[go: up one dir, main page]

WO2017055949A1 - Assistance de décision clinique pour diagnostic différentiel d'œdème pulmonaire chez des patients dans un état critique - Google Patents

Assistance de décision clinique pour diagnostic différentiel d'œdème pulmonaire chez des patients dans un état critique Download PDF

Info

Publication number
WO2017055949A1
WO2017055949A1 PCT/IB2016/055322 IB2016055322W WO2017055949A1 WO 2017055949 A1 WO2017055949 A1 WO 2017055949A1 IB 2016055322 W IB2016055322 W IB 2016055322W WO 2017055949 A1 WO2017055949 A1 WO 2017055949A1
Authority
WO
WIPO (PCT)
Prior art keywords
ards
pulmonary edema
predictors
patient
points
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/IB2016/055322
Other languages
English (en)
Inventor
Caitlyn Marie CHIOFOLO
Srinivasan VAIRAVAN
Nicolas Wadih Chbat
Rodrigo Cartin-Ceba
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Koninklijke Philips NV
Original Assignee
Koninklijke Philips NV
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Koninklijke Philips NV filed Critical Koninklijke Philips NV
Publication of WO2017055949A1 publication Critical patent/WO2017055949A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • CPE cardiogenic pulmonary edema
  • NCPE non-cardiogenic pulmonary edema
  • NCPE has a slow or difficult recovery due to impairment of the alveolar-capillary epithelial barrier properties and failure of repair due to initial insult.
  • PCWP pulmonary capillary wedge pressure
  • PAOP has been recognized as a potential marker.
  • use of pulmonary catheters to measure PCWP is controversial as the technique is invasive, offers unclear or limited benefits, and carries the burden of increased risk of infection, thrombosis and patient discomfort.
  • Laboratory data including markers such as brain natriuretic peptide (BNP) , NT-proBNP, and the troponins are often elevated in patients with acute CPE. Thus, an absence of elevation of these markers can be used to rule out CPE.
  • Radiologic data such as the vascular pedicle width
  • VPW has been shown to accurately differentiate CPE from NCPE, with a higher VPW (e.g., greater than 70mm) corresponding to CPE. However, the VPW is not routinely measured. Routine
  • Echocardiographic markers such as ejection fraction (EF%) or the ratio of mitral peak velocity of early filling to early diastolic mitral annular velocity (e/E' ratio) can be used to differentiate CPE from NCPE.
  • markers may have limited benefit late in the disease course, as patients with NCPE can develop concomitant CPE.
  • Patient history from charting data can also provide key
  • NCPE may also have great prognostic value and may help care providers better assess the chance of edema progression or recovery.
  • Identifying acute respiratory distress syndrome (ARDS) one type of NCPE, can aid in the patient prognosis and provide necessary context for care providers to better manage the patient's condition.
  • ARDS acute respiratory distress syndrome
  • NCPE however, they have limited applicability. Obtaining the markers may be invasive, not routinely available, not sensitive or not integrated with other markers. Further, risk of CPE or NCPE is not automatically calculated and none of the clinical markers allows for the further differentiation among causes of NCPE or identification of ARDS .
  • a method for diagnosing pulmonary edema including comparing patient information to a list of predictors in a predictor database to identify patient predictors,
  • a system for diagnosing pulmonary edema including a predictor database including a list of predictors and a processor comparing patient information to the list of predictors to identify patient predictors, assigning points based on the identified patient predictors and adding the points to determine an aggregated number of points to determine a probability of one of acute respiratory distress syndrome or cardiogenic pulmonary edema based on the aggregated number of points .
  • FIG. 1 shows a schematic drawing of a system according to an exemplary embodiment.
  • Fig. 2 shows a table of predictors for NCPE/CPE and assigned points for each according to an exemplary embodiment.
  • Fig. 3 shows a table assigning probability of NCPE/CPE based on aggregated points according to an exemplary embodiment.
  • FIG. 4 shows a flow diagram of a method for pulmonary edema detection according to an exemplary embodiment.
  • FIG. 5 shows a flow diagram of a method for pulmonary edema differentiation according to another exemplary embodiment.
  • Fig. 6 shows a flow diagram of a method for
  • Fig. 7 shows a flow diagram of a method for
  • FIG. 8 shows a flow diagram of a method for
  • FIG. 9 shows a flow diagram of a method for
  • Fig. 10 shows a flow diagram of a method for
  • FIG. 11 shows a flow diagram of a method for integrating ARDS detection along with pulmonary edema detection and differentiation according to another exemplary embodiment.
  • Fig. 10 shows a flow diagram of a method for
  • the exemplary embodiments may be further understood with reference to the following description and the appended drawings, wherein like elements are referred to with the same reference numerals.
  • the exemplary embodiments relate to a system and method for the differential diagnosis of CPE and NCPE and assessing the risk of acute respiratory distress syndrome
  • ARDS one particular type of NCPE.
  • a system 100 differentiates between CPE and NCPE and may further identify a patient's risk of ARDS.
  • the system 100 comprises a processor 102, a user interface 104, a display 106 and a memory 108.
  • the processor 102 may include a pulmonary edema detection module 110 for detecting a presence of pulmonary edema in a patient and a pulmonary edema differentiation module 112 for differentiating between CPE and NCPE and particularly between CPE and ARDS.
  • the processor 102 may further include an ARDS detection module 114.
  • a user may input user selection and/or preferences for the system 100 via the user interface 104, which may include input devices such as, for example, a keyboard, mouse and/or touch display on the display 106.
  • the pulmonary edema detection module 110 automatically detects patients at risk of pulmonary edema by identifying the presence of bilateral infiltrates and calculating the ratio of partial pressure arterial oxygen (PaC> 2 ) to fraction of inspired oxygen (F1O2) and/or the ratio of peripheral oxygen saturation (Sp0 2 ) to fraction of inspired oxygen (F1O 2 ) . Pulmonary edema is detected when bilateral infiltrates are present and low P/F or S/F is present. The presence of pulmonary edema may be
  • NCPE ARDS
  • CPE/CPE predictors which may be stored in the memory 108 via a
  • predictor database 116 Some of the predictors shown in Fig. 2, have been previously identified in Schmickl et al . (Chest. Jan 2012; 141 (1) : 43-50) . New predictors such as NT-pro BNP, EF%, E/E%, E/E', massive transfusion and high risk surgery have been added. As shown in Fig. 2, each predictor is assigned a weight with magnitude and sign. ARDS predictors have positive weights while CPE predictors have negative weights. The magnitude of the weights indicates the strength of the predictor. Patients are assigned points for each known predictor and the aggregated points may be used to assign a probability of ARDS or CPE according to, for example, the table shown in Fig. 3. For example, the probability of CPE is equal to 1 minus the
  • the determined probability may be displayed on the display 106.
  • the ARDS detection module 114 calculates an ARDS risk score, as described in PCT Published Application WO2013121374 entitled "Acute Lung Injury (ALI) /Acute Respiratory Distress Syndrome (ARDS) Assessment and Monitoring” and filed on February 14, 2013, the entire disclosure of which is incorporated herein by reference.
  • the ARDS detection module 114 may calculate the ARDS risk score using patient vitals such as, for example, heart rate, arterial systolic and diastolic blood pressure,
  • the ARDS risk score may be
  • the calculated ARDS risk score may be displayed on the display 106.
  • Fig. 4 shows an exemplary method 200 for automatically detecting pulmonary edema in a patient using the system 100.
  • the pulmonary edema detection module 110 may detect the presence of bilateral infiltrates in a patient.
  • bilateral infiltrates may be extracted from a chest x-ray image via imaging informatics, from a chest x-ray report via a Natural Language Processing (NLP) program, or from manual entry, either directly through the specific application or indirectly through charting in an electronic medical record.
  • NLP Natural Language Processing
  • a step 220 the ratio of partial pressure arterial oxygen (PaC ⁇ ) to fraction of inspired oxygen (FiC ⁇ ) and/or the ratio of peripheral oxygen saturation
  • ( SpC>2 ) / ( F1O2 ) are considered to be low - i.e., below a threshold value. For example, when (Pa0 2 )/(Fi0 2 ) is less than 300 or
  • the patient determines whether the patient has pulmonary edema. If the bilateral infiltrates are detected and either ( PaC> 2 ) / ( F1O 2 ) or ( SpC> 2 ) / ( F1O 2 ) are considered to be low, the patient is identified as having pulmonary edema. If either of these conditions are not met, it is determined that the patient does not have
  • pulmonary edema The result may be displayed on the display 106.
  • the presence or absence of edema may be indicated via a binary value 1 or 0, respectively.
  • Fig. 5 shows a method 300 differentiating between ARDS
  • pulmonary edema differentiation module 112 compares available patient history and/or information stored in, for example, a patient's electronic medical record, to the predictors stored in the predictor database 116.
  • points are assigned for each of the known predictors. As described above in regard to the predictor database 116, each predictor is assigned a weight with magnitude and sign, ARDS predictors having positive weights and CPE predictors having negative weights.
  • the points for all of the known patient predictors are added to calculate the aggregated number of points.
  • the aggregated number of points are used to determine the
  • the probability of either ARDS and/or CPE may be displayed on the display 106, in a step 350. Additional information such as, for example, identified patient predictors and/or aggregated number of points may also be displayed on the display 106.
  • a method 400 integrates pulmonary edema detection
  • the pulmonary edema detection module 110 detects the presence of pulmonary edema by detecting bilateral infiltrates and calculating the Pa0 2 )/(Fi0 2 ) and/or ( SpC> 2 ) / ( F1O 2 ) , as described above in regard to the method 200.
  • a step 420 the processor determines whether the pulmonary edema is present. If present, the method 400 proceeds to a step 430. If pulmonary edema is determined not to be present, no further action is taken.
  • the method 400 proceeds with the steps of the method 300.
  • the pulmonary edema proceeds with the steps of the method 300.
  • the pulmonary edema proceeds with the steps of the method 300.
  • the pulmonary edema proceeds with the steps of the method 300.
  • the pulmonary edema proceeds with the steps of the method 300.
  • the pulmonary edema proceeds with the steps of the method 300.
  • the pulmonary edema proceeds with the steps of the method 300.
  • the differentiation module 112 compares patient history/information with the predictors stored in the predictor database 116.
  • the patient's known predictors are assigned points and the points summed, in a step 450, to determine an aggregated number of points.
  • the aggregated number of points is used to determine the probability of either ARDS or CPE according to, for example, the table shown in Fig. 3.
  • the determined probability may be output or displayed on the display 106, in a step 470. Additional information such as, for example, the presence of pulmonary edema may also be displayed on the display 106.
  • the ARDS risk score calculated via the ARDS detection module 114, as described above with respect to the system 100 may be
  • a method 500a integrates the calculated ARDS risk score and the differentiation between ARDS and CPE using a suppression method.
  • the ARDS risk score is calculated using the ARDS detection module 114.
  • Steps 520a - 550a correspond to the steps 310 - 340, described above with respect to method 300, to determine the probability of CPE using the calculated aggregated number of points.
  • a step 560a the processor 102 determines whether the CPE probability is greater than a threshold value (e.g., 50%) . If the CPE is greater than the threshold value the method 500 proceeds to step 570a, in which any ARDS output, alarms and/or advisories indicating the patient's risk for ARDS are suppressed. If the CPE probability is not greater than the threshold value, no further action is taken. In a further embodiment, the CPE probability may be displayed on the display 106.
  • a threshold value e.g. 50%
  • a method 500b may integrate the ARDS detection module 114 with the method 300 of the pulmonary edema differentiation module 112 using a revision method.
  • the method 500b may be substantially similar to the method 500a described above, with steps 510b - 550b substantially corresponding to the steps 510a - 550a described above.
  • an ARDS risk score calculated in step 510b is multiplied by an ARDS probability determined in the step 550b to determine a revised ARDS risk score.
  • This revised ARDS risk score may be output (e.g., displayed) in a step a 570b.
  • an ARDS risk score is
  • a step 520c the processor 102 determines whether the calculated ARDS risk score exceeds a threshold value (e.g., 0.8) . If the ARDS risk score exceeds the threshold value and is thus representative of a high probability of ARDS, the method 500c proceeds to steps 530c - 580c, which substantially correspond to the steps 520b - 570b, described above with respect to the method 500b. If the ARDS risk does not exceed a threshold value in step 520c, no further action may be taken. Alternatively, where the ARDS risk score does not exceed the threshold value in the step 520c, the method 500c may display the ARDS risk score determined in the step 510c.
  • a threshold value e.g. 0.8
  • ARDS detection module 114, the pulmonary edema detection module 110 and the pulmonary edema differentiation module 112 may be integrated with one another. This integration, however, may be achieved in a number of different ways.
  • a method 600a may be substantially similar to the method 500a, described above, with the addition of the pulmonary edema detection module 110.
  • an ARDS risk score is determined via the ARDS detection module 114.
  • the presence of pulmonary edema is detected via the pulmonary edema detection module 110 using the method 200, as described above with respect to Fig. 4.
  • the method 600a may proceed to steps 640a - 690a, which substantially correspond to the steps 520a - 570a, described above with respect to the method 500a in Fig. 7.
  • steps 640a - 690a predictors are used to determine the probability of CPE. If it is determined that the probability of CPE is greater than a threshold value, outputs indicating a risk of ARDS are suppressed. If, in the step 630a, it is determined that pulmonary edema is not present, no further action is required.
  • a step 610b an ARDS risk score is determined using the ARDS risk module 114.
  • a step 620b the presence of pulmonary edema is detected via the pulmonary edema detection module 110 using the method 200, as described above with respect to Fig. 4. If it is determined, in a step 630b, that pulmonary edema is present, the method 600b may proceed to steps 640b - 690b, which substantially correspond to the steps 520a - 570b, described above with respect to the method 500a in Fig. 8. In the steps 640b - 690b, predictors are used to determine the probability of ARDS. This ARDS
  • ARDS risk score determined in step 610b to determine a revised ARDS risk score which may be output in the step 690b. If it is determined, in the step 630b, that pulmonary edema is not present, no further action is required .
  • a method 600c integrates the ARDS risk module 114 along with the pulmonary edema detection and differentiation modules 110, 112, the
  • pulmonary edema differentiation module 112 is integrated with the method 500c.
  • an ARDS risk score is
  • a step 615c it is determined whether the calculated ARDS risk score is greater than a threshold value. If the ARDS risk score does not exceed the threshold value, no further action is required.
  • the ARDS risk score calculated in the step 601c may be displayed. If the ARDS risk score is greater than the threshold value, the method 600c proceeds to a step 620c in which the presence of pulmonary edema is detected via the
  • pulmonary edema detection module 110 uses, for example, the method 200, as described above in regard to Fig. 4.
  • a step 630c it is determined whether pulmonary edema is present. If it is determined that the patient does not have pulmonary edema, no further action is required. Alternativelt , the ARDS risk score calculated in the step 601c may be displayed. If, however, pulmonary edema is present, the method 600c proceeds to steps 640c - 690c, which substantially correspond to steps 530c - 580c of the method 500c. In the steps 640c - 690c, predictors are used to determine the probability of ARDS. This ARDS
  • ARDS risk score determined in step 610b to determine a revised ARDS risk score, which may be output in the step 690c.
  • pulmonary edema detection module 110 the pulmonary edema differentiation module 112 and the ARDS risk detection module 114 may be programs containing lines of code that, when compile may be executed on a processor.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Measuring And Recording Apparatus For Diagnosis (AREA)

Abstract

L'invention concerne un système et un procédé de diagnostic d'œdème pulmonaire. Le système et le procédé effectuent les étapes suivantes : comparer des informations de patient avec une liste de prédicteurs dans une base de données de prédicteurs pour identifier des prédicteurs de patient, attribuer des points en fonction des prédicteurs de patient identifiés, ajouter les points pour déterminer un nombre de points agrégé, et déterminer une probabilité d'un des cas suivants : syndrome de détresse respiratoire aiguë ou œdème pulmonaire cardiogénique en fonction du nombre de points agrégé.
PCT/IB2016/055322 2015-09-28 2016-09-07 Assistance de décision clinique pour diagnostic différentiel d'œdème pulmonaire chez des patients dans un état critique Ceased WO2017055949A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201562233573P 2015-09-28 2015-09-28
US62/233,573 2015-09-28

Publications (1)

Publication Number Publication Date
WO2017055949A1 true WO2017055949A1 (fr) 2017-04-06

Family

ID=56979611

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IB2016/055322 Ceased WO2017055949A1 (fr) 2015-09-28 2016-09-07 Assistance de décision clinique pour diagnostic différentiel d'œdème pulmonaire chez des patients dans un état critique

Country Status (1)

Country Link
WO (1) WO2017055949A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN121075663A (zh) * 2025-11-06 2025-12-05 四川互慧软件有限公司 一种基于多模态数据的高原肺水肿风险预测方法及系统
CN121075663B (en) * 2025-11-06 2026-02-03 四川互慧软件有限公司 Plateau pulmonary edema risk prediction method and system based on multi-modal data

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012085750A1 (fr) 2010-12-21 2012-06-28 Koninklijke Philips Electronics N.V. Détection d'état de santé de patient et mortalité
WO2013121374A2 (fr) 2012-02-17 2013-08-22 Koninklijke Philips N.V. Évaluation et surveillance du syndrome respiratoire aigu (ali)/syndrome de détresse respiratoire aiguë (sdra)
EP2866033A1 (fr) * 2013-10-23 2015-04-29 Roche Diagniostics GmbH Diagnostic différentiel de la dyspnée aiguë sur la base de proSP-B de terminal C, KL-6 et peptides de type BNP

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012085750A1 (fr) 2010-12-21 2012-06-28 Koninklijke Philips Electronics N.V. Détection d'état de santé de patient et mortalité
WO2013121374A2 (fr) 2012-02-17 2013-08-22 Koninklijke Philips N.V. Évaluation et surveillance du syndrome respiratoire aigu (ali)/syndrome de détresse respiratoire aiguë (sdra)
EP2866033A1 (fr) * 2013-10-23 2015-04-29 Roche Diagniostics GmbH Diagnostic différentiel de la dyspnée aiguë sur la base de proSP-B de terminal C, KL-6 et peptides de type BNP

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
CHRISTOPHER N SCHMICKL ET AL: "Decision support tool for differential diagnosis of Acute Respiratory Distress Syndrome (ARDS) vs Cardiogenic Pulmonary Edema (CPE): a prospective validation and meta-analysis", CRITICAL CARE, BIOMED CENTRAL LTD., LONDON, GB, vol. 18, no. 6, 29 November 2014 (2014-11-29), pages 659, XP021207705, ISSN: 1364-8535, DOI: 10.1186/S13054-014-0659-X *
CHRISTOPHER N. SCHMICKL ET AL: "Comparison of Hospital Mortality and Long-term Survival in Patients With Acute Lung Injury/ARDS vs Cardiogenic Pulmonary Edema", CHEST, vol. 147, no. 3, 1 March 2015 (2015-03-01), US, pages 618 - 625, XP055319808, ISSN: 0012-3692, DOI: 10.1378/chest.14-1371 *
CHRISTOPHER N. SCHMICKL ET AL: "Decision Support Tool for Early Differential Diagnosis of Acute Lung Injury and Cardiogenic Pulmonary Edema in Medical Critically Ill Patients", CHEST, vol. 141, no. 1, 1 January 2012 (2012-01-01), US, pages 43 - 50, XP055320006, ISSN: 0012-3692, DOI: :10.1378/chest.11-1496 *
SCHMICKL ET AL., CHEST, vol. 141, no. 1, January 2012 (2012-01-01), pages 43 - 50

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN121075663A (zh) * 2025-11-06 2025-12-05 四川互慧软件有限公司 一种基于多模态数据的高原肺水肿风险预测方法及系统
CN121075663B (en) * 2025-11-06 2026-02-03 四川互慧软件有限公司 Plateau pulmonary edema risk prediction method and system based on multi-modal data

Similar Documents

Publication Publication Date Title
US12502219B2 (en) Systems and methods for treatment planning based on plaque progression and regression curves
EP4162870B1 (fr) Système et procédé d'estimation du diamètre d'une lumière saine et quantification de sténose dans des artères coronaires
RU2728855C2 (ru) Количественный показатель предупреждения об ухудшении работы сердечно-сосудистой системы
Matsumura et al. Accuracy of fractional flow reserve measurements in clinical practice: observations from a core laboratory analysis
JP7217289B2 (ja) 医療介入制御システム
US20210166378A1 (en) Medical image analysis system, storage medium, and medical image analysis method
US20170329918A1 (en) Internet of things based monitoring and assessment platform
EP3591663A1 (fr) Diagnostic et contrôle assistés par ordinateur des patients souffrant d'insuffisance cardiaque
KR20210053545A (ko) 질환 예측을 위한 사용자 인터페이스 제공 방법 및 이를 이용한 질환 예측을 위한 사용자 인터페이스 제공용 장치
US20230142909A1 (en) Clinically meaningful and personalized disease progression monitoring incorporating established disease staging definitions
US20170277853A1 (en) Data-driven performance based system for adapting advanced event detection algorithms to existing frameworks
JP7563459B2 (ja) 分析装置
Heimark et al. Accuracy of non-invasive cuffless blood pressure in the intensive care unit: Promises and challenges
WO2017055949A1 (fr) Assistance de décision clinique pour diagnostic différentiel d'œdème pulmonaire chez des patients dans un état critique
US20220277839A1 (en) Model to dynamically predict patient's discharge readiness in general ward
KR102788783B1 (ko) 스마트 혈류 역학 지표를 이용한 진단 보조 장치 및 방법
EP3705038B1 (fr) Système de reconnaissance de modèle pour classifier l'état fonctionnel de patients souffrant de maladies cardiaques, pulmonaires et vasculaires pulmonaires chroniques
Santos Vital-sign data-fusion methods to identify patient deterioration in the emergency department
EP4682898A1 (fr) Indice de dénervation rénale multifactorielle pour l'adéquation d'un patient et/ou la réactivité attendue pour le traitement de dénervation rénale
US20230268077A1 (en) Medical information processing device, medical information processing method, and storage medium
EP4201315A1 (fr) Procédé et système de dépistage de maladies cardiaques d'un patient
US20230320690A1 (en) Analysis device
WO2024201745A1 (fr) Dispositif de traitement d'information
Zhou et al. Impact of Sampling Frequency on the Accuracy and Diagnostic Efficacy of Flow Index in Monitoring Inspiratory Effort
WO2024211296A1 (fr) Procédé et système mis en œuvre par ordinateur pour déterminer un indicateur de type d'infarctus aigu du myocarde

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16770085

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 16770085

Country of ref document: EP

Kind code of ref document: A1