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Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 04/03/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-15, and 18-21, 25 are rejected under 35 U.S.C. 103 as being unpatentable over Rosenfeld et al. (US 2014/0046674 A1, hereinafter Rosenfeld) and in view of Jentzer et al. (hereinafter Jentzer, IDS ref) “Cardiogenic Shock Classification to Predict Mortality in the Cardiac Intensive Care Unit” Journal of The American college of Cardiology, Vol. 74, No. 17, 2019-2028).
Regarding Claim 1, Rosenfeld teaches,
A method for generating an order set (Rosenfeld, Table 1, ALGORITHMS
& PRACTICE STANDARDS DECISION SUPPORT, Cardiovascular, Figure 34A steps 2202-2218, treatment plan) for a patient based on a shock stage, (Rosenfeld, Table 1, cardiogenic shock) the method comprising:
(a) receiving patient health data with a computer system, wherein the patient health data are associated with a patient and are continuously received in a real-time manner (Rosenfeld, [0036], “The command center/remote location is manned by intensivists 24 hours a day, seven days per week. Each ICU comprises a nurse's station, to which data flows from individual beds in the ICU. Each patient in the ICU is monitored by a video camera, as well as by clinical monitors typical for the intensive care unit. These monitors provide constant real time patient information to the nurse's station, which in turn provides that information over a dedicated T-1 (high bandwidth) line to the ICU command center/remote location” NOTE: data collected 24/7 from the patient);
(b) accessing a staging algorithm with the computer system, the staging algorithm being configured to generate shock stage classification data from patient health data (Rosenfeld, [0058], “the patient care management system further comprises a relational database for storing a plurality of decision support algorithms and for prompting intensivists to provide care to patients based upon any of the decision support algorithms. The algorithms are selected from the group consisting of algorithms for treating; (…) Cardiogenic Shock”). [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1, Therefore, decision support for cardiogenic shock and generating alarm when a specific threshold is exceeded reads on the classification of shock stages (predefined threshold for shock stages).
(c) inputting the patient health data to the staging algorithm using the computer system as the patient health data are continuously received by the computer system in real-time (Rosenfeld, [0002] This invention relates generally to the care of
patients in Intensive Care Units (ICUs). More particularly this invention is a system and method for care of the critically ill that combines a real-time, multi-node telemedicine network and an integrated, patient care management system to enable specially-trained Intensivists to provide 24-hour/7-day-per-week patient monitoring and management to multiple, geographically dispersed ICUs from both on-site and remote locations
(d) generating, with the computer system, an alert (Rosenfeld, Figure 19, Rule engine 642, Alert screen 644; [0225] Referring to FIG. 19. Thus, monitor 636 provides information in HL 7 form to the interface engine 638. The physiological data is then formatted by the interface engine for storage in the database 640 where all patient information is maintained. The rules engine 642 searches for patterns of data indicative of clinical deterioration. [0226] One family of alarms looks for changes in vital signs over time, using pre-configured thresholds. These thresholds are patient-specific and setting/disease-specific”. when the shock stage classification data for the patient indicate a change in a shock stage (Rosenfeld, [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1, and [00232], As noted in Table I below, a wide variety of conditions is noted. Each of the conditions has an associated guideline of practice standard that can be presented to the intensivist who might be faced with that particular condition in a patient”. Therefore, standard decision support for cardiogenic shock and generating alarm when a specific threshold is exceeded reads on the classification of shock stages (predefined threshold for shock stages).
(e) generating, with the computer system and in response to the alert, an order set based on the shock stage classification data for the patient (Rosenfeld, [0230] “In order to standardize treatment across ICUs at the highest possible level, decision support algorithms are used in the present invention. These include textural material describing the topic, scientific treatments and possible complications. This information is available in real time to assist in all types of clinical decisions from diagnosis to treatment to triage”. Figure 34A- steps 2200-2218, figure 34b, [0345] Initially, the intensivist is prompted to determine whether the patient is hemodynamically stable (no angina, heart failure, or hypotension (systolic less than 80 mm)) 2200. If this criterion is not met, the intensivist is prompted to go to the cardio-pulmonary guidelines algorithm which is generally known to those skilled in the art”. NOTE: for specific cardiogenic shock, standard decision support known in the medical field is used. For example. the stage classifications are standard criteria for treatment applied.)
(f) storing the order set in an electronic medical record (EMR) for the patient using the computer system. (Rosenfeld, Figure 9,208, Database Server/Warehouse, Figure 20, note stored in EMR line log updated, Figure 9, [0202], The database server/warehouse function 208 comprises the amassed information of a wide variety of patients, in their various conditions, treatments, outcomes, and other information of a statistical nature that will assist clinicians and intensivists in treating patients in the ICU. The headquarters' function also serves to allow centralized creation of decision support algorithms and a wide variety of other treatment information that can be centrally managed and thereby standardized across a variety of command center/remote locations”).
Rosenfeld teaches standard support decision algorithm for cardiogenic shocks and consequently implement treatment plan, but Rosenfeld is silent on generating an output as shock stage classification data for the patient.
However, Jentzer teaches generating an output as shock stage classification data for the patient. (Jentzer, Table 2, Figure 1, Page 2119, “method, SCAI CS stages A through E were classified retrospectively using CICU admission data based on the presence of hypotension or tachycardia, hypoperfusion, deterioration, and refractory shock. Hospital mortality in each SCAI shock stage was stratified by cardiac arrest (CA)”. Page 2120 right col. “We used pragmatic and simplified definitions to divide patients into the 5 SCAI shock stages with increasing severity (A through E) using combinations of these variables (Central Illustration”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 2, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld is silent on wherein the shock stage classification data indicate an SCAI shock stage.
However, Jentzer teaches wherein the shock stage classification data indicate an SCAI shock stage. (Jentzer, Table 2, Figure 1, Page 2119,” method, SCAI CS stages A through E were classified retrospectively using CICU admission data based on the presence of hypotension or tachycardia, hypoperfusion, deterioration, and refractory shock. Hospital mortality in each SCAI shock stage was stratified by cardiac arrest (CA)”. Page 2120 right col. “We used pragmatic and simplified definitions to divide patients into the 5 SCAI shock stages with increasing severity (A through E) using combinations of these variables (Central Illustration”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 3, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld is silent on wherein the shock stage classification data indicate a numerical shock score value.
However, Jentzer teaches wherein the shock stage classification data indicate a numerical shock score value. (Jentzer, Table 4, TABLE 4 Severity of Illness Scores, Vital Signs, and Laboratory Data of Patients According to SCAI Shock Stage)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification score to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 4, combination of Rosenfeld and Jentzer teaches the method of claim 3,
Rosenfeld teaches shock score value comprises a cardiogenic shock score value. (Rosenfeld, [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1”).
Regarding Claim 5, combination of Rosenfeld and Jentzer teaches the method of claim 4,
Rosenfeld is silent on wherein generating the shock stage classification data further comprises correlating the cardiogenic shock score value with an SCAI shock stage using the computer system.
However, Jentzer teaches wherein generating the shock stage classification data further comprises correlating the cardiogenic shock score value with an SCAI shock stage using the computer system. (Jentzer, Page 2125, The SCAI statement authors clearly emphasize the added hazard posed by the presence of CA occurring in patients with or at risk of CS (12). In this cohort, the prevalence of CA increased substantially with increasing shock stage, highlighting the correlation between CA and severe shock in CICU patients. In our analysis, we clearly demonstrate the added mortality hazard posed by CA at all levels of shock severity, validating CA as a prognostically important modifier in the SCAI shock classification”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification score to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 6, combination of Rosenfeld and Jentzer teaches the method of claim 4,
Rosenfeld further teaches wherein the cardiogenic shock score value is computed based on data in the patient health data associated with measures of hypotension, lactate, vasopressor use, renal function, temporary mechanical support, and cardiac arrest. (Rosenfeld, Table 1, ALGORITHMS, & PRACTICE STANDARDS DECISION SUPPORT, Cardiovascular, MANAGEMENT OF HYPOTENSION, INOTROPES, MYOCARDIAL INFARCTION, renal dysfunction, see figure 33-37)
Regarding Claim 7, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld is silent on wherein the shock stage classification data indicate a risk stage for deterioration of the patient.
However, Jentzer teaches wherein the shock stage classification data indicate a risk stage for deterioration of the patient (Jentzer, 2120, right col. Deterioration was defined as increasing vasoactive drug requirements after the first hour or a rising lactate level after admission. We used pragmatic and simplified definitions to divide patients into the 5 SCAI shock stages with increasing severity (A through E) using combinations of these variables (Central Illustration)”.
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification score to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 8, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld further teaches wherein the patient health data comprise EMR data for the patient. (Rosenfeld, Figure 9,208, Database Server/Warehouse, Figure 20, note stored in EMR line log updated, Figure 9, [0202], “The database server/warehouse function 208 comprises the amassed information of a wide variety of patients, in their various conditions, treatments, outcomes, and other information of a statistical nature that will assist clinicians and intensivists in treating patients in the ICU”).
Regarding Claim 9, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld further teaches wherein the alert comprises an electronic message generated by the computer system. Rosenfeld, Figure 19, Rule engine 642, Alert screen 644; [0225] Referring to FIG. 19. Thus, monitor 636 provides information in HL 7 form to the interface engine 638. The physiological data is then formatted by the interface engine for storage in the database 640 where all patient information is maintained. The rules engine 642 searches for patterns of data indicative of clinical deterioration. The data base for each individual patient is then reviewed and process rules are applied 460 to the vital sign data” [0214]. These process rules relate to certain alarming conditions which, if a certain threshold is reached, provides an alarm to the intensivist on duty. The vital sign alarm 462 is then displaced to the intensivist who can then take appropriate action [0226] One family of alarms looks for changes in vital signs over time, using pre-configured thresholds. These thresholds are patient-specific and setting/disease-specific”).
Regarding Claim 10, combination of Rosenfeld and Jentzer teaches the method of claim 9,
Rosenfeld further teaches wherein generating the alert comprises sending a page to a clinician by transmitting the page from the computer system to a pager. ”[0214]. These process rules relate to certain alarming conditions which, if a certain threshold is reached, provides an alarm to the intensivist on duty. The vital sign alarm 462 is then displaced to the intensivist who can then take appropriate action”).
Regarding Claim 11, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld is silent on wherein the alert further comprises a tier alert associated with an SCAI stage indicated by the shock stage classification data.
Jentzer teaches wherein the alert further comprises a tier alert associated with an SCAI stage indicated by the shock stage classification data (Jentzer, Page 2121, right col. The prevalence of CA increased across the SCAI shock stages, from 7.3% in stage A to 55.8% in stage E. As the SCAI shock stage increased, there were more extensive vital sign and laboratory abnormalities, higher severity of illness scores, and more frequent AKI (Table 4). The use and dosage of vasoactive medications and supportive therapies”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification score to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Regarding Claim 12, combination of Rosenfeld and Jentzer teaches the method of claim 11,
Rosenfeld further teaches wherein the order set is updated based on a tier indicated by the tier alert. (Rosenfeld, Figure 33A-33C, [0345], “Therefore, the intensivist is lead through a decision support algorithm which prompts the
intensivist to determine the appropriate care to be given. [0345] Initially, the intensivist is prompted to determine whether the patient is hemodynamically stable (no angina, heart failure, or hypotension (systolic less than 80 mm)) 2200. If this criterion is not met, the intensivist is prompted to go to the cardio-pulmonary guidelines algorithm which is generally known to those skilled in the art. [0346] Alternatively, if this criterion is met, the intensivist is prompted to determine whether the patient is within 7 days of a myocardial infarction or at risk for myocardial ischemia 2202. If the patient is not within 7 days of a myocardial infarction or at risk for myocardial ischemia 2202, the intensivist is prompted to determine whether the wide complex QRS rhythm is sustained (greater than 30 seconds) 223”. Based on the prompted alert the internist follows the algorithm to treat the patient accordingly).
Regarding Claim 13, combination of Rosenfeld and Jentzer teaches the method of claim 11,
Rosenfeld further teaches wherein generating the alert comprises sending the alert to multiple users in a multidisciplinary health care team based on the tier indicated by the tier alert. (Rosenfeld, Figure 9, patient info front end at different locations. [0036], “The command center/remote location is manned by intensivists 24 hours a day, seven days per week. Each ICU comprises a nurse's station, to which data flows from individual beds in the ICU. Each patient in the ICU is monitored by a video camera, as well as by clinical monitors typical for the intensive care unit. These monitors provide constant real time patient information to the nurse's station, which in turn pro vides that information over a dedicated T-1 (high bandwidth) line to the ICU command center/remote location”. [0054] Intensivists detect impending problems by intermittently screening patient data, including both real time and continuously stored vital sign data. Patient severity of illness determines the frequency with which each patient's data is reviewed by the intensivists”).
Regarding Claim 14, combination of Rosenfeld and Jentzer teaches the method of claim 13,
Rosenfeld further teaches wherein generating the alert comprises providing, via the computer system, an access to a virtual videoconference room for the multiple users based on the tier indicated by the tier alert. (Rosenfeld, Figure 9, 11 [0052] Command center/remote location personnel communicate with ICU staff through videoconferencing and through "hot phones," which are dedicated telephones directly linked between the command center/remote location and the ICU. These communications links are used to discuss patient care issues and to communicate when a new order has been generated”).
Regarding Claim 15, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld further teaches further comprising generating a care path for the patient and displaying the care path for the patient to a clinician via the computer system, wherein the care path provides a visual depiction of escalation pathways and de-escalation pathways between different shock stages for the patient. (Rosenfeld, Figure 9, front end patient info, Figure 12, displays vital sign alarming 462, Display Real Time Vital Data, 472, Figure 11, remote surveillance display).
Regarding Claim 18, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld further teaches, wherein the shock stage comprises a cardiogenic shock stage. (Rosenfeld, [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1”).
Regarding Claim 19, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld further teaches wherein the shock stage comprises a non-cardiogenic shock stage. (Rosenfeld, Figure 31A, Step 2912, Non cardiogenic shock, such as “ Septic shock” , “occult hemorrhage”).
Regarding Claim 20, combination of Rosenfeld and Jentzer teaches the method of claim 19,
Rosenfeld further teaches, wherein the non-cardiogenic shock stage comprises one of a disruptive shock stage, a hypovolemic shock stage, or an obstructive shock stage. (Rosenfeld, Figure 31A, Step 2912, hypovolemic shock, such as “occult hemorrhage” NOTE: hypovolemic shock (e.g., hemorrhagic shock)).
Regarding Claim 21, combination of Rosenfeld and Jentzer teaches the method of claim 20,
Rosenfeld further teaches wherein the disruptive shock stage comprises a septic shock stage (Rosenfeld, Figure 31A, Step 2912, Non cardiogenic shock, such as “Septic shock”,
Regarding Claim 25, Rosenfeld teaches,
A system for shock staging comprising:
by receiving patient health data in real-time; applying the patient health data to a staging model that classifies the patient health data (Rosenfeld, Figure 9, Abstract, “patient care module displays selected data elements of the hospitalized patients. A decision support module applies decision support algorithms to selected data elements of a hospitalized patient and to user input to provide patient care advice. [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1,”): as including features that are correlated with a particular shock stage classification(Rosenfeld, [0058], “the patient care management system further comprises a relational database for storing a plurality of decision support algorithms and for prompting intensivists to provide care to patients based upon any of the decision support algorithms;
a paging module to generate an alert (Rosenfeld, Figure 19, Rule engine 642, Alert screen 644; [0225] Referring to FIG. 19. Thus, monitor 636 provides information in HL 7 form to the interface engine 638. The physiological data is then formatted by the interface engine for storage in the database 640 where all patient information is maintained. The rules engine 642 searches for patterns of data indicative of clinical deterioration. [0226] One family of alarms looks for changes in vital signs over time, using pre-configured thresholds. These thresholds are patient-specific and setting/disease-specific”in response to the shock stage classification data indicating a change in a shock stage ((Rosenfeld, [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1, and [00232], As noted in Table I below, a wide variety of conditions is noted. Each of the conditions has an associated guideline of practice standard that can be presented to the intensivist who might be faced with that particular condition in a patient”. Therefore, standard decision support for cardiogenic shock and generating alarm when a specific threshold is exceeded reads on the classification of shock stages (predefined threshold for shock stages); and
an engagement module (Figure 9-11, Front end Patient info, [0052] Command center/remote location personnel communicate with ICU staff through videoconferencing and through "hot phones," which are dedicated telephones directly linked between the command center/remote location and the ICU. These communications links are used to discuss patient care issues and to communicate when a new order has been generated. to generate an updated order set for the patient (Rosenfeld, [0230] “In order to standardize treatment across ICUs at the highest possible level, decision support algorithms are used in the present invention. These include textural material describing the topic, scientific treatments and possible complications. This information is available in real time to assist in all types of clinical decisions from diagnosis to treatment to triage”. Figure 34A- steps 2200-2218, figure 34b”) in response to the shock stage classification data indicating the change in the shock stage Rosenfeld, [0232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1, and [00232], As noted in Table I below, a wide variety of conditions is noted. Each of the conditions has an associated guideline of practice standard that can be presented to the intensivist who might be faced with that particular condition in a patient”. Therefore, standard decision support for cardiogenic shock and generating alarm when a specific threshold is exceeded reads on the classification of shock stages (predefined threshold for shock stages a staging module to generate shock stage classification data).
Rosenfeld teaches standard support decision algorithm for cardiogenic shocks and consequently implement treatment plan, but Rosenfeld is silent on generate shock stage classification data.
However, Jentzer teaches generate shock stage classification data. (Jentzer, Table 2, Figure 1, Page 2119, “method, SCAI CS stages A through E were classified retrospectively using CICU admission data based on the presence of hypotension or tachycardia, hypoperfusion, deterioration, and refractory shock. Hospital mortality in each SCAI shock stage was stratified by cardiac arrest (CA)”. Page 2120 right col. “We used pragmatic and simplified definitions to divide patients into the 5 SCAI shock stages with increasing severity (A through E) using combinations of these variables (Central Illustration”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Claims 16-17, and 22-24 are rejected under 35 U.S.C. 103 as being unpatentable over Rosenfeld and in view of Jentzer as applied to claim 1, and in further view of Demazumder, Deeptankar (WO 2019/046854 A1, hereinafter, Demazumder).
Regarding Claim 16, combination of Rosenfeld and Jentzer teaches the method of claim 1,
Rosenfeld is silent on wherein the staging algorithm comprises a machine learning model trained on training data to generate shock stage classification data from patient health data.
However, Demazumder teaches wherein the staging algorithm comprises a machine learning model trained on training data to generate shock stage classification data from patient health data. (Demazumder, [0040], “assessment of health care quality, based on analysis of time-varying physiological signals using infomiatic methods. The infomiatic methods include control-theoretic, mechanistic model- and machine-learning based algorithm development and deployment in a device, apparatus or computing platform, linear and nonlinear analyses, utilizing hardware and software tools for data acquisition, storage, management and analysis of electronic health records (EHR), development of metadata”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to incorporate a machine learning model based algorithm to modified Rosenfeld’s decision algorithm to determine cardiogenic shock stages classification as taught by Demazumder with the benefit of providing individualized health evaluation, risk stratification and early identification of impending catastrophic or worsening illness or other adverse events with more accurate early prediction. (Demazumder, [0040]).
Regarding Claim 17, combination of Rosenfeld, Jentzer, and Demazumder teaches the method of claim 16,
Rosenfeld and Jentzer are silent on wherein the machine learning model is a supervised learning model.
However, Demazumder teaches wherein the machine learning model is a supervised learning model. (Demazumder, [0093], “progressive Ai (e.g., support vector machine (SVM), random forests (RF), classification and regression tree (CART), and supervised and unsupervised deep learning techniques. Combinations maybe explored through forest trees to consistently identify the most important independent predictors”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to incorporate a machine learning model based algorithm to modified Rosenfeld’s decision algorithm to determine cardiogenic shock stages classification as taught by Demazumder with the benefit of providing individualized health evaluation, risk stratification and early identification of impending catastrophic or worsening illness or other adverse events with more accurate early prediction. (Demazumder, [0040]).
Regarding Claim 22, Rosenfeld teaches,
A non-transitory computer-readable media having stored thereon instructions that when executed by a processor cause the processor to perform a method comprising: retrieving patient health data from a data storage in real-time, wherein the patient health data are associated with a patient (Rosenfeld, Figure 9, and 12, [0202], 0202] Referring to FIG. 9, the distributed architecture of the present invention is shown. In concept, the distributed architecture comprises a headquarters component 200, The database server/warehouse function 208 comprises the amassed information of a wide variety of patients. in their various conditions, treatments, outcomes, and other information of a statistical nature that will assist clinicians and intensivists in treating patients in the ICU. The headquarters' function also serves to allow centralized creation of decision support algorithms and a wide variety of other treatment information that can be centrally managed and thereby standardized across a variety of command center/remote locations);
continuously retrieved from the data storage; and storing the shock stage classification data using the processor. (Rosenfeld, [0058], “the patient care management system further comprises a relational database for storing a plurality of decision support algorithms and for prompting intensivists to provide care to patients based upon any of the decision support algorithms. The algorithms are selected from the group consisting of algorithms for treating; (…) Cardiogenic Shock”). [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1,
Rosenfeld teaches standard support decision algorithm for cardiogenic shocks and consequently implement treatment plan, but Rosenfeld is silent on generating shock stage classification data for the patient in real-time.
However, Jentzer teaches generating shock stage classification data for the patient in real-time. (Jentzer, Table 2, Figure 1, Page 2119, “method, SCAI CS stages A through E were classified retrospectively using CICU admission data based on the presence of hypotension or tachycardia, hypoperfusion, deterioration, and refractory shock. Hospital mortality in each SCAI shock stage was stratified by cardiac arrest (CA)”. Page 2120 right col. “We used pragmatic and simplified definitions to divide patients into the 5 SCAI shock stages with increasing severity (A through E) using combinations of these variables (Central Illustration”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Rosenfeld decision algorithm to include criteria of SCAI classification to determine cardiogenic shock stages as taught by Jentzer with the benefit of providing robust hospital mortality risk stratification, and predict the risk of death in patients with, and at risk for, CS preventing earlier and take appropriate action based on patience data. (Jentzer, Abstract, conclusion). It would have been obvious to a person of ordinary skill to include the SCAI shock stage classification guideline from well-known, the Society for Cardiovascular Angiography and Interventions (SCAI) shock stage classification for adult patients with the algorithm, in order to yield the predicted results of generating patient’s cardiogenic shock stage classification, yet with higher accuracy of predicting risk of patience death based on patient health data (KSR).
Both Rosenfeld and Jentzer teach using algorithm to classify data, but both are silent on using a machine learning model.
However, Demazumder teaches accessing a machine learning model trained on training data to generate shock stage classification data from patient health data; and by inputting the patient health data to the machine learning model as the patient health data (Demazumder, [0040], “assessment of health care quality, based on analysis of time-varying physiological signals using infomiatic methods. The infomiatic methods include control-theoretic, mechanistic model- and machine-learning based algorithm development and deployment in a device, apparatus or computing platform, linear and nonlinear analyses, utilizing hardware and software tools for data acquisition, storage, management and analysis of electronic health records (EHR), development of metadata. [0061] The advantage of using neural networks is that sophisticated multi-modal classification using convolutional layers and time-series data can be combined directly with static features within the same classifier”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to incorporate a machine learning model based algorithm to modified Rosenfeld’s decision algorithm to determine cardiogenic shock stages classification as taught by Demazumder with the benefit of providing individualized health evaluation, risk stratification and early identification of impending catastrophic or worsening illness or other adverse events with more accurate early prediction. (Demazumder, [0040]).
Regarding Claim 23, combination of Rosenfeld, Jentzer, and Demazumder teaches the non-transitory computer-readable media of claim 22
Rosenfeld further teach wherein the method performed by the processor further comprises generating an alert (Rosenfeld, Figure 19, Rule engine 642, Alert screen 644; [0225] Referring to FIG. 19. Thus, monitor 636 provides information in HL 7 form to the interface engine 638. The physiological data is then formatted by the interface engine for storage in the database 640 where all patient information is maintained. The rules engine 642 searches for patterns of data indicative of clinical deterioration. [0226] One family of alarms looks for changes in vital signs over time, using pre-configured thresholds. These thresholds are patient-specific and setting/disease-specific” when the shock stage classification data for the patient indicate a change in a shock stage. (Rosenfeld, [00232], “the algorithm providing decision support to intensivists as well as information concerning the latest care and practice standards for any given condition” such as “cardiogenic shock”, see table 1, and [00232], As noted in Table I below, a wide variety of conditions is noted. Each of the conditions has an associated guideline of practice standard that can be presented to the intensivist who might be faced with that particular condition in a patient”. Therefore, standard decision support for cardiogenic shock and generating alarm when a specific threshold is exceeded reads on the classification of shock stages (predefined threshold for shock stages).
Regarding Claim 24, combination of Rosenfeld, Jentzer, and Demazumder teaches the non-transitory computer-readable media of claim 23,
Rosenfeld further teaches wherein the method performed by the processor further comprises in response to the alert, generating an order set based on the shock stage classification data for the patient (Rosenfeld, [0230] “In order to standardize treatment across ICUs at the highest possible level, decision support algorithms are used in the present invention. These include textural material describing the topic, scientific treatments and possible complications. This information is available in real time to assist in all types of clinical decisions from diagnosis to treatment to triage”. Figure 34A- steps 2200-2218, figure 34b, [0345] Initially, the intensivist is prompted to determine whether the patient is hemodynamically stable (no angina, heart failure, or hypotension (systolic less than 80 mm)) 2200. If this criterion is not met, the intensivist is prompted to go to the cardio-pulmonary guidelines algorithm which is generally known to those skilled in the art”. NOTE: for specific cardiogenic shock, standard decision support known in the medical field is used. For example. the stage classifications are standard criteria for treatment applied.); and
storing the order set in an electronic medical record (EMR) for the patient. (Rosenfeld, Figure 9,208, Database Server/Warehouse, Figure 20, note stored in EMR line log updated, Figure 9, [0202], The database server/warehouse function 208 comprises the amassed information of a wide variety of patients, in their various conditions, treatments, outcomes, and other information of a statistical nature that will assist clinicians and intensivists in treating patients in the ICU. The headquarters' function also serves to allow centralized creation of decision support algorithms and a wide variety of other treatment information that can be centrally managed and thereby standardized across a variety of command center/remote locations”).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Min et al. (US 11210786 B2) recites “The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.” (Abstract).
Villela et al. “Systems of Care in Cardiogenic Shock”. Frontiers in Cardiovascular Medicine, Electronic Publication Date: 2021-09-16.
Abstract: “Outcomes for cardiogenic shock (CS) patients remain relatively poor despite significant advancements in primary percutaneous coronary interventions (PCI) and temporary circulatory support (TCS) technologies. Mortality from CS shows great disparities that seem to reflect large variations in access to care and physician practice patterns. Recent reports of different models to standardize care in CS have shown considerable potential at improving outcomes. The creation of regional, integrated, 3-tiered systems, would facilitate standardized interventions and equitable access to care. Multidisciplinary CS teams at Level I centers would direct care in a hub-and-spoke model through jointly developed protocols and real-time shared decision making. Levels II and III centers would provide early access to life-saving therapies and safe transfer to designated hub centers. In regions with large geographical distances, the implementation of telemedicine-cardiac intensive care unit (CICU) care can be an important resource for the creation of effective systems of care”.
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/DILARA SULTANA/Examiner, Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858 3/2/2026