SYSTEM AND METHOD FOR DETERMINING ONSET AND DISEASE PROGRESSION

Abstract

The invention disclosed herein concerns screening and early detection of a variety of disease conditions in seemingly healthy subjects, enabling early intervention and treatment.

Claims

1-57. (canceled)

58. A method for determining presence of at least one disease-associated marker in a breath sample from a subject, the method comprising exposing to a breath sample at least one sampling unit comprising one or more adsorbing regions capable of reversibly associating volatiles in said breath sample, the adsorbing region being different from a metallic surface or metallic nanoparticle; and analyzing the at least one sampling unit being exposed to the volatiles to identify volatiles adsorbed onto the one or more adsorbing regions and determining presence of said at least one disease-associated marker; wherein an increase in an amount of said marker as compared to an amount of said marker measured at an earlier time point being indicative of existence of a disease state.

59. The method according to claim 58, wherein the increase in the amount of said marker as compared to the amount measured at an earlier time point is of at least 50%.

60. The method according to claim 58, carried out one or more times at various time points to detect a change in the amount of the marker.

61. The method according to claim 58, wherein each of the one or more adsorbing regions is configured to reversibly associate to volatiles in said breath sample.

62. The method according to claim 58, wherein the one or more adsorbing regions being formed of a solid adsorbent configured to physically trap the volatile materials, selected from the group consisting of at least one material selected from organic porous polymers, ion-exchange resins, carbon molecular sieves, and sulfonated polymers, a material selected amongst carbon adsorbents, carbon allotropes or carbonaceous materials, wherein the one or more adsorbing regions is a material having a surface area between 5 and 1500 m.sup.2/g, a density of between 0.2 and 0.7 and/or a micropore diameter between 4 and 300 A.

63. The method according to claim 62, wherein the carbon adsorbents are selected amongst graphitized carbon blacks having a 20/40 mesh, graphitized carbon blacks having a 60/80 mesh and carbon molecular sieves, selected from the group comprising of Carbotrap F, Carbotrap C, Carbotrap Y, Carbotrap B, Carbotrap X, Carbopack F, Carbopack C, Carbopack Y, Carbopack B, Carbopack X, Carboxen 1016, Carboxen 569, Carboxen 1021, Carboxen 1018, Carbosieve S-III, Carboxen 1003, Carbosieve G, Carboxen 1000 and Carboxen 1012 wherein the one or more adsorbing regions is a material having a surface area between 5 and 1500 m.sup.2/g, a density of between 0.2 and 0.7 and/or a micropore diameter between 4 and 300 A.

64. The method according to claim 58, wherein following exposure of the one or more adsorbing regions to a breath sample, said adsorbing regions are treated to cause desorption or dissociation of the volatiles from the surface, and analyze the desorbed volatiles.

65. The method according to claim 64, wherein the volatiles are analyzed by gas-chromatography (GC), GC-lined mass-spectrometry (GC-MS), proton transfer reaction mass-spectrometry (PTR-MS), electronic nose device (E-nose), quartz crystal microbalance (QCM), infra-red spectroscopy (IR) or ultraviolet spectroscopy (UV).

66. The method according to claim 58, wherein the disease-associated marker is a marker indicative of a bacterial, viral or fungal disease, the virus selected from the group comprising of an enveloped or non-enveloped virus, a norovirus or parvovirus, an influenza virus or a coronavirus, preferably SARS-CoV-2.

67. The method according to claim 66, wherein the disease is a hospital associated infection (HAI), caused by methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), Clostridium difficile, Acinetobacter baumannii, or multi-drug resistant (MDR) Acinetobacter sp.

68. A method for determining presence of at least one pathogen in a subject's body, the method comprising exposing to a breath sample at least one sampling unit comprising one or more adsorbing regions capable of reversibly associating to volatiles in said breath sample, the one or more adsorbing regions being different from a metallic surface or a metallic nanoparticle; and analyzing the at least one sampling unit to identify the volatiles adsorbed onto the one or more adsorbing regions to determine presence of at least one pathogen-associated marker; wherein the presence of said marker is indicative of existence of the pathogen in the subject's body.

69. The method according to claim 68, wherein the method is repeated one or more times to determine a change in an amount of said marker, wherein an increase in the amount of said marker as compared to an amount of said marker measured at an earlier time point is indicative of existence of a disease state.

70. The method according to claim 68, wherein the pathogen is a virus, a bacterium, or a fungus.

71. The method according to claim 68, wherein the subject is asymptomatic, or ventilated.

72. The method according to claim 70, wherein the pathogen is VAP causing.

73. A method for determining onset of VAP in a ventilated subject, the method comprising exposing to a breath sample from a ventilated subject at least one sampling unit comprising one or more adsorbing regions capable of reversibly associating to volatiles in said breath sample; and analyzing the at least one sampling unit to identify volatiles adsorbed onto the one or more adsorbing regions to determine presence of at least one marker of a VAP-causing pathogen; wherein the presence of said marker is indicative of onset of VAP.

74. The method according to claim 73, wherein the at least one sampling unit is provided in a respiratory system typically used in ventilating the subject.

75. The method according to claim 74, wherein the method comprises exposing at least one sampling unit positioned at an outlet line of a respiratory system to a breath sample exhaled by the subject, the at least one sampling unit comprising one or more adsorbing regions capable of reversibly associating to volatiles present in said sample; analyzing the at least one sampling unit to identify the volatiles adsorbed onto the one or more adsorbing regions to determine presence of at least one pathogen-associated marker, wherein the presence of said marker being indicative of existence of the pathogen-associated marker in the ventilated subject's body.

76. The method according to claim 75, wherein the at least one sampling unit is in a form of a vessel comprising the one or more adsorbing regions and allowing a timed residence contact of the breath sample with the one or more adsorbing regions.

77. The method according to claim 75, wherein the disease-causing pathogen is a bacterium, a virus or a fungus.

78. The method according to claim 75, the method comprising detaching the at least one sampling unit from the outlet line of the respiratory system and analyzing same to determine volatiles adsorbed onto the one or more adsorbing regions.

79. The method according to claim 78, wherein the volatiles adsorbed onto the one or more adsorbing regions are desorbed and thereafter analyzed.

80. The method according to claim 75, wherein analysis is carried out by a spectrometric method.

81. The method according to claim 75, the method comprising comparing the materials adsorbed onto the one or more adsorbing regions to a marker database to identify the material indicative of the presence of a disease-causing pathogen, being indicative of onset of a disease.

82. The method according to claim 75, wherein the presence of the disease-causing pathogen is indicative of onset of a disease, associated with a bacterium.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0137] In order to better understand the subject matter that is disclosed herein and to exemplify how it may be carried out in practice, embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which:

[0138] FIG. 1 is a schematic representation of a mechanical ventilator (MV) showing several locations of a sampling or collecting units in accordance with embodiments of the invention.

[0139] FIG. 2 is a schematic representation of a parallel and a tandem arrangement of a mechanical ventilator (MV) showing several locations of several sampling or collecting units according to certain embodiments of the invention.

[0140] FIG. 3 is a schematic drawing of an exemplary VADS (Ventilated Associated Detection System) of the invention, where a single analytical chemistry unit is deployed, being processed in the cloud server and provides a patient's status in different forms.

[0141] FIG. 4 is a schematic representation of an analytical chemistry unit used by the present invention.

[0142] FIG. 5 is a schematic representation of a cloud server sub-system for data analysis.

[0143] FIG. 6A provides a computerized output of a GC-MS raw data after processing in a data analysis software entity.

[0144] FIG. 6B provides an enlarged picture of a particular target molecule indicative of the presence of a certain bacterium in an exhaled breath of a patient.

[0145] FIG. 6C provides the bacterial load of a patient showing characteristic markers indicative of the microbiome.

[0146] FIGS. 7A-7C provide bacterial load of Staph (MRSA), Acinetobacter (9) and Klebsiella, respectively of a patient.

[0147] FIGS. 8A-8C provide bacterial load S.Aureus,Act, Acineto-bacter and Pseudo-monas, respectively of a patient.

[0148] FIGS. 9A-9E provide detection of bacterial load by detecting “fingerprints” (cluster of compounds) that were previously found to be characteristic of a certain bacterium.

[0149] FIGS. 10A-10D provide detection of bacterial load by detecting functional groups that were previously found to be characteristic of a certain bacterium.

[0150] FIGS. 11A-11D provide detection of bacterial load by detecting specific single compounds that were previously found to be characteristic of a certain bacterium.

[0151] FIGS. 12A-12E provide a method by which the concentration of a specific compound (1-ethyl-4-methyl-benzene—FIG. 11B) is determined in exhaled air.

[0152] FIGS. 13A-13F provide concentrations of compounds indicative of 3 specific bacteria in patients diagnosed with pneumonia vs. healthy individuals.

DETAILED DESCRIPTION OF EMBODIMENTS

[0153] The present invention is directed to methods and systems as disclosed herein.

[0154] For the purpose of demonstrating the uniqueness of the technology, a complex medical condition was chosen as a test. VAP involves a pathogen, a subject who is typically uncooperative, who has an existing active medical condition and a potentially evolving life-threatening disease which early detection is highly sought for. Thus, the examples provided herein demonstrate the capabilities of the technology disclosed herein in (a) early detection of a pathogen (before symptoms associated with the pathogens are apparent), (b) the ability to detect the pathogen or disease at an early stage and prevent its development and complications that may be fatal if not detected at an early stage, (c) detection of a pathogen in a subject already suffering from an active disease, (d) the ability to distinguish between markers associated with the pathogen and other illness-causing factors, (e) in collecting samples from a subject who is uncooperative, (f) the ability to monitor treatment and relate it to the levels of markers, [i.e., decrease in marker levels may be related to decrease in pathogen activity (effectiveness of treatment). In contrast, increase of marker levels under treatment may indicate that treatment is not effective and there is need to change treatment (use of antibiotics, use of different antibiotics, physical therapy, positional changes etc.)], (g) periodic scanning of mechanical ventilators for contaminants, (h) compare marker levels in all mechanical ventilators (MV) in an ICU unit and detect contamination with endemic bacterium; and others.

[0155] Thus, methods of the invention aim at providing a method for early detection of a bacterial condition, disease or disorder in a subject; a method for determining onset of a bacterial condition, disease or disorder in a subject; a method for preventing development or delaying the progression or onset of a bacterial condition, disease or disorder in a subject. These methods are conducted according to the present invention by sampling exhaled air coming out of a subject; sampling being performed by adsorbing materials/compounds that are present in the exhaled air, desorbing the adsorbed material/compounds, analyzing them and comparing them to a predetermined known database that contains information pertaining to such material/compounds that are discharged by bacteria to their surroundings.

[0156] The present invention is also directed to a system for accurate and quantitative determination of compounds present in exhaled and/or inhaled air from the respiratory system of a living animal, preferably a mammal, preferably human. These compounds are volatile and semi-volatile compounds (VOC, sVOC), preferably organic compounds. The system detects such compounds by sampling air exhaled and inhaled from the human lungs and adsorbs the compounds present in the air while the sampled air passes through appropriate units that comprise appropriate sorbents that adsorb these compounds in a manner that preferably does not alter their chemical properties and structure. Such sampling requires considerable amounts of air to pass through the appropriate units and hence according to the present invention the system may be used in patients being mechanically ventilated such that the pressure exerted by the ventilator provides deriving force for allowing sufficient amount of exhaled and/or inhaled air to be sampled. Passive sampling is also possible, for example taking an air sample of air exhaled from the nose to prevent sampling the bacterial/viral/fungal population in the mouth.

[0157] The compounds adsorbed on the sorbents of the appropriate units are usually desorbed by heating to a temperature above the boiling points of the adsorbed compounds, yet below the temperature the sorbent begins to break down chemically. The desorbed compounds are transferred to analytical unit that separates and identifies the compounds. By identification it is meant identifying both the chemical nature and chemical properties of these compounds, their grouping, such as alkyls, alkenes, alkynes, alcohols, amines, aromatic, cyclic, heteroatoms such as P, N, O, S, present in any of the above groups, and their relative amounts in the sampled air. A further analysis system receiving the data identified by the analytic unit and provides a chemical picture, i.e. a list of the compounds that were detected and identified and provides an indication of compounds that are present in the respiratory system of the individual whose air is sampled.

[0158] This obtained list of compounds includes numerous compounds and their intensities, and their connection to a disorder or a diseased stage is not straightforward and should be elucidated. For such elucidation the present invention makes use of two other features present in the analysis system. The two other features are a unique database and unique algorithms; both are part of the present invention.

[0159] The unique database includes data that were previously obtained that include lists of grouped identified compounds discharged to the surroundings by bacteria where the volatile and semi-volatile compounds produced and discharged to the surroundings of the bacteria were captured, identified and serve as a base for comparison. Each bacterium discharges its own unique compounds at certain relative amounts and the entire list of compounds is the “fingerprint” of this particular bacterium that includes biomarkers. The biomarkers serve for analysis according to the present invention.

[0160] The unique algorithm of the present invention compares between the groups of biomarkers being a cluster of biomarkers present in the database and the compound, or compounds that are identified in the exhaled air. Hence a match between the compound obtained by the analyzed sampled air of the individual and the clusters in the database serves as an indication of the presence of the particular microbiome indicative of a certain bacteria in the respiratory system of the individual and hence the disorder or disease associated with such bacteria is identified.

[0161] The background compounds that are present in the respiratory system of the individual originating from the surroundings of the individual and the apparats in his vicinity may be or may not be taken into account (by means of subtracting background). These include compounds present in the source of air of the ventilator system, the microbiome of the specific hospital, clinic the individual is placed in and compounds discharged by medical equipment in the individual surroundings. These compounds will enter the respiratory system through the air incoming into the lungs and hence sampling the air in the inlet tube of the ventilator, elucidating the compound(s) as done for the exhaled air and subtracting it from the compound(s) present and identified in the exhaled air that may provide a cleaner picture of the compounds that originate in the respiratory system due to a disorder or a disease.

[0162] The present invention thus demonstrates and exemplifies a relevant portion of the group of compounds discharged by various bacterium, identifying the nature and relative amounts of the compounds that are produced by these bacterium and produce the list of various specific compounds that are indicative of the existence of these bacteria in the air that is sampled. These compounds are termed target molecules and are indicative of this specific bacterium. The group of compounds and their relative amounts are indicative of a certain bacterium (or bacteria) and therefore serve as biomarkers unequivocally identifying the bacterium or bacteria. According to the present invention, identifying biomarkers of one or more bacterium in the exhaled air in a certain measurement and an increase in its concentration relative to a previous measurement is indicative of the disorder, condition or disease that is known to be associated with this specific identified bacteria. Identifying the biomarkers of one bacterium or a cluster of biomarkers, that is a group of more than one biomarker, are indicative of a disorder, a conditioned or a diseased state.

[0163] It should be understood that each bacterium is identified by its characteristic compounds. A certain bacterium at a given situation may produce and discharge to its surroundings one or more compounds where the compound or compounds that is/are produced and discharged vary in their amounts when comparing one bacterium to another different bacterium. Overlap of single (one or more) of compounds discharged by two different bacterium is frequently found, however, the complete identified spectra of a specific, well identified bacterium is different than that of another bacterium by the fact that the quantities and entire list of compounds of the specific bacterium are different than another bacterium. Further, each of the compound(s) produced and discharged is/are identified in the analytical system by its retention time (RT). Calculating the relative retention times (RRT) for each set of compounds discharged from a specific bacterium provides more a accurate evidence of a specific bacterium. Therefore mere identification of compounds, let alone one compound in the air sampled from the respiratory system of an individual is not sufficient for identifying the presence of a certain bacterium since different bacterium may produce and discharge to the surroundings the same compound (albeit at different concentration).

[0164] The present invention is thus directed to identifying diseases and disorders that are associated with bacterial infection allowing to collect volatile and semi-volatile compounds discharged to the surroundings of bacteria by the bacteria, identify these compound, their intensities, and compare these with a database that includes previous collect data on nature of compounds discharged by known bacteria and the intensities of each such compound.

[0165] In particular, the present invention is directed to identifying disorders, diseases or conditions associated with the lungs, non limiting examples being Bronchiectasis, Emphysema, Chronic Bronchitis, Chronic Obstructive Pulmonary Disease (COPD), Asthma, Pneumonia, Pleural Effusion (PE) and ventilator-associated pneumonia (VAP).

[0166] FIGS. 1 and 2 schematically depict a mechanical or any other type of a ventilator unit 1. According to a proper operation of the unit, air is forced from the unit through an inhaling tube 2, in the direction shown by the arrow, and the intubation tube 7 to the patient's lungs. Compounds present in the lungs, preferably volatile organic compounds (VOC's), markers, are taken with the exhaled air from the lungs through the intubation tube 7 and exhale tube 3, in a direction shown by the arrow. The exhaled air is moved out of the lungs either passively or due to pressure exerts by the unit on the breathing system of the subject. Alternatively, it may also be moved out by a pump. One or more sampling units 4, optionally equipped each with a valve 5 or a T-shaped connection, are installed in either or both the inhale 2 and exhale 3 tubes where they passively and/or actively absorb VOCs.

[0167] In some cases, location of the one or more sampling units 4 may be inside the inhale and/or exhale tubes and/or perpendicular to the inhale and/or exhale tubes. When located perpendicular to the inhale and/or exhale tubes, the location is typically at a distance of 1-200 cm, 1-20 cm, 10-60 cm, 50-100 cm, 100-150 cm or 150-200 cm from the bifurcation 6 that separates the inhale tube from the exhale tube. The number of units in the inhale and/or exhale tubes may vary and can be one or more per inhale or exhale tube.

[0168] Location of the sampling unit perpendicular to the inhale or exhale tube is typically at a distance of between 1-50 mm, 1-30 cm, between 1-20 cm, between 1-10 cm or between 1-5 cm from the flow to minimize the Venturi effect and the resulting turbulence in the sampling unit. A flow-limiter (not shown) may be located at the distal end of the unit to control flow rate, increase VOC uptake, minimize fluctuations and, thus, provide high repeatability. The valves 5 which are optional (and are for illustration only) are positioned close the connection to the inhale tube 2 or exhale tube 3 when the unit 4 is not connected or is detached. Any other means such a T-shaped connector which tip is closed when the unit is shut, is also possible.

[0169] In FIG. 1, a single sampling unit 4 is shown that is positioned perpendicular to tube 2 and a single unit is positioned relative to tube 3. In FIG. 2, a plurality of sampling units is shown, grouped equally at each tube 2 and 3. The number of sampling units may be one or more, wherein the units may be positioned at the inhale tube 2 or the exhale tube or on both. Typically, at least one sampling unit is positioned at the exhale tube 3. In cases where the number of tubing is greater than 1, namely two or more, and in cases where two or more sampling units are positioned at the exhale tube or the inhale tube, the units may be arranged in parallel or in tandem, as shown in FIG. 2. While a parallel setting is shown positioned at the inhale tube and a tandem setting at the exhale tube, this is not limiting and any combination of setting for each of the inhale and exhale tubes may be used.

[0170] Turning to FIG. 3, a ventilated associated detection system (VADS) 100 is shown. The system comprises of a plurality of sampling units labeled (as Collecting units) CU1, CU2, CU3 . . . etc, (50) which chemical contents is transferred by a sample connection that may be an autosampler (60) to an analytical chemistry unit 101. Data obtained from the analytical chemistry unit 101 is delivered to a central server (not shown) or a cloud server 201. At the central server/cloud—data is inserted to analysis algorithm and compared to metadata bank and to patient's previous samples. The metadata bank includes sets of ‘clusters’ being several VOC's which are specific to a particular disease or pathogen (determined in accordance with the present invention). The ‘clusters’ may be also sub-classified to a specific pathologic agent (e.g., to a specific bacterium causing the disease) or to a specific sub-population of patients (e.g., a group of diabetic patients with VAP). The metadata also includes data on the patient and data base on known microbiomes and the hospital's microbiomes which may also be used in assessing the patient's condition. Algorithm(s) determine statistical probability for change is patient's status that may indicate the onset of an infection, cardiovascular status, nutritional status, cancer status and reports said to staff. “Change” implies a pre-determined gradient (change) in concentration of cluster elements over a number of successive sampling events. The output is in turn delivered for further action/treatment or for storage. The output may be subsequently delivered to a nurse/doctor's station 301, a mobile application 401, a hospital information system 501 and to others.

[0171] In FIG. 4 an analytical chemical unit 101 is provided. It includes one or more GC-MS instruments such as 101A and 101B connected to a PC 102 and running a software illustrated in 103. The contents of the collecting units 4 (shown in FIGS. 1 and 2) that contain patient's samples, is being fed to the GC-MS via an autosampler for digital conversion and generating of digital raw data. The GC-MS output raw data is processed in the data analysis software entity and patient specific data measurement is provided, which is packed, encrypted and sent over the internet network using an IP protocol. The packed data includes the current measurement as well as the patient ID, time stamp and/or other related information. The user interface (UI) provides the technician with the ability to monitor and control the GC-MS operation as well as monitor and control of the analysis process.

[0172] In FIG. 5 a cloud server subsystem is shown in 201A. The cloud server receives and manages information from multiple analytical chemistry units deployed in the system. A non-limiting embodiment of the present invention provided in this figure is the division of the system into two main groups 202 and 203. System 202 includes the software includes the IP protocols that send and receive information between the cloud to the chemistry units and remote stations, and the security and authorization entity which encrypt and decrypt each patient data and provide the authorization for accessing the information. It also includes the cloud server event management entity. These may include new data that is received, patient alarm, failures indication or any other system events, and statistic/analytic query for inquires required by user and/or other software entities. System 203 includes the main software processing related to this invention. The cloud server data base 204 comprises patient data base, storing measurement history from specific patients taken in different time interval, and cluster data base related to specific molecules signals characteristic which is used for the prediction of a specific disease. The data access and management software entity is the interface to store and retrieve data from the relevant data base. The cluster analysis software algorithm is getting new measurement from the patient, compare and process it with information retrieved from the cluster data base and previous patient measurements. The patient status evaluation algorithm software is responsible to predict patient disease of specific type and/or indication of treatment effectiveness and/or generate an alarm for one or more of the remote stations. The algorithm determines the level of change between consecutive measurements from the initial patient baseline when patient arrives to the ICU to the latest, and based on the gradient characteristic providing the status of patient and statistical prediction of specific disease and the effectiveness of treatment given based on early detection. A Machine Learning (ML) algorithm entity is used for continuous improvement of the prediction made based on the gradient measurement using the entire patient's history data collected in the system. It is used for improvement of the statistical false alarm and misdetection of the system over time.

[0173] FIG. 6A demonstrates a computerized output of a GC-MS raw data after its processing in the Data Analysis software entity, generating patient specific data measurement. FIG. 6B provides an enlarged image of a particular target molecule indicative of the presence of a certain bacterium in an exhaled breath of a patient. FIG. 6C provides the bacterial load of the patient, showing characteristic target molecules of the microbiome of the patient.

[0174] In FIGS. 7A, 7B and 7C, the bacterial load of three known bacteria in a certain patient diagnosed according to the present invention are displayed. In particular, Staph (MRSA), Acinetobacter (9) and Klebsiella, respectively, are demonstrated.

[0175] In FIGS. 8A, 8B and 8C, the bacterial load of three known bacteria in a certain patient diagnosed according to the present invention are displayed. In particular, S. Aureus, Act, Acineto-bacter and Pseudo-monas, respectively are demonstrated.

[0176] Turning to FIGS. 9A-9E, the detection of a bacterial load of Acinetobacter (FIG. 9B, FIG. 9C), Pseudomonas (FIG. 9D) and klebsiella (FIG. 9E) is demonstrated. The referral of “Acinetobacter” with a specific indication of a day, i.e. “day 1”, “day 5”, etc. relates to the findings of the bacterium by a conventional method of taking lavage from the lung, growing on an appropriate substrate, and detecting by time of flight. The reason for the conventional method of finding only Acinetobacter is associated with the mechanism with which it is carried, i.e. the exact location in the lung the lavage is taken, the development of the colonies on the growth medium and the sample taken for analysis. The results of the present invention are given as concentrations of peaks representing target molecules that vary in amount over time. Hence of the 2461 compounds that are detected by the GC-Ms system (FIG. 9A), only the concentrations of 41 target compounds (previously elucidated) are elected (FIG. 9B). FIG. 9C demonstrates the concentrations of 20 compounds characteristic of Acinetobacter (previously elucidated) and their development over time. Clearly the presence of growing amounts of Acinetobacter is already found on the 4.sup.th day, prior to the detection of VAP by conventional means (X-ray). FIG. 9D demonstrates the same for Pseudomonas and FIG. 9E for klebsiella. Hence while the conventional route is restricted to the efficiency in the extraction of lavage (depth and location in the lung), the present invention samples all compounds exhaled and therefore provides a fuller more detailed picture of ALL bacterium that are present as each of these bacteria is reflected by its characteristic compounds—target compounds.

[0177] Turning to FIGS. 10A-10D, the detection of the bacterial load of Acinetobacter (FIG. 10C) and Pseudomonas (FIG. 10D) is demonstrated. The referral to “Acinetobacter” or “pseudomonas” with a specific indication of a day, i.e. “day 1”, “day 5”, etc. relates to the findings of the bacterium by a conventional method of taking lavage for the lung, growing on an appropriate substrate, and detecting by time of flight. The results according to the method of the present invention are given as concentrations of peaks representing molecules that vary in amount over time. FIG. 10A demonstrates detection of alcohols (277 compounds) that are characteristic of bacteria (previously elucidated). FIG. 10B demonstrates detection of aldehydes (789 compounds) that are characteristic of bacteria (previously elucidated). FIG. 10C provides the detection of Acinetobacter based on its (previously elucidated) 19 compounds and FIG. 10D provides the detection of Pseudomonas based on its (previously elucidated) 16 compounds.

[0178] Turning to FIGS. 11A-11D, the detection of bacterial load of three different bacteria based on specific compounds (previously elucidated) is demonstrated. The referral to “Culture Staphylococcus” with a specific indication of a day, i.e. “day 1”, “day 5”, etc. relates to the findings of the bacterium by conventional method of taking lavage for the lung, growing on an appropriate substrate, and detecting by time of flight. Hence FIG. 11A provides the concentrations of 40 target compounds that have previously been elucidated as belonging to these bacteria. FIG. 11B shows the detection of 1-ethyl,4-methyl-benzene indicative of the presence of Staphylococcus. FIG. 11C shows the detection of 1,3-dimethyl-benzene indicative of the presence of Pseudomonas Aeruginosa and FIG. 11D shows the detection of benzaldehyde indicative of the presence of Klebsiella.

[0179] In FIGS. 12A-12D the method for determining the amount of each of the compounds according to the invention is given. FIG. 12A shows part of the chromatogram including numerous compounds among them 1-ethyl, 4-methyl-benzene (FIG. 12E) identified according to its retention time. It is however buried under many other compounds. FIG. 12B demonstrates the deconvolution that is done in order to detect more specifically the peak of this specific compound. FIG. 12C is yet a further enlarged demonstration of the result of the deconvolution. FIG. 12D shows the isolated peak of this specific compound that enables to calculate its area (area under the curve) thus determining its concentration in the exhaled air.

[0180] Turning to FIGS. 13A-13F, concentration of three typical bacterium associated with pneumonia are given. Patients diagnosed with pneumonia have a concentration of compounds that are indicative of the specific bacterium in the scale of 0.2-20×10.sup.6 whereas health individuals have a concentration of compounds that are indicative of the specific bacterium in the scale of 0.2-200×10.sup.3. Thus, FIGS. 13A, 13C and 13E demonstrate patients diagnosed with pneumonia while FIGS. 13B, 13D and 13F demonstrate healthy individuals.

EXPERIMENTAL

[0181] The sampling or collecting units that were uses contained sorbents selected from TENAX™, i.e. Poly(2,6-diphenyl-p-phenylene oxide) (PPPO), Carboxen™, i.e. Sulfonated polymers; Carbon molecular sieves that are prepared by the controlled pyrolysis of poly(vinylidene chloride) or sulfonated polymers. Carbon adsorbents used according to the invention include Carbotrap F, Carbotrap C, Carbotrap Y, Carbotrap B, Carbotrap X, Carbopack F, Carbopack C, Carbopack Y, Carbopack B, Carbopack X, Carboxen 1016, Carboxen 569, Carboxen 1021, Carboxen 1018, Carbosieve S-III, Carboxen 1003, Carbosieve G, Carboxen 1000 and Carboxen 1012.

[0182] Carbon adsorbents may also be selected amongst graphitized carbon blacks having a 20/40 mesh, graphitized carbon blacks having a 60/80 mesh and carbon molecular sieves. In some embodiments, the adsorbents having a surface area between 5 and 1500 m2/g, a density of between 0.2 and 0.7 and/or a micropore diameter between 4 and 300 A.

[0183] Thermal desorption units for desorbing volatiles adsorbed to the sampling units were selected from Markes TD100-xr (Autosampler), TD-100 cold traps, including Stainless steel thermal desorption sorbent tubes. Perkin Elmer Turbo Matrix 650 ATD Thermal Desorption System, including Stainless steel thermal desorption sorbent tubes. Shimadzu TD-20 or TD-30 Thermal Desorption System, including Stainless steel thermal desorption sorbent tubes. Gerstel TDS 3C/TDS-A2 Thermal Desorption System, including Stainless steel thermal desorption sorbent tubes. Scientific Instrument Services (SIS) TD-5 Thermal Desorption System, including Stainless steel thermal desorption sorbent tubes. CDS 9300 Thermal Desorption System with CDS 7550 Autosampler, including Stainless steel thermal desorption sorbent tubes. CDS 7550S Stand-gone 72 position Thermal Desorption system, including Stainless steel thermal desorption sorbent tubes.

[0184] The analytical system, GC, MS and GCMS were selected form GCMS with TOF, Marke BenchTOF-HD, Time-of-flight mass spectrometer for GC Agilent 7890 and GC×GC modulator, including the option of collecting different collision energies at the same time. Quadrupole GCMS, Agilent GC 7890B with Agilent MSD 5977B, Agilent GC 6890 with Agilent MSD 5975, Quadrupole GCMS, Agilent GC 7890 with Agilent MSD 5975, Quadrupole GCMS, Agilent GC 6890 with Agilent MSD 5973, GCMS, Agilent 7250 GC/Q-TOF, GCMS, Agilent 7010B Triple Quadrupole GC/MS, GCMS, Thermo Scientific Q Exactive™ GC Orbitrap™ GC-MS/MS.

[0185] GC Column Samples: the GC separate the analytes using 2 capillaries columns. The first (main non-Polar column) column was selected from the following: SGEPN 99054140 (SN:073438A23), 20M×0.18 mmID-BPX5×0.18 μm df, with He flow of 0.5 ml/min (Constant flow/pressure), and the Zed column is polar column. GC capillary column: Agilent DB5-ms 30M×0.25 mmID×0.50 μm df, with He flow of 1.5 ml/min (Constant flow/pressure). GC capillary column: Zebron ZB-5, 30M×0.25 mmID×0.25 μm df, with He flow of 1.2 ml/min (Constant flow/pressure). GC capillary column: Agilent DB5-ms 60M×0.25 mmID×1.0 μm df, with He flow of 1.5 ml/min (Constant flow/pressure). GC capillary column: Agilent DB5-ms 60M×0.53 mmID×df, with He flow of 5 ml/min (Constant flow/pressure). GC capillary column: Agilent DB1 60M×0.32 mmID×0.5 μm df, with He flow of 2 ml/min (Constant flow/pressure). GC capillary column: Agilent DB1 30M×0.18 mmID×0.25 μm df, with He flow of 0.6 ml/min (Constant flow/pressure). GC capillary column: Agilent DB1 30M×0.15 mmID×0.15 μm df, with He flow of 0.3 ml/min (Constant flow/pressure).

Calculation of Area Peak for Determining Growth of Bacterial/Viral/Fungal Mass

[0186] Compounds desorbed from the sampling units are analyzed using a GCMS instrument, after chromatographic separation in a capillary GC column. In the resulting MS chromatogram, all separated substances appear as chromatographic peaks, arranged by their retention times. Each peak consists of a continuous line connecting several points, wherein each point is the sum of abundances of fragment ion generated from the fragmentation of the material molecules. The peak area is calculated by performing an integral derivative of the abundance of ions according to the time (d.sub.abundance/dt) from the starting point of the peak to its end, as derived from Eq. 1:

[00001]$\begin{array}{cc}{\int }_{PS-T}^{PE-T}\frac{\mathrm{da}}{\mathrm{dt}}=\mathrm{peak}\mathrm{area}.& \left(\mathrm{Eq}.1\right)\end{array}$

[0187] wherein in Eq. 1, PS-T is the peak start time, PE-T is the peak end time, da is the derivative of the ion's abundance, and dt is the derivative of the retention time.

[0188] It should be noted that peak area values vary between GCMS instruments, as well as by integration software used. However, since a disease onset or a bacterial/viral/fungal load is determined by a determining a change between two consecutive measurements, for each subject, by using the same GCMS instrument, the determination is indicative and conclusive.

[0189] In order to calibrate the peak area of each marker, a known substance is used as an internal standard (IS) and is inserted into the sampling unit. The IS is used in a known concentration, volume and pressure (e.g. volume of 1 ml standard gas with 3 ppm of the IS compound, at an inlet pressure of 25 psi). The peak areas are normalized according to the IS area utilizing a known and accepted IS calculation method. Three non-limiting examples of IS used in accordance with the invention are shown in Table 1.

TABLE-US-00001 TABLE 1 3 Internal Standard molecules used for VAP detection. Relative Average Conc.-ppm Average Standard IS # Compound Name CAS RN Formula RT (v/v) Area Deviation (%) S-1 Methane, bromochloro- 74-97-5 CH.sub.2BrCl 10.22 3.0 466,103 30 (IS1) S-3 Chlorobenzene-d5 3114-55-4 C.sub.6ClD.sub.5 17.64 3.0 2,120,000 30 (IS3) S-4 p-Bromofluorobenzene 460-00-4 C.sub.6H.sub.4BrF 20.39 3.0 2,779,000 30 (IS4)

[0190] A marker area size of 1,000 (10.sup.3) calculated by Eq. (1) above is approximately equivalent to a concentration of 0.006 ppm (v/v) of IS-1. An area of 1,000,000 (10.sup.6) is approximately equivalent to a concentration of 6.4 ppm (v/v) of IS-1.

[0191] Assessment of the Development of a Disease Based on a Calculated Area

[0192] Each patient arriving/brought for prolonged hospitalization caries their own medical background, thus by analyzing the patient's exhaled air upon arrival according to the present invention, a baseline that characterizes the specific patient is created (Patient Baseline). The patient's baseline or marker background level is calculated by the sum of all areas measured for the marker peaks, that appear in his exhaled breath.

[0193] An assessment of the development of an infectious disease is carried out while monitoring the total areas of the marker peaks, throughout the days of hospitalization. These give an index regarding the changing/developing in the bacterial load (BL), viral load (VL) or fungal load (FL) in the patient's respiratory system, i.e. in the lungs. An increase in the peak area of about 50% or more is considered a significant change that reflects an increase in bacterial load. In the case of an increase of about 50% in the marker peaks area, the algorithmic way in which the development of an infectious disease is described is follows:

[0194] If the total biomarkers compounds (TBCM) area of the 2.sup.nd day is greater (over 50%) compared to the patient's baseline (1.sup.st day), and the TBCM area of the 3.sup.rd day is greater (over 50%) than the TBCM area of the 2.sup.nd day, this servers an indication of a significant increase in bacterial/viral/fungal load (BL/VL/FL), which should be reported and continued to be monitored.

[0195] Assessing the Development of Infectious Disease Such as VAP

[0196] The algorithm that shows signs of growth in the bacterial or viral load makes it possible to give an assessment regarding the bacterial family or the type of virus or fungi. The algorithm contains various metrics that consider the peak area size of the biomarkers, the biomarkers numbers, the type of biomarkers and the ratio between them. Hence the algorithm includes, inter alia, data concerning bacterial/viral/fungal load, markers, total marker compounds, general markers, bacterial/viral/fungal number and others.

[0197] The Following examples are based on 36 patients that were enrolled in the trials. 28 patients were included in actual trials (that required sampling for at least 3 days). Of the 28 patients, 6 patients were recognized as potential VAP cases (z20%) by using the analysis of exhaled breath samples of ventilated patients according to the invention.

[0198] Target Molecules (TMs) indicating specific bacterial mass growth that are attributed to bacteria associated with VAP were detected in all 6 patients. Important to note is that as exemplified below the TMs were detected in exhaled air of the patients one, two and three day prior to finding of the standard-of-care clinical signs of VAP. These standard-of-care clinical signs include a new and persistent (>48-h) or progressive radiographic infiltrate plus two of the following: temperature of >38° C. or <36° C., blood leukocyte count of >10,000 cells/ml or <5,000 cells/ml, purulent tracheal secretions, gas exchange degradation and significant bacterial growth of a tracheal secretion sample. All 6 patients were thus isolated as potential VAP cases, where their VAP was later confirmed the treating physicians/medical staff of the ICU through monitoring of patient's clinical signs and symptoms over the course of his ICU stay.

Example 1

[0199] A 56-year old male, generally healthy, presented with acute flu (viral upper respiratory infection) was admitted to ICU in Sheba hospital (Ramat Gan, IL) for mechanical ventilation support. The patient remained under ventilation for 9 days and was released from hospital on day 12. The clinical parameters are given in Table 2:

TABLE-US-00002 TABLE 2 Patient Day 2 Day 3 Day 4 Day 5 Day 6 VAP Day 8 Day 9 Temperature 36.8 37 36.2 36.3 36.6 36.3 36.3 37.3 WBC .sub.(white blood cell) 2.6 3.22 4.96 4.05 3.58 3.06 3.68 4.51 CRP .sub.(C reactive protein 131.86 71.38 43.76 120.6 259.12 203.49 134.33 88.54 Antipyretic n n n N n n N n Culture negative nd nd nd Pseudomon nd Nd nd Antibiotics Tazocin same same same same Ciprofloaxcin Same same X-Ray 2 1 1 1 1 4 4 3 Biomarkers 3 2 2 5 4 4 3 3 n = negative; y = yes; nd = not done; X-ray and biomarkers = numbers 1-5 indicate level where 1 = normal and 5 = very high

[0200] Analysis of the patient's exhaled air detected markers. In particular, 4 specific pseudomonas-specific markers were detected already on day 1 prior to the detection of clinical signs. Hence the analysis of the exhaled air detected biomarkers two days prior to the X-Ray and 2-3 days prior to the detection by culture. Therefore, VAP was detected much earlier than clinical signs that are routinely used.

[0201] All biomarkers spiked on day 5. The spiking of the marker designated No. 4 was significantly more than the others. This is associated with exponential growth. Thus, VAP occurred in the patient while under antibiotic therapy. As a consequence of its detection, the antibiotic treatment was revised following clinical signs of VAP.

Example 2

[0202] A 24-year old male, admitted to neurosurgical ICU in Rambam hospital (Haifa, IL) following severe fall from height, suffering from scull base fracture, epidural hemorrhage, lung contusion and traumatic pneumothorax. The man was placed on mechanical ventilation support for 13 days. The patient died on day 15. The clinical parameters are given in Table 3:

TABLE-US-00003 TABLE 3 Day 1 2 3 4 5 6 7 VAP Temperature 36.4 36.5 37.4 37.8 37.9 338.8 337.4 WBC (white 18.9 11.88 10.56 11.26 15.49 118.3 117.02 blood cell) CRP (C 42.41 reactive protein Culture Gram (+) Gram (+) bacillus, bacillus, B. cereus S. aureus Antibiotics Cefamezin Csame na na na Tazocin Tazocin X-Ray 33 3 33 33 Staph 2 22 00 66 22 00 (MRSA) Acinetobacter 0 00 13 00 22 33 00 Klebsiella 0 00 11 00 88 00 00 Day 8 9 10 11 12 13 Temperature 36.3 37.7 37.1 38.4 36.4 36.2 WBC (white 16.88 12.23 14.64 12.91 12.58 99.31 blood cell) CRP (C 87.36 31.97 reactive protein Culture Antibiotics Tazocin Tazocin, Tazocin, Tazocin, Tazocin, Tazocin, Vanco. Vanco. Vanco. Vanco. Vanco. Vanco. X-Ray 85 33 Staph 00 00 22 22 00 (MRSA) Acinetobacter 22 00 00 22 00 Klebsiella 00 00 22 22 00 00 n = negative; y = yes; nd = not done; X-ray and biomarkers = numbers 1-5 indicate level where 1 = normal and 5 = very high

[0203] Analysis of the exhaled air detected biomarkers. In particular, 3 different bacteria were detected with different concentrations and different daily appearances. Culture detected only 1 bacterial source (MRSA). The total bacterial mass that was detected spiked on day 5, being 2 days prior to the clinical diagnosis that pointed to VAP and 2 to 3 days before the culture (that itself requires 1 or 2 days for analysis).

Example 3

[0204] A 34-year old male, admitted to neurosurgical ICU at Rambam hospital (Haifa, IL) for head trauma following severe fall, suffered from traumatic scull base fracture, subdural and epidural hemorrhage, multiple ribs fractures, lung contusion and pneumothorax. He was placed on mechanical ventilation support for 7 days. Released from hospital on day 14. The clinical parameters are given in Table 4:

TABLE-US-00004 TABLE 4 Day 1 2 3 4 VAP 6 7 8 WBC 7.4 7.68 7.4 12.38 12.67 11.54 11.22 10.59 CRP Na Na Na Na Na na Na na Anti- Na Na Na Na Na na Na na pyretic Culture Lower respire. tract cult. results Antibiotics Rcephin Rcephin Rcephin Rcephin Rcephin, Cefamezin, Cefamezin, cefamezin Cefamezin Cefamezin Cefamezin Cefamezin Cefamezin, Tazocin Tazocin Tazocin X-Ray 0 0 0 2 3 0 4 5 S. Aureus 2 5 0 0 1 2 3 3 6 × 10.sup.6 Acinetobacter 0 4 0 0 4 2 2 3 12 × 10.sup.6 Pseudomonas 1 4 1 1 3 0 0 0 5 × 10.sup.3 n = negative; y = yes; nd = not done; X-ray and biomarkers = numbers 1-5 indicate level where 1 = normal and 5 = very high

[0205] Analysis of the exhaled air detected biomarkers. In particular, 3 different biomarkers, i.e. three different bacteria with different concentrations and different daily appearances were detected. Contrary to these findings, culture detected only 1 bacterial source. The total bacterial mass that was detected spiked on the 5.sup.th day. These findings were revealed 2 days before X-Ray diagnosis and 1 or 2 days before culture (1, 2 days required for analysis of culture).

[0206] It should further be noted that bacterial mass detected two spikes (2.sup.nd and 5.sup.th days) where the data of the 2.sup.nd day is unique.

Example 4

[0207] Markers identified for Staphy Aureus were bromochloro methane, 1,4-difluoro benzene, chlorobenzene, p-bromofluorobenzene, 3-methylbutanal, 2-methylbutanal and dimethyl trisulfide.