Relationship between cell number and Ct.

<p>Log-linear relationships between the copy number of pathogen-specific sequence and Ct_pathogen and between the copy number of the human-specific sequence and Ct_human. (A) <i>S. pneumoniae</i>, <i>H. influenzae</i>, <i>Pseudomonas</i> spp., or <i>M. catarrhalis</i> was suspended in sputum. DNA was then purified from the suspension, and the target sequences specific to each organism were amplified by PCR. A log-linear relationship indicates that the sputum does not contain molecules that inhibit isolation of DNA or exponential amplification by PCR. Experiments were done in triplicate. A bar indicates standard deviation. (B) Human genomic DNA isolated from sputum was serially diluted, and a DNA sequence in the human SFTPC gene (arbitrarily selected from human genes, of which sequence is specific to human by BLAST search of GenBank database; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024474#pone-0024474-t002" target="_blank"><b>Table 2</b></a>) was amplified. A log-linear relationship indicates that the sputum does not contain molecules that inhibit isolation of DNA or exponential amplification by PCR.</p

List of target genes.

<p>*These organisms may be found in a healthy airway on rare occasions. When detected in pneumonic patients, they are very likely to be the causative pathogen. We therefore list them as non-commensal organisms.</p><p># F: forward primer, B: backward primer, D: detection probe. Each primer set was tested against the DNA panel and was confirmed to provide specific amplification only for its target sequence. The panel contains DNA from human, from 65 organisms, and from 2 organisms that harbor drug resistance-related genes; the organisms include <i>Streptococcus pneumoniae</i>, <i>Haemophilus influenzae</i>, <i>Moraxella catarrhalis</i>, <i>Pseudomonas aeruginosa</i>, <i>Klebsiella pneumoniae</i>, <i>Stenotrophomonas maltophilia</i>, <i>Staphylococcus aureus</i>, <i>Mycoplasma pneumoniae</i>, <i>Chlamydophila pneumoniae</i>, <i>Chlamydophila psittaci</i>, <i>Legionella pneumophilia</i>, <i>Mycobacterium tuberculosis</i>, <i>Mycobacterium intracellulare</i>, <i>Mycobacterium avium</i>, <i>Mycobacterium kansasii</i>, <i>Nocardia asteroids</i>, <i>Pneumocystis jiroveci</i>, <i>Acinetobacter baumannii</i>, <i>Anaerococcus hydrogenalis</i>, <i>Aspergillus flavus</i>, <i>Aspergillus fumigates</i>, <i>Aspergillus niger</i>, <i>Bacteroides caccae</i>, <i>Bacteroides fragilis</i>, <i>Bacteroides thetaiotaomicron</i>, <i>Candida albicans</i>, <i>Candida parapsilosis</i>, <i>Clostridium perfringens</i>, <i>Clostridium ramosum</i>, <i>Corynebacterium</i> spp., <i>Coxiella burnetii</i>, <i>Cryptococcus neoformans</i>, <i>Eikenella corrodens</i>, <i>Enterobacter aerogenes</i>, <i>Enterobacter cloacae</i>, <i>Enterococcus faecalis</i>, <i>Enterococcus faecium</i>, <i>Escherichia coli</i>, <i>Haemophilus haemolyticus</i>, <i>Haemophilus parainfluenzae</i>, <i>Klebsiella oxytoca</i>, <i>Legionella bozemanii</i>, <i>Legionella micdadei</i>, <i>Mycobacterium gordonae</i>, <i>Neisseria meningitides</i>, <i>Peptostreotococcus anaerobius</i>, <i>Porphyromonas asaccharolytica</i>, <i>Prevotella bivia</i>, <i>Prevotella intermedia</i>, <i>Proteus mirabilis</i>, <i>Serratia marcescens</i>, <i>Staphylococcus auricularis</i>, <i>Staphylococcus epidermidis</i>, <i>Staphylococcus simulans</i>, <i>Streptococcus anginosus</i>, <i>Streptococcus constellatus</i>, <i>Streptococcus intermedius</i>, <i>Streptococcus mitis</i>, <i>Streptococcus pyogenes</i>, <i>Streptococcus uberis</i>, <i>Trichosporon asahii</i>, and <i>Trichosporon mucoides</i>; drug resistance-related genes include IMP and mecA.</p

Protocol for the prospective observatory study.

<p># The number was determined so that the primary endpoint is evaluated at a resolution of 0.1.</p

Battlefield hypothesis.

<p>(A) When pneumonia occurs, the numbers of both the causative pathogen and human inflammatory cells increase at the inflammation site. Meanwhile, the colonizing pathogen lags behind. The ratio of pathogen to human cells may be a good indicator for the differentiation of the causative pathogen from the colonizing pathogen. (B) The cell number ratio is measurable by quantitative PCR. The Ct (threshold cycle) is the PCR cycle at which a statistically significant fluorescent signal is first observed. Ct_pathogen is the Ct for the pathogen-specific gene, Ct_human is the Ct for the human-specific gene, and both are log-proportional to the number of the cells (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024474#pone-0024474-g002" target="_blank"><b>Figure 2</b></a>). Accordingly, ΔCt_pathogen =  −(Ct_pathogen−Ct_human) is log-proportional to the ratio of pathogen to human cells. (C) Because ΔCt_pathogen indicates the ratio of pathogen to human cells, we may be able to determine the ΔCt_pathogen cutoff, a ΔCt_pathogen value above which a pathogenic role of the pathogen in pneumonia is strongly suggested.</p

Patients' characteristics.

<p>Patients' characteristics.</p

List of pneumonia-causing pathogens.

<p>& values indicated are the averages from the references cited.</p><p>$ includes <i>Escherichia coli,</i><i>Enterobacter</i> species, Proteus species, and <i>Serratia</i> species.</p><p># includes anaerobes, <i>Staphylococcus</i> species (other than <i>S. aureus</i>), <i>Streptococcus</i> species (other than <i>S. pneumoniae</i>), <i>Acinetobacter</i> species, and <i>Aspergillus</i> species.</p><p>*includes viruses and <i>Coxiella burnetii</i>.</p

Determination of the ΔCt cutoff.

<p>(A) Selection of purulent sputum. Sputum was classified by its gross appearance, with 50 samples studied for each classification. Purulent sputum had a Ct_human <27 (>7×10³ human cells/µL of sputum; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024474#pone-0024474-g002" target="_blank"><b>Figure 2B</b></a>). Samples with M2–P3 appearance as well as a Ct_human <27 (enclosed by a dotted line) were studied further. Classification of the gross appearance of the sputum (M1, M2, P1, P2, and P3) are according to Miller and Jones <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024474#pone.0024474-Miller1" target="_blank">[10]</a>. (B) Determination of the ΔCt cutoff. ΔCt_pathogen was measured for 4 representative commensal organisms (n = 223). Samples from patients with pneumonia in which a likely causative pathogen was identified using criteria (1)–(4) (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024474#s2" target="_blank">Methods</a>) are shown as blue circles, and samples from patients with pneumonia in which none of criteria (1) – (4) was fulfilled were shown as white circles. The ΔCt_pathogen cutoff (a red line) was defined as the smallest ΔCt_pathogen for the blue circles. Sputum in which the pathogen was not detected and thus ΔCt_pathogen was not assigned is shown at the bottom (labeled as “Not detected”). (C) Reproducibility of ΔCt_pathogen measurements. Duplicate samples were isolated from a single patient in a single day (n = 28), and each of the duplicate samples was independently measured for ΔCt_pathogen. Both of the measurements provided ΔCt_pathogen located on the same side (above or below) of the cutoff. Red line: the cutoff for each organism. (D) Temporal profile of ΔCt_pathogen during antibiotic treatment. A single sample set contains multiple sputum samples isolated from a single patient during antibiotic treatment. A total of 9 consecutive sample sets that included 7 of pneumonia with ΔCt_pathogen for <i>S. pneumoniae</i> > cutoff and 2 of pneumonia with ΔCt_pathogen for <i>H. influenzae</i> > cutoff at day 1 were studied. ΔCt_pathogen decreased to below the cutoff in the course of treatment.</p

A prospective study.

<p>(A) ΔCt_pathogen for each commensal organism (n = 153). Samples in which real-time PCR failed to detect the organism are shown at the bottom (“Not detected”). The ΔCt_pathogen cutoff demarcated well the samples obtained from the patients in whom the likely causative pathogen was identified by criteria (1) – (4). (B) Interrelationship between the pathogens detected. Samples obtained from the patients in whom the other 3 commensal organisms were identified as a likely causative pathogen or samples in which a non-commensal organism was detected by real-time PCR are colored. Most of the colored circles are located below the cutoff line.</p

Additional file 1 of Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography – a multicenter retrospective cohort study in Japan

Supplementary Material

Additional file 1 of Impact of upper and lower respiratory symptoms on COVID-19 outcomes: a multicenter retrospective cohort study

Additional file 1. Supplemental Figure 1. Study flow chart of patient identification and selectionStudy flow chart of patient identification and selection. A total of 117 records were excluded from the 3431 cases registered in the coronavirus disease 2019 (COVID-19) taskforce database owing to lack of essential clinical information. Ultimately, 3314 patients met the eligibility criteria, of which 2709 had respiratory symptoms. Supplemental Figure 2. Frequency of assisted respiration therapy and death in all four groups (a) Univariate analysis of the proportion of high-flow oxygen therapy with COVID-19 in each group. (b) Univariate analysis of the proportion of use of invasive mechanical ventilation (IMV) with COVID-19 in each group. (c) Univariate analysis of the proportion of use of extracorporeal membrane oxygenation (ECMO) with COVID-19 in each group. (d) Univariate analysis of the proportion of death with COVID-19 in each group. Supplemental Table 1.　Common non-respiratory symptoms in each group