissue coinfection(18) was defined as collecting clinically significant pathogens from the infected skin area. Others, such as osteomyelitis, intracranial infection, peritonitis, hepatapostema, and et al., were grouped as other coinfections. Two of our researchers reviewed the records of all patients with positive microbiologic results to assess clinical significance.

Invasive procedures are operative procedures in which skin or mucous membranes and connective tissue are incised or punctured, and an instrument is introduced through a natural body orifice. They do not include using instruments such as otoscopes for examinations or very minor procedures such as drawing blood(19). In our study, invasive procedures included deep vein- and arterio-puncture, trachea cannula, tracheotomy, urethral catheterization, lumbar puncture, abdominocentesis, thoracentesis, bone marrow aspiration, bone marrow biopsy, electronic gastroscopy, extracorporeal membrane oxygenation, continuous renal replacement therapy (CRRT), and et al. No general agreement exists on the age at which a person becomes old. However, most countries have accepted the chronological age of 65 as a definition of "elderly" or older persons(20). Hence, the definition of older patients in our study referred to those 65 years or older, which differs from our previous study of elderly patients(21).

2.3 Statistical analysis

To address missing data, we employed multiple imputations utilizing the "mice" package in R. Categorical variables were assessed in terms of absolute numbers and their respective proportions. Continuous variables were analyzed as mean and standard deviation if normally distributed or as median and interquartile range (IQR) if non-normally distributed. Categorical variables were compared using the χ2 test or Fisher exact test, whereas continuous variables were compared using the Mann-Whitney U or Student's t-test.

The elderly COVID-19 patients of the first stage were randomized into the training and validation cohorts (ratio of 3:1, respectively). The predictors of coinfections in elderly patients with COVID-19 were identified in the training cohort and were used to develop a model. Variables with P value less than 0.05 in the univariate logistic analysis were selected for the least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, LASSO analysis with 10-fold cross-validation was performed to further choose features via λ optimization. The coefficients of the selected features were then shrunk to zero at the optimal λ value, retaining only those parameters with nonzero coefficients. Finally, the variables identified through LASSO regression were utilized in the multivariate logistic analysis to create a nomogram for identification coinfections in elderly patients with COVID-19. This nomogram, which is based on the weighted influence of each factor, establishes a reliable scoring threshold for predicting the likelihood of coinfections in elderly COVID-19 patients. Also, an interactive web-based dynamic nomogram application was developed using Shiny.

The performance of the nomogram was assessed by the discriminative and celebrative ability of the training cohort, as well as the internal and external validation cohorts. The discrimination, measured using the receiver operating characteristic curve (ROC), is the ability of the model to distinguish between older patients with COVID-19 who got coinfections and those who didn't. Generally, an area under curve (AUC) greater than 0.75 indicates an excellent discriminative ability. The diagnostic performance was evaluated using the AUC (95%CI), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) at 90% sensitivity (to balance specificity), 90% specificity (to balance sensitivity), and the maximum Youen's index (the optimal cutoff)(22).

Calibration refers to the agreement between the predicted and observed outcomes, which was performed using bootstrapping with 1000 research resamples and assessed utilizing calibration plots. A calibration plot with the calibration slope and intercept presented the predicted and observed probabilities. Ideally, the slope would be close to 1 and intercept 0. The model calibration was also assessed with the Hosmer - Lemeshow test for goodness of fit, and the P values were reported. A P > 0.05 means a good fit of the model, indicating no significant difference between the predicted and observed outcomes. A decision curve analysis (DCA) to evaluate the clinical usefulness of the models was performed by drawing a decision curve that computed the net benefit, and the range of positive net benefit was analyzed.

All analyses were performed using the R software version 4.3.2 (http://www.r-project.org). All statistical tests were two-sided, with a significance level of < 0.05.

2.4 Sample size

To validate the accuracy of the constructed models, we employed the methodology outlined by Riley et al.(23) for sample size calculation. This involved entering a prevalence rate of 26.61% among elderly COVID-19 patients with coinfections, a C statistic of 0.86, and a predictor parameter count of 3 within the multivariate model using the "pmsampsize" package in R software. Based on these parameters, a minimum of 301 cases was determined as essential. Our study included 465 and 306 elderly patients in the training and external validation cohorts, respectively, surpassing the prescribed minimum sample size threshold. Furthermore, during model development, we strictly adhered to the recommended guideline of ensuring a minimum of 10 occurrences per predictive factor. Consequently, the sizes of our training datasets significantly exceed this threshold, signifying a diminished risk of overfitting and ensuring accurate estimation of crucial parameters in the predictive models. This substantial sample size forms a sturdy basis for the development and validation of our models.

3.1 Demographics and characteristics upon admission

In the first stage of the study, 916 inpatients with confirmed COVID-19, divided into non-elderly and elderly groups, were included (Figure 1). The clinical characteristics and outcomes of the non-elderly and elderly groups with coinfections are compared in Table 1.

Demographically, the median age was 54.0 (IQR 49.0–59.0) years for the non-elderly group and 79.0 (IQR 73.0–84.0) years for the elderly group, while sex did not differ significantly between the two groups. Of note, in the elderly group, frequencies of non-hyperpyrexia (T < 39℃, 44.2% vs. 25%, P = 0.026) and dyspnoea (53.9% vs. 31.2%, P = 0.009) were significantly higher, whereas the rate of headache (9.09% vs. 25%, P = 0.008) was significantly lower, than these clinical features in the non-elderly group. The proportions of diabetes (62.4% vs. 10.4%, P < 0.001), hypertension (53.9% vs. 35.4%, P = 0.036), chronic cardiovascular diseases (35.8% vs. 14.6%, P = 0.009), and chronic respiratory diseases (33.3% vs. 14.6%, P = 0.019) were significantly higher, while that of haematological disorders (1.82% vs. 8.33%, P = 0.047) was significantly lower in the elderly vs. non-elderly individuals. We found a substantially higher proportion of the elderly group that had undergone invasive procedures (42.4% vs. 16.7%, P = 0.002). In addition, several laboratory indices, including oxygenation index (OI), albumin (ALB), albumin/globulin (A/G), estimated glomerular filtration rate (eGFR), blood potassium (K⁺), blood sodium (Na⁺), and procalcitonin (PCT), were significantly different (P < 0.05) between the two groups, as shown in Table 1.

The coinfection rate was significantly higher in the elderly than that in the non-elderly group (26.61% vs. 16.22%, P = 0.001, Table 1). There was no significant difference in the number of body sites with coinfections between the two groups (P > 0.05). We found that the co-occurrence rate of pulmonary coinfection (85.5% vs. 62.5%, P < 0.001) and other coinfections (0.61% vs. 10.4%, P = 0.002) in the elderly group was significantly different from those in the non-elderly group, with significantly higher rates of pulmonary coinfection in the elderly. No significant differences were observed between the two groups in terms of bloodstream coinfection, intestinal coinfection, urinary tract coinfection, or skin and soft tissue coinfection (Table 1).

3.2 Clinical outcomes

Elderly COVID-19 patients with coinfections showed a significantly higher rate of intensive care unit admissions (30.9% vs. 14.6%, P = 0.04) and mortality (40% vs. 22.9%, P = 0.046) than the non-elderly population (Table 1).

Table1. Comparison of clinical characteristics and outcomes between the non-elderly (< 65) and elderly (≥ 65) COVID-19patients (916 in total) with coinfections in the first stage.

Variables	Non-elderly COVID-19 patients with coinfection (48/296, 16.22%)	Elderly COVID-19 patients with coinfection (165/620, 26.61%)	P
Demographic data
Male	28 (58.3%)	121 (73.3%)	0.069
Age	54.0 [49.0;59.0]	79.0 [73.0;84.0]	<0.001
Clinical symptoms
T <39℃	12 (25.0%)	73 (44.2%)	0.026
T ≥39℃	20 (41.7%)	52 (31.5%)	0.256
Chill	4 (8.33%)	4 (2.42%)	0.079
Cough	7 (14.6%)	21 (12.7%)	0.926
Dyspnea	15 (31.2%)	89 (53.9%)	0.009
Headache	12 (25.0%)	15 (9.09%)	0.008
Muscular soreness	8 (16.7%)	16 (9.70%)	0.278
Comorbidities
Diabetes	5 (10.4%)	103 (62.4%)	<0.001
Hypertension	17 (35.4%)	89 (53.9%)	0.036
Chronic cardiovascular diseases	7 (14.6%)	59 (35.8%)	0.009
Chronic respiratory disease	7 (14.6%)	55 (33.3%)	0.019
Chronic renal disease	8 (16.7%)	19 (11.5%)	0.485
Chronic liver disease	7 (14.6%)	7 (4.24%)	0.018
Autoimmune disease	3 (6.25%)	6 (3.64%)	0.425
Cerebrovascular diseases	3 (6.25%)	30 (18.2%)	0.074
Hematological disorder	4 (8.33%)	3 (1.82%)	0.047
Tumor	9 (18.8%)	17 (10.3%)	0.186
Invasive procedure	8 (16.7%)	70 (42.4%)	0.002
Arterial blood gases
Lac	1.21 [0.82;1.85]	1.30 [0.90;1.72]	0.719
OI	311 [212;373]	263 [148;352]	0.043
Blood routine
WBC	6.70 [4.27;8.67]	7.20 [4.90;10.3]	0.245
HGB	110 [87.0;130]	118 [107;130]	0.053
PLT	167 [97.0;226]	168 [123;242]	0.331
Percentage of NE	81.7 [66.5;89.5]	81.9 [71.2;91.0]	0.418
Percentage of LY	10.1 [5.19;20.7]	8.70 [4.76;17.1]	0.264
Percentage of MO	6.89 [3.81;9.96]	7.40 [4.14;10.6]	0.996
NLR	7.91 [3.05;17.3]	9.29 [4.43;17.9]	0.246
Liver function
ALB	32.8 [30.6;36.3]	31.2 [28.1;34.8]	0.007
GLB	27.8 [24.3;30.5]	28.9 [25.1;32.8]	0.133
A/G	1.21 [1.07;1.47]	1.11 [0.96;1.31]	0.003
TBIL	9.85 [6.18;17.2]	10.8 [7.90;14.2]	0.531
DBIL	3.35 [2.18;7.08]	4.10 [3.00;5.70]	0.352
ALT	19.9 [14.9;34.9]	25.0 [16.3;40.2]	0.092
AST	29.4 [20.3;38.7]	34.1 [23.8;49.9]	0.063
AST/ALT	1.38 [0.90;1.96]	1.37 [1.03;1.77]	0.967
Kidney function
eGFR	89.1 [34.3;115]	71.4 [37.7;86.5]	0.024
Myocardial enzyme
CK	68.3 [31.6;153]	88.0 [49.9;185]	0.075
Serum electrolytes
K	4.28 [4.11;4.51]	3.95 [3.56;4.40]	<0.001
Na	138 [135;140]	140 [136;142]	0.021
Coagulation function
PTA	90.5 [77.0;104]	93.2 [78.4;101]	0.945
INR	1.06 [0.94;1.12]	1.02 [0.96;1.14]	0.887
FIB	4.38 [3.41;5.37]	4.37 [3.34;5.50]	0.949
D Dimer	0.30 [0.16;0.82]	0.36 [0.17;0.99]	0.327
Inflammatory factor
PCT	0.12 [0.05;0.53]	0.44 [0.09;2.80]	0.003
CRP	40.7 [15.1;96.3]	57.6 [18.3;102]	0.530
ESR	57.0 [33.0;83.0]	61.0 [39.0;82.0]	0.784
IL 10	5.00 [2.97;8.23]	5.00 [3.01;7.49]	0.890
IL 6	19.3 [4.14;54.2]	15.9 [6.15;42.4]	0.664
Details of coinfection
Numbers of coinfection
1	7 (14.6%)	38 (23.0%)	0.289
2	1 (2.08%)	9 (5.45%)	0.462
3	1 (2.08%)	3 (1.82%)	1.000
Position of coinfection
Pulmonary coinfection	30 (62.5%)	141 (85.45%)	0.001
Bloodstream coinfection	8 (16.7%)	30 (18.18%)	0.978
Intestinal coinfection	9 (18.8%)	28 (16.97%)	0.944
Urinary tract coinfection	4 (8.33%)	18 (10.91%)	0.789
Soft tissue coinfection	3 (6.25%)	7 (4.24%)	0.697
Other coinfections	5 (10.4%)	1 (0.61%)	0.002
Clinical outcomes
ICU admission in hospitalization	7 (14.6%)	51 (30.9%)	0.040
Death (while in hospital)	11 (22.9%)	66 (40.0%)	0.046

Abbreviations: ALB: albumin; A/G: albumin/ globulin; ALT: alanine aminotransferase; AST: aspartate transaminase; AST/ALT: aspartate-to-alanine transaminase ratio; CK: creatine kinase; CRP: C-reactive protein; DBIL: direct bilirubin; eGFR: estimated glomerular filtration rate; ESR: erythrocyte sedimentation rate; FIB: fibrinogen; GLB: globulin; HGB: hemoglobin; INR: international normalized ratio; IL 10: interleukin 10; IL 6: interleukin 6; ICU: intensive care unit; K⁺: blood potassium; Lac: lactic acid; LY: percentage of leukomonocyte; MO: percentage of monocyte; NE: percentage of neutrophil; NLR: neutrophil-to-lymphocyte ratio; Na⁺: blood sodium; OI: oxygenation index; PLT: platelet; PTA: prothrombin activity; PCT: procalcitonin; TBIL: total bilirubin; WBC: white blood cell.

3.3 Characteristics of coinfections in elderly patients with COVID-19

The age distribution of elderly patients with and without coinfections is shown in Figure 2A. The incidence of coinfections increased with age, from 24% to 40%. Notably, pulmonary infection (86%) was the most common coinfections in the elderly group, followed by bloodstream coinfection (19%), intestinal coinfection (15%), urinary tract coinfection (12%), and skin and soft tissue coinfection (2%, Figure 2B).

The most common pathogens isolated from coinfections in elderly patients with COVID-19 were gram-negative bacteria (48%), fungi (38%), and gram-positive bacteria (14%, Figure 2C). The four most common gram-positive bacteria isolated were Enterococcus faecium (15), Staphylococcus aureus (12), Corynebacterium striatum (10), and Coagulase negative staphylococcus (6) (Figure 2D). The five most commonly isolated gram-negative bacteria were Acinetobacter baumanii (95), Klebsiella pneumoniae (72), Pseudomonas aeruginosa (29), Burkholderia cepacian (18), and Escherichia coli (17). The four most commonly detected fungi were Candida albicans (82), Aspergillus spp. (34), Candida glabrata (17), and Candida tropicalis (10). We tested for only two viruses, Cytomegalovirus and Herpes simplex virus (Figure 2D). Four multidrug resistant bacteria were identified, including carbapenem-resistant Klebsiella pneumoniae (9), carbapenem-resistant Acinetobacter baumannii (7), methicillin-resistant Staphylococcus areus (3), and vancomycin-resistant Enterococci (4) (Figure 2E).

The distribution of coinfecting pathogens by age group in elderly patients with COVID-19 is shown in Figure 2F. The most common pathogens isolated in patients aged less than 90 years were gram-negative bacteria, followed by fungi and gram-positive bacteria. In patients aged > 90 years, fungi were the major pathogens detected.

3.3 Nomogram to identify early coinfections in elderly patients with COVID-19

In the first stage of this study, 620 elderly patients with COVID-19 were included in the analysis and randomly divided into training (n = 465) and internal validation (n = 155) cohorts at a ratio of 3:1. The data grouping for the two cohorts was random and matched (P > 0.05, Table S1). Univariable logistic regression analysis showed age, invasive procedure, diabetes, OI, white blood cells (WBC), hemoglobin (HGB), percentage neutrophils, percentage lymphocytes, percentage monocytes (MO), neutrophil-lymphocyte ratio (NLR), ALB, A/G, aspartate-to-alanine transaminase ratio, eGFR, K⁺, Na⁺, D dimer, PCT, and C-reactive protein (CRP) were predictors of coinfections in the elderly patient with COVID-19 (Table 2).

Table 2. Predictors to identify elderly COVID-19 patients with coinfections in the training cohort using univariable and multivariate logistic regression analysis

Characteristics	Univariable logistic regression				Multivariate logistic regression
Characteristics	OR	CI	P	OR		CI	P
Male	1.51	0.97-2.36	0.07	-		-	-
Age	1.03	1-1.06	0.03	-		-	-
Invasive procedure	7.71	4.75-12.54	<0.001	5.17		2.78-9.69	<0.001
Diabetes	5.14	3.33-7.94	<0.001	5		2.86-8.92	<0.001
Hypertension	1.03	0.69-1.56	0.87	-		-	-
Heart disease	1	0.66-1.52	1	-		-	-
Chronic respiratory disease	1.48	0.96-2.3	0.08	-		-	-
Chronic renal disease	1.53	0.75-3.11	0.25	-		-	-
Chronic liver disease	0.58	0.21-1.55	0.28	-		-	-
Autoimmune disease	1.21	0.45-3.26	0.7	-		-	-
Central nervous system disease	1.08	0.64-1.85	0.77	-		-	-
Hematological disorder	1.31	0.24-7.22	0.76	-		-	-
Tumor	0.68	0.35-1.33	0.26	-		-	-
Lac	1.05	0.84-1.32	0.65	-		-	-
OI	1	1-1	<0.001	-		-	-
WBC	1.13	1.07-1.2	<0.001	1.01		0.93-1.09	0.86
HGB	0.99	0.98-1	0.03	1		0.98-1.01	0.6
PLT	1	1-1	0.28	-		-	-
Percentage of NE	1.05	1.03-1.07	<0.001	-		-	-
Percentage of LY	0.95	0.92-0.97	<0.001	-		-	-
Percentage of MO	0.91	0.87-0.96	<0.001	0.96		0.91-1.02	0.17
NLR	1.05	1.03-1.07	<0.001	-		-	-
ALB	0.9	0.86-0.95	<0.001	0.97		0.89-1.05	0.46
GLB	0.98	0.94-1.03	0.44	-		-	-
A/G	0.37	0.16-0.87	0.02	0.71		0.16-3.11	0.66
TBIL	1	0.98-1.02	0.99	-		-	-
DBIL	1	0.98-1.03	0.77	-		-	-
ALT	1	1-1.01	0.26	-		-	-
AST	1.01	1-1.01	0.07	-		-	-
AST/ALT	1.34	1.01-1.79	0.04	-		-	-
eGFR	0.98	0.98-0.99	<0.001	0.99		0.98-1	0.25
CK	1	1-1	0.08	-		-	-
K⁺	1.61	1.1-2.37	0.01	1.19		0.69-2.07	0.53
Na⁺	1.06	1.02-1.11	<0.001	1.02		0.96-1.09	0.46
PTA	0.99	0.98-1	0.23	-		-	-
INR	1.12	0.58-2.16	0.74	-		-	-
FIB	1.1	0.96-1.25	0.16	-		-	-
D Dimer	1.18	1.07-1.31	<0.001	-		-	-
PCT	3.58	2.47-5.19	<0.001	3.48		2.47-5.23	<0.001
CRP	1.01	1-1.01	<0.001	1		0.99-1	0.12
ESR	1	0.99-1.01	0.89	-		-	-
IL10	1	0.99-1.02	0.81	-		-	-
IL6	1	1-1	0.23	-		-	-

Abbreviations: ALB: albumin; A/G: albumin/ globulin; ALT: alanine aminotransferase; AST: aspartate transaminase; AST/ALT: aspartate-to-alanine transaminase ratio; CK: creatine kinase; CRP: C-reactive protein; DBIL: direct bilirubin; eGFR: estimated glomerular filtration rate; ESR: erythrocyte sedimentation rate; FIB: fibrinogen; GLB: globulin; HGB: hemoglobin; INR: international normalized ratio; IL 10: interleukin 10; IL 6: interleukin 6; K⁺: blood potassium; Lac: lactic acid; LY: percentage of leukomonocyte; MO: percentage of monocyte; NE: percentage of neutrophil; NLR: neutrophil-to-lymphocyte ratio; Na⁺: blood sodium; OI: oxygenation index; PLT: platelet; PTA: prothrombin activity; PCT: procalcitonin; TBIL: total bilirubin; WBC: white blood cell;

LASSO regression was used to further screen the statistically significant parameters in univariable logistic regression analysis (P < 0.05). The coefficients of variables are shown in Figure 3A. A 10-fold cross-validation method was applied to the iterative analysis, and a model with excellent variable performance was obtained when λ was 0.0059 (Figure 3B). The variables screened included invasive procedures, diabetes, WBC count, HGB, MO, ALB, A/G, eGFR, K⁺, Na⁺, PCT, and CRP, whose coefficients were not equal to 0 (Table S2). Multivariate logistic regression was further analyzed parameters that were screened by LASSO regression. The results showed diabetes comorbidity (OR = 5, 95% CI = 2.86–8.92, P = < 0.001), previous invasive procedure (OR = 5.17, 95% CI = 2.78–9.69, P < 0.001), and PCT level (OR = 3.48, 95% CI = 2.47–5.23, P < 0.001) were significantly associated with coinfections in elderly COVID-19 patients (Table 2).

The nomogram was developed based on the multivariate logistic regression analysis results with three predictors, diabetes comorbidity, previous invasive procedure, and PCT level (Figure 3C). Based on the ordinary nomogram, we developed a web-based dynamic nomogram application (https://elderly-covid19.shinyapps.io/DynNomapp/) to precisely calculate the probability of coinfections in elderly patients with COVID-19.

3.4 Nomogram validation

3.4.1 Internal validation

The AUCs in the training and internal validation cohorts were 0.86 (95% CI: 0.82–0.90) and 0.82 (0.78–0.87), respectively (Figures 4A and 4B). The Hosmer–Lemeshow test revealed high concordance between the predicted and observed probabilities for the training and internal validation cohorts (P > 0.05, Figures 4D and 4E). The DCA illustrated that the clinical net benefit of the nomogram was higher than the default strategy of "treat all" or "treat none," with a wide threshold probability range in the training and internal validation cohorts for predicting the risk of coinfections in older patients with COVID-19 (Figure 4G and 4H). These results indicated that the proposed model performed well in both the training and internal validation cohorts.

3.4.2 External validation

An independent cohort of 306 elderly inpatients who were diagnosed with COVID-19 between 2 February 2023 and 30 June 2023 at Xiangya Hospital of Central South University in Changsha, Hunan, Jiangxi Provincial People's Hospital in Nanchang, Jiangxi, the Affiliated Nanhua Hospital of University of South China in Hengyang, Hunan, and the First Affiliated Hospital of Xinxiang Medical University in Weihui, Henan were prospectively enrolled for external validation of the nomogram. Comparisons of the baseline characteristics and clinical outcomes between elderly patients with COVID-19 in each of the two stages are shown in Table S3. The independent cohort was used for external validation of the nomogram. The AUC for the external validation cohort was 0.83 (95% CI: 0.78–0.87, Figure 4C). The calibration plot of the nomogram indicated no deviation from the reference line (P > 0.05, Figure 4F). The external validation cohort showed that our nomogram to predict coinfections in elderly patients with COVID-19 achieved positive net clinical benefits over a broad range of threshold probabilities, indicating the high clinical utility of this model (Figure 4I).

3.5 Comparison of the diagnostic performance of the nomogram, PCT alone, and CRP alone

A comparison of the diagnostic performances of the constructed nomogram, PCT alone, and CRP alone to predict the presence of coinfections in elderly patients with COVID-19 is shown in Table 3. The AUC (95% CI) of the nomogram was significantly (P < 0.05) higher than that for PCT alone or CRP alone in all cohorts at 0.86 (95% CI: 0.82–0.90), 0.82 (95% CI: 0.75–0.89), and 0.83 (95% CI: 0.78–0.87), respectively.

With a fixed specificity of 90%, the nomogram had higher sensitivity, accuracy, PPV, and NPV for detecting the presence of coinfections in elderly patients with COVID-19 than PCT alone and CRP alone in all three cohorts (Table 3). Similarly, the test performance of the nomogram at 90% sensitivity in the two validation cohorts was consistent across training validation cohorts. However, close to a sensitivity of 90%, the nomogram had a specificity of 47%, accuracy of 59%, and PPV of 72% in the training cohort, which was slightly lower than those for PCT alone but higher than those for CRP alone. At the maximum Youden's index, the nomogram had an optimal cutoff of -0.30 with a specificity, sensitivity, accuracy, PPV, and NPV of 98%, 64%, 64%, 91%, and 87%, respectively, in the training cohort. All indices of diagnostic performance of the nomogram in the training cohort were higher than those for PCT alone and CRP alone, which is similar to results from the internal and external validation cohorts (Table 3).

Table3. Diagnostic performance of the nomogram, PCT alone, and CRP alone for detection of coinfection in all elderly COVID-19 patients from the three cohorts.

Cohorts		Training cohort (n=465)			Internal validation cohort (n=155)			External validation cohort (n=306)
Variables		Nomogram	PCT	CRP	Nomogram	PCT	CRP	Nomogram	PCT	CRP
AUC (95%CI)		0.86 (0.82-0.90)	0.82 (0.78-0.87)	0.61 (0.55-0.67)	0.82 (0.75-0.89)	0.68 (0.60-0.77)	0.55 (0.45-0.65)	0.83 ( 0.78-0.87)	0.70 ( 0.64-0.76)	0.64 (0.58-0.70)
P*		NA.	0.04	<0.001	NA.	0.005	<0.001	NA.	<0.001	<0.001
Specificity Fixed at 90%^§	Sen.	70%	56%	15%	33%	6%	6%	44%	29%	21%
	Accuracy	85%	81%	70%	77%	76%	71%	70%	63%	59%
	PPV	73%	69%	38%	52%	40%	15%	79%	71%	64%
	NPV	89%	84%	73%	82%	77%	76%	67%	61%	59%
Sensitivity Fixed at 90%^§	Spe.	47%	52%	19%	53%	49%	19%	45%	33%	22%
	Accuracy	59%	61%	39%	62%	56%	36%	65%	58%	53%
	PPV	40%	41%	30%	37%	32%	26%	57%	52%	48%
	NPV	93%	91%	84%	95%	89%	88%	85%	78%	74%
At maximum Youden's index	Optimal cutoff	0.30	0.23	53.45	-2.20	0.07	97.70	-0.72	0.18	30.55
	Spe.	98%	87%	60%	65%	55%	81%	77%	73%	57%
	Sen.	64%	64%	57%	92%	75%	33%	80%	59%	64%
	Accuracy	88%	81%	59%	71%	60%	70%	78%	67%	60%
	PPV	91%	65%	35%	44%	34%	34%	74%	64%	55%
	NPV	87%	86%	78%	96%	88%	80%	83%	69%	66%

* Refers to the comparison of C-statistic and its 95%CI with that of the nomogram in the same cohort.

^§Fixed at specificity or sensitivity closest to 90%.

Abbreviations: CI, confidence interval; NPV, negative predictive value; PPV, positive predictive value; Sens., sensitivity; Spe., Specificity.

We established a nomogram to identify coinfections in elderly COVID-19 patients using a multicenter prospective cohort study. The nomogram offers an easy-to-use and individualised tool with high accuracy and validity. It can enable timely interventions, thus limiting antimicrobial agent overuse, hospitalisation costs, and unfavourable outcomes in elderly patients with COVID-19.

We compared, for the first time, the clinical characteristics and outcomes of elderly and non-elderly patients with COVID-19, both with coinfections. In this study, the incidence of coinfections in the elderly group was 32.6% (data obtained from all elderly adults in two stages, Table S3), which was largely consistent with previously published results(8) but significantly higher than the 7–8% observed in the whole study population(6). Elderly exhibit an increased risk of coinfections, as well as greater disease severity and higher mortality rates with COVID-19 compared with younger adults(24). Moreover, the presentation of acute infections, such as fever and headaches, in the elderly group was atypical and subtle, with a significantly higher incidence of non-hyperpyrexia. Several factors can explain this phenomenon. Cerebrovascular comorbidities were more prevalent in the elderly group in our study, which may have resulted in neurological deficits and subsequently impairment of thermoregulatory/inflammatory reflexes(25). Furthermore, decreased heat production due to reduced muscle mass and blunted peripheral vasoconstriction capacity in older adults may have led to low basal body temperatures(26). Lastly, a reduction in immune cell production and circulating cytokines may have been related to the lower incidence of hyperpyrexia and headache in elderly patients with coinfections(27, 28). Therefore, in clinical practice, more attentions to potential coinfections are needed in elderly patients because the signs and symptoms of acute infection may be less obvious in this population, potential leading to misdiagnosis or underdiagnosis.

Our study presented the characteristics of coinfections, in terms of infection location, infection aetiology, and age distribution of elderly patients with COVID-19. The proportion of coinfections in elderly patients with COVID-19 increased with age, which is consistent with previously published results(8). Notably, pulmonary coinfection is the most common coinfections in elderly patients with COVID-19. SARS‐CoV‐2-induced injury to lung epithelial cells can attract neutrophils and macrophages, promote inflammation, and eventually cause co-invading microbes to bind to cells and proliferate(29). Clinicians need to keenly distinguish other pathogens from SARS‐CoV‐2 because of the overlapping and non-specific symptoms of pneumonia. This study identified the most common coinfecting pathogenic microorganisms in elderly patients with COVID-19 as gram-negative bacteria, namely Acinetobacter baumanii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The prevalence of gram-negative bacteria, especially multidrug-resistant gram-negative bacteria, has increased during the COVID-19 pandemic(30). Healthcare workers should be aware of this increasing trend to optimise antimicrobial prescription patterns in this high-risk patient population. Our study also analysed the incidence of fungal coinfections in elderly COVID-19 patients. In addition to bacterial coinfections, clinicians should be aware of the possible occurrence of fungal coinfections among elderly patients with COVID-19, especially those in the highest age range (> 90). In summary, our results provide evidence to support the need for diagnosis of coinfections in elderly patients with COVID-19, which is crucial for empirical treatment. In addition to restraint in the use of antibiotics, regular microbial sampling may offer current and personalised data to guide the ongoing administration of antibiotics.

No single inflammatory biomarker can capture the complex and variable interactions of coinfections. Thus, we constructed a nomogram based on the joint variables of diabetes comorbidity, previous invasive procedures, and PCT level to identify coinfections in elderly patients with COVID-19. These variables are readily available from routine clinical studies. The colonisation rate of pathogenic microbial strains may increase in conditions of hyperglycaemia, thereby increasing susceptibility to coinfections(31). Invasive procedures, such as tracheal cannulation, tracheotomy, central catheter placement, and CRRT, may remarkably increase the probability of various secondary infections, especially those caused by MDROs. Advances in lifesaving medical practices that expose patients to invasive procedures have been associated with an increase in the incidence of nosocomial infections. Sustained efforts should be made to control aggressive infections during pandemic periods. Improving hand hygiene compliance among healthcare workers is the cornerstone of any preventive approach to reduce healthcare-associated infections and cross-transmission of MDROs(32). Regarding invasive device-associated infections, we suggest that particular attention should be paid to reducing the exposure and dwelling time of devices(33) and that the necessity of their continued use must be evaluated daily(34).

PCT and CRP are well-known systemic markers of inflammation and infection(35). However, in our study, PCT outperformed CRP in predicting coinfections in elderly patients with COVID-19 across all cohorts. There are several possible reasons for this finding. First, PCT increases not only in bacterial infections but also in fungal infections (36); therefore, PCT has a good predictive value for both bacterial and fungal infections. Second, cytokines released following viral infection, such as interferon-gamma, lead to PCT downregulation (37). Therefore, PCT levels may help a clinician discriminate between an isolated case of isolated viral pneumonia and mixed pneumonia in patients with SARS‐CoV‐2 infection during the pandemic season(38). In contrast, the cutoff value for CRP to detect secondary bacterial infections is considerably higher, and the predictive values are slightly less robust(39); however, there is a lack of data on the prediction of fungal coinfections. Our results are in line with the published literature, which shows a stronger correlation between PCT levels and bacterial coinfections in COVID-19, while CRP levels seem to have a lower discriminatory ability(40), as do in predicting fungal coinfections. Third, the different kinetics of PCT and CRP levels should be considered. Méndez et al. showed greater expression of PCT in the first two days of infection and slower CRP kinetics(41) in community-acquired pneumonia, suggesting that PCT could be a more reliable indicator upon admission of elderly patients with COVID-19. In summary, PCT testing upon admission for geriatric patients with COVID-19 may be of particular importance for detecting coinfections.

Early and accurate diagnosis of coinfections is crucial to avoid misuse of antimicrobial agents. Available evidence suggests that a high proportion of patients with COVID-19 receive unnecessary antibiotic treatment(11), which may lead to adverse drug events, secondary opportunistic infections, and antimicrobial resistance. Because of this, the nomogram, which can help identify elderly patients with COVID-19 who have coinfections, can facilitate targeted use of antimicrobials, thus promoting antimicrobial agent stewardship.

Clinicians can quickly assess the probability of coinfections in elderly COVID-19 patients by inputting indicators on the website we constructed (https://elderly-covid19.shinyapps.io/DynNomapp/). The nomogram outperformed use of either PCT and CRP alone in detecting coinfections in elderly patients with COVID-19 with high credibility, effectiveness, and clinical practicability. External validation confirmed that our model was generalizable for elderly patients with COVID-19 during different times and in different locations. Furthermore, the nomogram exhibited a higher predictive value for coinfections in the elderly group with COVID-19 than in the non-elderly group with COVID-19 (AUC: 0.675) (Figure S1), indicating the specificity of this model for older individuals. In consequence, we introduced a web-based application that is a practical, convenient, and accurate online tool for clinicians.

We acknowledge some limitations to this study. First, although our researchers were cautious about diagnosing coinfections, there are still several considerations. For example, not all patients underwent testing for microorganisms during hospitalisation. Clinical diagnoses were made by experienced clinicians based on signs, symptoms, or radiological evidence for those with a high suspicion of coinfections but negative microbiological tests. It remains difficult to distinguish between colonisation, contamination, and infection based on samples that result in positive cultures or detection of microorganisms(42), which represents a critical barrier to coinfections detection and antibiotic stewardship. We might have misclassified some non-coinfected patients as coinfected, resulting in bias. Likewise, some coinfections may have gone undiagnosed in our study.

Second, although the coinfections analysed in our study were defined as coinfections occurring within 48 hours of admission, we did not fully distinguish between community-acquired infections and hospital-acquired infections secondary to a previous admission. We believe that this is more in line with the real-world situation in which most patients are transferred between several hospitals. Therefore, the initial clinician should consider therapeutic measures, such as invasive procedures during the last hospitalisation, in determining the likelihood of coinfections. Third, we excluded patients with incomplete case data, which introduced selection bias and may have influenced the final study results. Finally, the baseline characteristics of elderly patients with COVID-19 in the two study stages were not fully matched. We speculate that this may have occurred because of different infection epidemiologies in the two time periods or measurement errors in different laboratories at the multicenter. However, the high discrimination and satisfactory calibration of the nomogram in the external validation further indicated more robust performance in predicting coinfections in elderly COVID-19 patients.

In summary, we acknowledge the limitations associated with the selection of patients during different waves of the pandemic, as well as those due to the observational nature of the study and missing data. However, the results of this study are representative and reflect the real-world situation of elderly patients with COVID-19 after China abandoned its zero COVID-19 policy. In future research, we aim to promote and test this nomogram in additional centers to validate its clinical benefits.

Our study described the clinical characteristics and constructed a nomogram with high accuracy and validity for the first time, which is promising for diagnostic prediction and antimicrobial agent stewardship in elderly COVID-19 patients with coinfections.

AUC: area under curve; ALB: albumin; A/G: albumin/ globulin; BSI: bloodstream infection; COVID-19: coronavirus disease 2019; CRP: C-reactive protein; CRRT: renal replacement therapy; DCA: decision curve analysis; eGFR: estimated glomerular filtration rate; HGB: hemoglobin; IQR: interquartile range; K⁺: blood potassium; LASSO: least absolute shrinkage and selection operator; MDROs: multidrug-resistant organisms; MO: percentage of monocyte; Na⁺: blood sodium; NPV: negative predictive value; NLR: neutrophil-lymphocyte ratio; OI: oxygenation index; PPV: positive predictive value; PCR: polymerase chain reaction; PCT: procalcitonin; ROC: receiver operating characteristic curve; SSTI: skin and soft tissue infection; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; UTI: urinary tract infection; WBC: white blood cell.

Ethics approval and consent to participate

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the Xiangya Hospital of Central South University, approval No. 202401002. All patients were required to provide written informed consent.

Consent for publication

This manuscript contains only anonymised data. Participants gave consent for the publication of this data.

Availability of data and materials

The raw data supporting the conclusion of this article could be inquired from the corresponding authors.

Competing interests

The authors declare that they have no competing interests.

Funding

This manuscript was supported by grants from the National Natural Sciences Foundation of China (82070613 and 82370638 to RC), the Science and Technology Innovation Program of Hunan Province (2022RC1212 to RC), and the Natural Science Foundation of Hunan Province China (2023JJ10095 to RC and 2021JJ70146 to LW).

Authors' contributions

JZ: Writing – original draft, Writing – review & editing, Investigation. XW: Conceptualization, Investigation, Writing – original draft. JL: Data curation, Methodology, Writing – original draft. ML: Methodology, Validation, Writing – original draft. LW; Data review, Methodology, Writing – review & editing, Funding acquisition; XK; Data review, Methodology, Writing – review & editing; XZ: Data review, Methodology, Writing – review & editing; YH; Data review, Methodology, Writing – review & editing; JQ: Writing – review & editing, Conceptualization, , Investigation, Resources, Visualization; RC: Writing – original draft, Writing – review & editing, Conceptualization, Funding acquisition, Investigation, Resources, Visualization.

Acknowledgments

We thank the study participants for volunteering in this study. We sincerely thank the support of Jiangxi Provincial People's Hospital in data collecting and Yixiang Zheng and Li Wu of Xiangya Hospital for their instruction in the statistics and writing of this manuscript. We would like to thank Editage for English language editing.

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No competing interests reported.

Supplementarymaterial.docx

A novel nomogram for the early identification of coinfections in elderly patients with COVID-19

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Trial registration:

Figures

1. Background

2. Materials and Methods

2.1 Study design and data collection

2.2 Definitions

2.3 Statistical analysis

2.4 Sample size

3. Results

3.1 Demographics and characteristics upon admission

3.2 Clinical outcomes

3.3 Characteristics of coinfections in elderly patients with COVID-19

3.3 Nomogram to identify early coinfections in elderly patients with COVID-19

3.4 Nomogram validation

3.4.1 Internal validation

3.4.2 External validation

3.5 Comparison of the diagnostic performance of the nomogram, PCT alone, and CRP alone

4. Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1