U.S. patent application number 13/678853 was filed with the patent office on 2013-07-04 for systems and methods for automated prediction of risk for perioperative complications based on the level of obstructive sleep apnea.
This patent application is currently assigned to WATERMARK MEDICAL, INC.. The applicant listed for this patent is Watermark Medical, Inc.. Invention is credited to Chris Berka, Daniel J. Levendowski, Philip R. Westbrook.
Application Number | 20130172687 13/678853 |
Document ID | / |
Family ID | 39528325 |
Filed Date | 2013-07-04 |
United States Patent
Application |
20130172687 |
Kind Code |
A1 |
Levendowski; Daniel J. ; et
al. |
July 4, 2013 |
SYSTEMS AND METHODS FOR AUTOMATED PREDICTION OF RISK FOR
PERIOPERATIVE COMPLICATIONS BASED ON THE LEVEL OF OBSTRUCTIVE SLEEP
APNEA
Abstract
A system for predicting risk for perioperative complications is
described including a user device for receiving a set of risk
factors to determine perioperative complications for a patient
including patient data useful to determine the likelihood of
obstructive sleep apnea. The system also includes an acquisition
module to receive data from an obstructive sleep apnea sleep study
of the patient. Further a determination module can determine the
severity of obstructive sleep apnea for the patient. The system can
also include an analysis module having a predictive model that
incorporates one or more prediction equations for predicting
perioperative complications derived from one or more databases
having multiple patient data relevant to predict perioperative
complications. The analysis module can be configured to apply the
one or more prediction equations to the set of risk factors and the
severity of obstructive sleep apnea for the patient in order to
identify perioperative complication risks of that patient and to
generate a perioperative complications report for the patient.
Inventors: |
Levendowski; Daniel J.;
(Carlsbad, CA) ; Westbrook; Philip R.; (Carlsbad,
CA) ; Berka; Chris; (Carlsbad, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Watermark Medical, Inc.; |
Boca Raton |
FL |
US |
|
|
Assignee: |
WATERMARK MEDICAL, INC.
Boca Raton
FL
|
Family ID: |
39528325 |
Appl. No.: |
13/678853 |
Filed: |
November 16, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11955185 |
Dec 12, 2007 |
8333696 |
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13678853 |
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60869795 |
Dec 13, 2006 |
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Current U.S.
Class: |
600/300 |
Current CPC
Class: |
G16H 50/50 20180101;
G06F 19/00 20130101; A61B 5/0002 20130101; A61B 5/7275 20130101;
A61B 5/4818 20130101 |
Class at
Publication: |
600/300 |
International
Class: |
A61B 5/00 20060101
A61B005/00 |
Claims
1. A system for predicting risk for perioperative complications
comprising: a user device for receiving a set of risk factors to
determine perioperative complications for a patient including
patient data useful to determine the likelihood and severity of
sleep apnea; an acquisition module to receive data from a sleep
apnea sleep study of the patient; a determination module to
determine the severity of sleep apnea for the patient; and an
analysis module having a predictive model that incorporates one or
more prediction equations for predicting perioperative
complications derived from one or more databases having multiple
patient data relevant to predict perioperative complications, the
analysis module configured to apply the one or more prediction
equations to the set of risk factors and the severity of sleep
apnea for the patient in order to identify and report the
likelihood of perioperative complication risks of that patient.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 11/955,185, filed Dec. 12, 2007, which claims
priority to U.S. Provisional Patent Application No. 60/869,795,
filed Dec. 13, 2006. Each of these applications is incorporated
herein by reference in their entirety.
FIELD OF THE INVENTION
[0002] The present invention relates generally to a hospital
monitoring system, and, in particular to an apnea risk evaluation
system for automated prediction of risk for perioperative
complications.
BACKGROUND OF THE INVENTION
[0003] Obstructive sleep apnea (OSA) is characterized by periodic,
partial or complete obstruction of the upper airway during sleep.
The underlying pathophysiology of OSA is complex. However, it is
generally accepted that the stability and patency of the upper
airway is dependent upon the action of oropharyngeal dilator
muscles, which are normally activated during each inspiration.
These muscles can increase their activity to overcome obstruction
during wakefulness, but the normal decrease in activity that occurs
with sleep can allow the susceptible airway to collapse. Return of
airway muscle activity appears to require either arousal, or a
change of brain state to a lighter stage of sleep. Given the choice
between sleeping and breathing, the un-medicated brain will choose
breathing. The repetitive asphyxia causes repetitive arousals,
resulting in fragmented sleep and daytime somnolence. Airway
obstruction also causes sleep-associated oxygen desaturation
(Chronic Intermittent Hypoxia, or CIH), episodic hypercarbia, and
cardiovascular dysfunction.
[0004] The prevalence of OSA in adults in Western countries is
estimated to be 5%. See Young, T., P. E. Peppard, and D. J.
Gottlieb in their article entitled "Epidemiology of obstructive
sleep apnea: a population health perspective", published in
American Journal of Respiratory and Critical Care Medicine, 2002,
165(9): p. 1217-39. This number is likely to increase as the
population becomes older and more obese. The prevalence of
obstructive sleep apnea in surgical patients has been estimated to
be 1-9%, and may be even more common in certain populations. See
Kaw, R., F. Michota, A. Jaffer, S. Ghamande, D. Auckley, and J.
Golish in their article entitled "Unrecognized sleep apnea in the
surgical patient: implications for the perioperative setting,"
published in Chest, 2006. 129(1): p. 198-205. A recently completed
study at Washington University found that the prevalence of OSA
(defined as AHI>5) in surgical patients was more than 19%. See
Finkel, K., L. Saager, E. Safarzadeh, M. Bottros, and M. Avidan in
their article entitled "Obstructive Sleep Apnea: The Silent
Pandemic," presented in the ASA Annual Meeting, 2006, Chicago,
Ill.
[0005] Patients with OSA, even if asymptomatic, present special
perioperative challenges. During the first three postoperative
days, the risk of life-threatening apnea is increased in OSA
patients due to the high levels of pain that mandate administration
of analgesics (especially opioids). In the subsequent three
postoperative days, REM sleep and deep slow-wave NREM rebounds,
which again increases the risk of prolonged apneas during sleep.
Therefore, patients who suffer from OSA appear to have two
separate, sequential reasons for increased apnea during the week
following surgery. See Rosenberg, J., G. Wildschiodtz, M. H.
Pedersen, F. von Jessen, and H. Kehlet, in the article entitled
"Late postoperative nocturnal episodic hypoxaemia and associated
sleep pattern" published in the British Journal of Anaesthesia,
1994. 72(2): p. 145-50, Reeder, M. K., M. D. Goldman, L. Loh, A. D.
Muir, K. R. Casey, and J. R. Lehane, in the article entitled "Late
postoperative nocturnal dips in oxygen saturation in patients
undergoing major abdominal vascular surgery. Predictive value of
pre-operative overnight pulse oximetry" published in the British
Journal of Anaesthesia, 1992. 7(2): p. 110-5, Reeder, M. K., M. D.
Goldman, L. Loh, A. D. Muir, K. R. Casey, and D. A. Gitlin, in the
article entitled "Postoperative obstructive sleep apnoea.
Haemodynamic effects of treatment with nasal CPAP" published in the
British Journal of Anaesthesia, 1991. 46(10): p. 849-53, Reeder, M.
K., M. D. Goldman, L. Loh, A. D. Muir, P. Foex, K. R. Casey, and P.
J. McKenzie, in the article entitled "Postoperative hypoxaemia
after major abdominal vascular surgery" published in the British
Journal of Anaesthesia, 1992. 68(1): p. 23-6 and Rosenberg, J. and
H. Kehlet, in the article entitled "Postoperative episodic oxygen
desaturation in the sleep apnoea syndrome" published in Acta
Anaesthesiol Scand, 1991. 35(4): p. 368-9.
[0006] The majority of the postoperative concerns in OSA patients
are possible respiratory depression and hypoxemia following
surgery. This may be potentiated by administration of systemic
opioids for analgesia. Narcotics and sedatives suppress the brain's
afferent output to pump muscles such as the diaphragm and chest
wall, resulting in inadequate tidal volume and associated fall in
minute ventilation and a progressive rise in carbon dioxide levels.
The rise in carbon dioxide levels causes further suppression of the
arousal response, therefore, potentially causing respiratory
arrest. Narcotics and sedatives also depress the brains afferent
output to upper airway dilator muscles causing a reduction in upper
airway tone.
[0007] In the early postoperative period, high pain scores, opioid
consumption, and surgery itself may all contribute to a disruption
of the normal sleep architecture. See Rosenberg-Adamsen, S., H.
Kehlet, C. Dodds, and J. Rosenberg in their article entitled
"Postoperative sleep disturbances: mechanisms and clinical
implications" published in the British Journal of Anaesthesia,
1996. 76(4): p. 552-9, Cronin, A., J. C. Keifer, H. A. Baghdoyan,
and R. Lydic, Opioid in their article "inhibition of rapid eye
movement sleep by a specific mu receptor agonist" published in the
British Journal of Anaesthesia, 1995. 74(2): p. 188-92, Keifer, J.
C., H. A. Baghdoyan, and R. Lydic, in their article entitled "Sleep
disruption and increased apneas after pontine microinjection of
morphine" published in Anesthesiology, 1992. 77(5): p. 973-82 and
Knill, R. L., C. A. Moote, M. I. Skinner, and E. A. Rose, in the
article entitled "Anesthesia with abdominal surgery leads to
intense REM sleep during the first postoperative week" published in
Anesthesiology, 1990. 73(1): p. 52-61. The observed changes are
sleep deprivation and fragmentation in the early days after
surgery, REM sleep is usually absent on the first postoperative
night, and sometimes even on the second and third postoperative
nights. See Knill, R. L., C. A. Moote, M. I. Skinner, and E. A.
Rose, in the article entitled "Anesthesia with abdominal surgery
leads to intense REM sleep during the first postoperative week"
published in Anesthesiology, 1990. 73(1): p. 52-61. Slow-wave sleep
is also suppressed. Obstructive breathing is exacerbated in the
late post-operative period as a result of a raised arousal
threshold during deep non-REM sleep and the inherent breathing
instability during REM sleep. In most patients, REM sleep
subsequently reappears (rebound REM sleep) with increased intensity
and duration, and REM-associated hypoxemic episodes increase about
three-fold on the second and third postoperative nights compared
with the night before surgery. See Knill, R. L., C. A. Moote, M. I.
Skinner, and E. A. Rose, in the article entitled "Anesthesia with
abdominal surgery leads to intense REM sleep during the first
postoperative week" published in Anesthesiology, 1990. 73(1): p.
52-61 and Rosenberg, J., G. Wildschiodtz, M. H. Pedersen, F. von
Jessen, and H. Kehlet, in their article entitled "Late
postoperative nocturnal episodic hypoxaemia and associated sleep
pattern" published in the British Journal of Anaesthesia, 1994.
72(2): p. 145-50. Sleep studies [8, 10-12], performed in patients
undergoing major abdominal surgery and open heart surgery have
shown that postoperative sleep patterns are disturbed severely by
early depression of REM and slow wave sleep (SWS) in the early
postoperative period, and by rebound of REM sleep and SWS in the
late postoperative period. See Knill, R. L., C. A. Moote, M. I.
Skinner, and E. A. Rose, in the article entitled "Anesthesia with
abdominal surgery leads to intense REM sleep during the first
postoperative week" published in Anesthesiology, 1990. 73(1): p.
52-61, Aurell, J. and D. Elmqvist, in the article entitled "Sleep
in the surgical intensive care unit: continuous polygraphic
recording of sleep in nine patients receiving postoperative care"
published in the British Medical Journal (Clin Res Ed), 1985.
290(6474): p. 1029-32, Ellis, B. W. and H. A. Dudley, in the
article entitled "Some aspects of sleep research in surgical
stress" published in the J Psychosom Res, 1976. 20(4): p. 303-8 and
On, W. C. and M. L. Stahl, in the article entitled "Sleep
disturbances after open heart surgery" published in the American
Journal of Cardiology, 1977. 39(2): p. 196-201. The rebound of REM
sleep may contribute to the development of sleep-disordered
breathing and nocturnal episodic hypoxemia. See Catley, D. M., C.
Thornton, C. Jordan, J. R. Lehane, D. Royston, and J. G. Jones, in
the article entitled "Pronounced, episodic oxygen desaturation in
the postoperative period: its association with ventilatory pattern
and analgesic regimen" published in Anesthesiology, 1985. 63(1): p.
20-8, Gentil, B., A. Lienhart, and B. Fleury, in the article
entitled "Enhancement of postoperative desaturation in heavy
snorers" published in Anesthesia Analgesia, 1995. 81(2): p. 389-92
and Reeder, M. K., M. D. Goldman, L. Loh, A. D. Muir, K. R. Casey,
and J. R. Lehane, in the article entitled "Late postoperative
nocturnal dips in oxygen saturation in patients undergoing major
abdominal vascular surgery. Predictive value of pre-operative
overnight pulse oximetry" published in Anaesthesia, 1992. 47(2): p.
110-5. Also REM rebound in the late postoperative period (at a time
when oxygen therapy is likely to be discontinued and the patient
discharged) may explain the finding that the highest perioperative
mortality risk is not the day of surgery, or even the second day;
it is on the third or fourth postoperative day. See Rosenberg, J.,
M. H. Pedersen, T. Ramsing, and H. Kehlet, in the article entitled
"Circadian variation in unexpected postoperative death" published
in the British Journal of Surgery, 1992. 79(12): p. 1300-2, which
is herein incorporated by reference. The rebound of SWS can raise
the arousal threshold, prolonging the time to arousal and allowing
longer episodes of obstruction with deeper oxyhemoglobin
desaturations.
[0008] The impact of OSA on patient safety is beginning to be
recognized. See Deutscher, R., D. Bell, and S. Sharma, in the
article entitled "OSA protocol promotes safer care" published in
the Anesthesia Patient Safety Foundation Newsletter 2002-2003: p.
58. When compared to matched controls, orthopedic patients with OSA
had more adverse outcomes. See Gupta, R., J. Parvizi, A. Hanssen,
and P. Gay, in the article entitled "Postoperative complications in
patients with obstructive sleep apnea syndrome undergoing hip or
knee replacement: a case-control study" published in the Mayo
Clinic Proceedings, 2001. 76: p. 897-905. Up to one-third of these
patients developed substantial respiratory or cardiac
complications, including arrhythmias, myocardial ischemia,
unplanned ICU transfers and/or reintubation. The length of
hospitalization was significantly longer for patients with OSA
compared to the control subjects. For these orthopedic patients the
majority of the cardiorespiratory or neuropsychiatric postoperative
complications occurred within the first 72 hours after the joint
replacement. The authors theorized that this might have been due to
the combination of anesthetic agents, sedatives, and narcotics in
conjunction with supine positioning during sleep postoperatively.
Conversely, Sabers et al. found that OSA was not an independent
risk factor for unanticipated hospital admission or for other
adverse perioperative events in patients scheduled for outpatient
surgery. These conflicting findings suggest that the
type/invasiveness of surgery, which in turn may extend length of
hospitalization, may contribute to the interaction between OSA and
post-surgical complications.
[0009] Recent articles suggest that, at the present time,
disastrous respiratory outcomes during the perioperative management
of patients with obstructive sleep apnea are a major problem for
the anesthesia community. See Benumof, J. L., in the article
entitled "Obesity, sleep apnea, the airway, and anesthesia"
published in Current Opinion in Anaesthesiology, 2004. 17(1): p.
21-30. The American Society of Anesthesiologists recently published
Practice Parameters for the perioperative management of patients
with OSA. See A Report by the American Society of Anesthesiologists
Task Force on Perioperative Management of Patients with Obstructive
Sleep Apnea, Practice Guidelines for the Perioperative Management
of Patients with Obstructive Sleep Apnea, published in
Anesthesiology, 2006. 104(5): p. 1081-1093. The purpose of the
guideline was to improve perioperative care and reduce the risk of
adverse outcomes in patients with OSA who receive sedation,
analgesia, or anesthesia for diagnostic or therapeutic procedures
under the care of an anesthesiologist. Unfortunately, due to a lack
of medical evidence to guide the ASA recommendations, the practice
parameters were based solely on expert opinion and are heavily
reliant on the judgment of the anesthesiologist. Although there is
an understanding of the broad categories of what increases the
likelihood of developing a post-surgical complication, little is
known about how individual risk factors interact to cause these
complications. See Rock, P. and A. Passannante, in the article
entitled "Preoperative assessment: pulmonary" published in
Anesthesiology Clinics of North America, 2004. 22(1): p. 77-91,
which is herein incorporated by reference. While several
observations have led to considerable speculation in the literature
as to the implications of sleep disturbance and sleep disordered
breathing on perioperative morbidity and mortality, evidence of a
causal relationship is still slight. See Loadsman, J. and D.
Hillman, in the article entitled "Anaesthesia and sleep apnoea"
published in the British Journal of Anaesthesia, 2001. 86(2): p.
254-266. Although OSA and anesthetic agents both produce
significant changes in the respiratory system, little information
exists regarding their relative contribution to the subsequent
development of post-surgical complications, or the synergy between
these and/or other risk factors such as the positional influence of
OSA, type of anesthesia, use of opioids or invasiveness of the
surgery.
[0010] A number of methods have been proposed to reduce the risk
for postoperative complications. For example, Redmond proposed
administering a drug or agent, such as methylol to a patient in
conjunction with CPB surgery to reduce the risk for complications.
See U.S. Pat. No. 6,641,571, issued Nov. 4, 2003. Turcott proposed
monitoring respiration patterns to identify the presence of
periodic breathing or Cheyne-Stokes respiration (i.e., recognize
hyperventilation and apnea or hypoventilation) in patients with
congestive heart failure. See U.S. Pat. No. 6,600,949, issued Jul.
29, 2003. This information may be used to warn the patient or
healthcare provider of changes in the patient's condition that
might result in a perioperative complication. Lynn proposed to
automatically diagnose obstructive sleep apnea in a centralized
hospital critical care or cardiac ward environment. In a preferred
embodiment, the system would automatically identify the presence
and severity of obstructive sleep apnea and communicate with an
intravenous infusion system to prevent the progression of said
identified obstructive sleep apnea by limiting infusion of a
narcotic. Bardy proposed a method for an automated multiple
near-simultaneous health disorder diagnosis and analysis, and, in
particular, to an automated collection and analysis patient care
system and method for ordering and prioritizing multiple health
disorders to identify an index disorder. See U.S. Pat. No.
7,117,028, issued Oct. 3, 2006. These methods propose reducing the
risk of complications via a means for monitoring or intervening, or
identifying a patient with the pathophysiology corresponding to a
heath disorder diagnosis. These methods differ from this invention
which provides a means to identify those patients with an increased
risk of perioperative complications, and provide physicians a
method to reduce risk as a result of an intervention (change in
sedative or narcotic, place on CPAP prior to surgery, etc).
[0011] There is no doubt that an integrated approach to the
identification of patients at risk for perioperative complications
will influence the practice of medicine. Advanced identification of
patients at greater risk for perioperative complications will
influence who administers an analgesic or, how, when and what
sedative is administered. Prior identification of high risk
patients can be used to determine the appropriate type of facility
to be used for a given procedure (i.e., outpatient facility vs.
hospital). Physicians can interactively model difference scenarios
with the patient to assess changes in risk, making it easier for
the clinician and patient to select the surgical option (i.e.,
nerve block vs. general anesthesia) which maximizes patient comfort
and safety.
SUMMARY OF THE INVENTION
[0012] In one embodiment, a system and method is described, which
can combine, for example, the acquisition of a patient's history
and physical information, type and invasiveness of the surgery,
type and dose of the intended sedative or narcotic, and level of
OSA severity which can be derived from a questionnaire and/or a
sleep study, for example; combined with a database of similar
measures, for example; and, in one embodiment, automated analyses
to predict the likelihood of perioperative complications and
extended hospitalization. The resulting data can allow, for
example, the system administrator to predict perioperative
complications such as those that occur during administration of
anesthesia, surgery, in the recovery room, on the medical/surgical
ward, and result in readmission within a predetermined period, for
example, 30 days.
[0013] In another embodiment, the system can predict the likelihood
of perioperative complications such as those that occur during
administration of anesthesia, surgery, in the recovery room, on the
medical/surgical ward, and result in readmission within a
predetermined period, for example, 30 days. The system provides a
means for physicians and patients to input information to a
database, and then through comparative analysis of patient data to
database data identify risk levels or assess changes in risk level
and as a result target interventions, such as the type and dosage
of sedative or narcotic, and use of CPAP prior to surgery, in order
to reduce the frequency and severity of complications.
[0014] In some embodiments, a statistical analysis, for example
logistic step-wise regression analysis, is used to determine the
relationships and interactions between perioperative complications
and the various inputs (i.e., risk factors described below). One of
the advantages of logistic regression analysis is that a likelihood
of a correct classification can be computed for each variable
included in the regression equation. Another advantage of logistic
regression analysis is its capability to derive a propensity score
from sets of predictive variables. This feature allows the
predictive capability derived multiple variables to be isolated
into a single measure, such that the predictive power from a
limited size data set is not exceeded or over fitted. In other
words, the practical application of "holding all other variables
constant" can be achieved by incorporating the predictive
capability of these variables into a propensity score and then
developing a new logistic regression equation to model the changing
variable. This feature is helpful given the plethora of risk
factors and the need to isolate risk factors to make predictions
based on case findings. In addition to logistic regression
analysis, there are other statistical analyses that can be employed
to derive probabilities or predict likelihood of events. As with
any of the various statistical approaches that can be employed, the
analysis begins with the database of data used to derive the
predictive equations. One skilled in the art can appreciate how
sets of predictive equations can be derived from a database of
patient information to determine the risk likelihoods, and look up
tables can be used to select recommendation/report messages
proposed as outputs from the system.
[0015] In some embodiments, the methods and systems described above
are implemented in a computer system. For example, the likelihood
of perioperative complications for a given patient is obtained
using sets of prediction equations derived from a patient
population of data and incorporated into a software application
that can be applied to an individual patient's data. Responses to
the patient and physician data can be inputted via a computer input
device and written to a database of patient responses.
Alternatively, a web-based application could be used to input
patient data to a database of patient information. In yet another
embodiment the software application containing the prediction
equations is executed on a computer with access to the patient's
database of data and connected to an output device, for example a
printer so that the prediction report can be printed. In a hospital
environment, portable, handheld or wireless devices can be used to
input or update patient and physician information to the patient's
database. With access to the patient data, the prediction software
could be operated from the portable, handheld or wireless device,
providing immediate updated information that could be presented to
the hospital staff via the LCD screen as the patient conditions
which impact the predictive model(s) are updated. The sleep study
data used to compute OSA severity can be downloaded, computed and
written directly to the patient's database. The risk predictions
can be immediately updated when the sleep data are acquired from
additional sleep studies obtained during the perioperative
period.
[0016] In one embodiment, the data from the patient's database and
the outcomes from the surgery are updated to the population
database. Routine additions to all inputs and outputs of the
predictive model will allow the accuracy of the equations to be
refined and improved. As the population database is expanded
separate rules may be developed to account for special cases.
[0017] Other features and advantages of the present invention
should be apparent from the following description which
illustrates, by way of example, aspects of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] FIG. 1 is a schematic drawing of an exemplary network
environment within which embodiments described herein can be
implemented.
[0019] FIG. 2 illustrates a model, including list of factors upon
which a system for predicting perioperative complications is
based.
[0020] FIG. 3 is a block diagram illustrating aspects of an
exemplary stand-alone computer system which implements embodiments
of the invention.
[0021] FIG. 4 is a block diagram illustrating aspects of an
exemplary stand-alone computer system which implements embodiments
of the invention.
[0022] FIG. 5 is a block diagram illustrating aspects of an
exemplary computer network system which implements embodiments of
the invention.
[0023] FIG. 6 is a flow diagram of a method for prediction of risk
for perioperative complications.
DETAILED DESCRIPTION
[0024] Methods and Systems for prediction of risks for
perioperative complications are described below. The following
description sets forth numerous specific details such as examples
of specific systems, components, methods, and so forth, in order to
provide a good understanding of several embodiments of the present
invention. It will be apparent to one skilled in the art, however,
that at least some embodiments of the present invention may be
practiced without these specific details. In other instances,
well-known components or methods are not described in detail or are
presented in simple block diagram format in order to avoid
unnecessarily obscuring the present invention. Thus, the specific
details set forth are merely exemplary. Particular implementations
may vary from these exemplary details and still be contemplated to
be within the spirit and scope of the present invention.
[0025] FIG. 1 is a schematic drawing of an exemplary network
environment within which embodiments described herein can be
implemented. Referring to FIG. 1 of the drawings, a network
environment within which embodiments of the described technique may
be practiced is indicated generally by reference numeral 110.
Network environment 110 can accommodate a number of user devices 12
(only a few of which have been shown) each of which is able to
communicate with a server 14 via a network 16. The network can be a
wide area network (WAN) or local area network (LAN) using
conventional network protocols or combinations of those. User
devices 12 are electronic devices capable of communicating with the
server 14 via the network 16. For example, user devices can be
personal computers, cellular telephones, or other electronic
devices capable of communicating over the network 16 directly or
indirectly. A server can be one or more computers or devices on a
network that manages network resources. A server can also refer to
one or more programs managing resources rather than the entire
computer.
Inputs to the System:
[0026] FIG. 2 illustrates a model, including list of factors upon
which a system for predicting perioperative complications is based.
A system and method for predicting perioperative complications can
be based on a number of risk factors that constitute the input to a
patient database. The means used to acquire the necessary
information and/or the risk factor may vary depending on the method
employed. For example, the list of factors can be inputted via the
user devices 12 illustrated and described in FIG. 1.
[0027] The input data can include demographic information such as
age, gender, pre- or post-menopausal and ethnicity; anthropomorphic
data such as weight, height, body mass index, neck or waist
circumference, hip to waist ratio; and history of co-morbid
diseases, such as hypertension, heart disease/congestive heart
failure, chronic obstructive pulmonary (COPD) or other lung
disease, diabetes or insulin insensitivity, stroke, pregnancy,
sleep disorders, depression, or cancer; and current medications are
all factors acquired during a pre-surgery history and physical
because they are known to influence outcomes.
[0028] In addition to demographic, anthropomorphic, and
co-morbidity data, patient responses as to the severity of symptoms
of daytime somnolence, history of snoring, witnessed obstructive
breathing during sleep or being awaken by obstructive breathing are
commonly used to assess the prior-probability of OSA, either by
clinical judgment or by statistical analysis. A number of
questionnaires are currently available to assess daytime somnolence
including the Epworth, Stanford, or Karolinka Sleepiness Scales.
Structured questionnaires such as the Berlin test identify the
severity and frequency of snoring, how often the patient is told
they stop breathing during sleep, or how often they wake up in the
night choking or struggling to breath (as a result of an apnea or
hypopnea).
[0029] In clinical practice, these demographic, anthropomorphic and
OSA related questionnaire responses should be acquired from the
patient prior to surgery either as part of a pre-admission
procedure or by the surgeon or primary-care physician. In the
preferred embodiment, these data would be conveniently obtained
using the Apnea Risk Evaluation system (ARES) Screener
questionnaire. These data could be obtained by the patient using a
written form of the Screener and entered or scanned into a computer
system, for example the user device 12, or entered using a
web-based application, and stored in a database of which an example
is illustrated in FIG. 3 described below.
[0030] Another set of data that should be obtained in order to
identify the risk of a perioperative complication are the
invasiveness of the surgery, type of anesthesia, and type of opioid
to be administered. All three of these risks are related and impact
the length of hospitalization. For example, a non-laparoscopic
abdominal surgery is invasive and usually requires general
anesthesia and a 72-hour hospital stay. Some laparoscopic surgery
could be performed with a regional or nerve blocking anesthetic
with a short or no hospital stay. As the invasiveness of the
surgery increases, so does the post-surgical dose and strength of
the pain medication. In the preferred embodiment, these three risk
factors are combined into a simple set of responses that could be
easily assigned by a surgeon, anesthesiologist or primary care
physician. A more refined means to categorize the surgery type can
be performed using the International Statistical Classification of
Diseases and Related Health Problems (ICD9) or current procedural
terminology (CPT) code assigned by the physician and a reference
table. Similar techniques can be used to allow physicians to select
a specific type or from a menu of anesthesia or post-operative
medication and dose to be administered by generic or brand name
with a reference table used to assign this information into
specific risk categories. These data could be obtained by the
physician using a written form and entered into a computer via, for
example the user device 12, or entered using a web-based
application, and stored in a database.
[0031] A third set of data that would be beneficial in the
identification of perioperative risk is the results from a
diagnostic or screening OSA sleep study. In the preferred
embodiment, the sleep study is conducted using the ARES Unicorder,
a single site wireless recorder that is affixed to the forehead by
the patient. It is used to acquire oxygen saturation, pulse rate,
airflow, respiratory effort, snoring levels (dB), head movement and
head position. Alternatively, electroencephalography,
electromyography and electrocculargrams are acquired so that
rebound in slow-wave (SWS) and rapid eye movement (REM) sleep that
increases the risk of complications can be periodically monitored.
Ideally the recorder monitors signal quality during use and alerts
the patient what needs to be fixed to improve signal quality,
preferable with verbal messages. Alternatively, a laboratory or
in-home Polysomonography (Level I or II) study, limited channel
(Level III) or screening (Level IV) study could be used to acquire
any or all of these signals or other signals (i.e., ECG, etc) that
may be used to assess sleep disordered breathing. See U.S. Pat. No.
7,117,028, issued Oct. 3, 2006. In some embodiments the assessment
can also be acquired through the user device described in FIG.
1.
[0032] FIG. 3 is a block diagram illustrating aspects of an
exemplary stand-alone computer system which implements embodiments
of the invention. The computer system can include a user device 12,
which can have a user interface to input a set of risk factors, for
example those illustrated in FIG. 2, that can be used to determine
whether a patient is at risk of perioperative complications. In one
embodiment user interface of the user device 12 can receive data or
information via a keyboard or a touch pad, for example.
Alternatively, a web-based application can be used to input the
patient data to a database of patient information.
[0033] The computer system can also include a processing unit 3002
having a predictive model. The predictive model can incorporate one
or more prediction equations for perioperative complications
derived from one or more databases 3005 having multiple patient
data relevant to predict perioperative complications. The databases
can be a compilation of databases, in the same or different
locations or a single database referred to as a population
database. In general the one or more databases 3005 can be one or
more existing databases from which relevant data to determine
perioperative complications are accessed. In one embodiment, the
system can include a patient database 3006 to store data received
at the user device 12. The processing unit 3002 can be a part of
the user device 12 or part of a separate computer system, or a
server. For example, prediction software that incorporates the
prediction equations can be operated from a portable, handheld or
wireless device, providing immediate updated information. In some
embodiments, the system includes an updating module 3003 to
incorporate the set of risk factors for the patient, including
surgical risk factors, and sleep study data of the patient into the
predictive model in order to optimize the predictive model. The
processing unit 3002 can assess changes in risk levels of
perioperative complications for a patient and target intervention
to reduce the frequency and severity of the perioperative
complications as is described herein. Routine additions to all
inputs and outputs of the predictive model will allow the accuracy
of the equations to be refined and improved. As the population
database is expanded separate rules may be developed to account for
special cases. The system can be coupled to an output device 3004,
for example, a printer or a monitor so that perioperative
complications reports generated by the system can be outputted. The
report can also be stored for offline analysis or utilized for real
time monitoring.
[0034] FIG. 4 is a block diagram illustrating aspects of an
exemplary stand-alone computer system which implements embodiments
of the invention. The system for predicting risk for perioperative
complications includes a user device 12 for receiving a set of risk
factors to determine perioperative complications for a patient
including patient data useful to determine the likelihood of
obstructive sleep apnea. An acquisition module 4001 can be
configured to receive data from an obstructive sleep apnea sleep
study of the patient. In one embodiment the sleep study data can be
conducted with the patient in various positions including the
supine position. Also sleep study data can be acquired
automatically and can be written to a sleep study database. In
addition a determination module 4002 can determine the severity of
obstructive sleep apnea for the patient. The determination module
4002 can score the data from the obstructive sleep apnea sleep
study to determine the obstructive sleep apnea severity. The system
can also include an analysis module 4003 having a predictive model
that incorporates one or more prediction equations for predicting
perioperative complications derived from one or more databases
having multiple patient data relevant to predict perioperative
complications, the analysis module 4003 configured to apply the one
or more prediction equations to the set of risk factors and the
severity of obstructive sleep apnea for the patient in order to
identify perioperative complication risks of that patient and to
generate a perioperative complications report for the patient. The
perioperative complications report may include recommendations to
reduce the frequency and severity of the perioperative
complications. The report generated can be outputted to an output
device 3004, for example, a printer or a screen. Some embodiments
include an updating module 3003 to incorporate the set of risk
factors for the patient, including surgical risk factors, and sleep
study data of the patient into the predictive model in order to
optimize the predictive model.
[0035] FIG. 5 is a block diagram illustrating aspects of an
exemplary computer network system which implements embodiments of
the invention. In this embodiment the system is implemented in a
network environment 4110 similar to the network environment
illustrated in FIG. 1. The network environment 4110 can accommodate
one or more users inputting patient data to the system. User
devices provide the means for acquiring patient information or
data. Each of the user devices 12 can communicate with a server 14
via a network 16. In one embodiment the user device 12 can include
a user interface for receiving data via, for example a keyboard. A
user can also communicate with the server 16 through the user
interface which can be associated with a computer system or a
process that requests service of another computer system or
process, for example, a handheld or wireless device. A user can
also communicate via, for example, one or more computers, a
computer application or software such as a web browser that runs on
a user's local computer or workstation and connects to a server as
necessary or a combination of both. The server 14 can be one or
more computers or devices on a network that manages network
resources. In one embodiment the system can be configured with a
database or data storage area 3005 that can be any sort of internal
or external memory device and may include both persistent and
volatile memories. The function of the database 3005 is to maintain
data in for long-term storage and also to provide efficient and
fast access to instructions for applications that are executed. The
database 3005 can store a patient's data. When the database 3005 is
used for the purpose of storing patient's data it can be referred
to as the patient's database. The database 3005 can also be one or
more databases some of which stores patient data and some of which
store multiple patient data relevant to predict perioperative
complications, for example a population database. In one embodiment
the patient database is coupled to the user device 12. In another
embodiment the population database is coupled to the server.
However the databases are not limited to a particular location. In
some embodiments, the server 14 includes an analysis module 4003
illustrated in FIG. 3 above. The analysis module 4003 can have a
predictive model that incorporates one or more prediction equations
for perioperative complications derived from one or more databases
having multiple patient data relevant to predict perioperative
complications, the analysis module configured to apply the one or
more prediction equations to the set of risk factors for the
patient to identify and report the likelihood of perioperative
complication risks of that patient. The report generated can be a
perioperative complications report. In another embodiment the
system can be accomplished in a single computer system independent
of a network.
[0036] After the sleep study data have been acquired, it is
preferable that the signals are analyzed using automated, validated
algorithms. That way the OSA severity can be immediately determined
if the data were acquired the night before surgery or obtained
routinely during hospitalization. Alternatively, the data could be
scored and the OSA severity determined by visually inspection or a
combination of automated scoring and visual inspection.
[0037] FIG. 6 is a flow diagram of an exemplary procedure for
prediction of risk for perioperative complications. The described
procedure or method can be implemented using the systems described
in connection with FIGS. 1, 3 and 4. In step 6001, the procedure
starts with inputting patient data to, e.g., a user device
illustrated in FIG. 3. This is the data described in connection
with FIG. 2 above. The procedure then continues to step 6002, where
data from one or more databases having multiple patient data
relevant to predict perioperative complications is processed. The
process can incorporate an analysis module, illustrated in FIG. 3
above, having a predictive model that uses one or more prediction
equations derived from the data in the one or more databases and by
applying the one or more prediction equations to the set of risk
factors in order to predict perioperative complications risks for a
given patient. In step 6003 risk levels of perioperative
complications are identified. This step can also assess changes in
risk levels of perioperative complications. The process then
continues to step 6004, where a perioperative complications report
is generated. The report can include recommended interventions to
reduce the frequency and severity of the perioperative
complications. In one embodiment the process can further include
updating the risk predictions when new data is acquired from
additional sleep studies obtained during the perioperative
period.
[0038] In some embodiments, apneas (the cessation of airflow for at
least 10 seconds), hypopneas (partial obstruction leading to oxygen
desaturation or arousal), and prolonged obstructive breathing or
hypoventilation events are differentiated and categorized. A
patient who suffers from a complete cessation of breathing during
sleep (apnea) as opposed to a partial obstruction will be at
greater risk for post-surgical hypoxemia, assuming all other risk
factors are held constant. Patients with prolonged obstructive
breathing that normally does not manifest as unique obstructive
breathing events (and are not normally counted in the AHI) may
become more severe in the perioperative period, thus increasing the
risk of complications. Patients who exhibit prolonged periods of
hypoventilation (increased respiratory effort, loud or increased
snoring, flow limitation and slow increase in desaturation) are
particularly susceptible to post-surgical hypoxia. These
distinctions can be incorporated into an algorithm at the
processing unit illustrated in FIG. 3 or the server illustrated in
FIG. 4 to streamline and improve the prediction system and method.
In one embodiment the change in airflow associated with an apnea,
hypopnea, obstruction or hypoventilation is based on flow
amplitude. In an alternative measure, the required changes in
airflow are identified by flow limitation. Alternatively,
clinically significant changes in airflow could be detected by
visual inspection.
[0039] In another embodiment, oxygen saturation is measured during
the sleep study, preferably with a pulse-oximeter (i.e., SpO2) so
that hypopneas are further stratified based on the depth of
desaturation. This information can be incorporated into the list of
factors describe in FIG. 2 and can also be used to update the
prediction equations that are processed in the processing unit or
server. A recent report linked the relationship between the body
mass index, lung volume and the ability to desaturate. See Sasse,
S. A., P. Westbrook, D. Levendowski, T. Zavora, R. Dalati, C.
Vincent, and M. Popovic, in the article entitled "Timing of Changes
in Oxyhemoglobin Saturation Resulting from Breath Holding"
published in Sleep Medicine, 2006. 7 (S2): p. S46 (30). By way of
example, an individual with a body mass index of 24 would
desaturate on average 4% after holding his/her breath for
30-seconds at functional residual capacity, while an individual
with a body mass index of 34 would desaturate approximately 10% in
the same length of time. A relatively slim patient or
pre-menopausal woman who desaturates less than 4% prior to surgery
may experience significantly greater desaturation levels from sleep
disordered breathing post-operatively as a result of a narcotics
and sedative. Recent report suggest that multiple hypopnea criteria
are very useful in predicting patients who will have a positive
outcome with mandibular advancement therapy or in predicting the
treatment pressure for fixed continuous positive airway pressure
(CPAP). See Westbrook P, Levendowski D, Morgan T, Patrickus J,
Berka, C, Olmstead R, Zavora T, Whitmoyer M, in "Predicting
Treatment Outcomes for Oral Appliance Therapy for Sleep Apnea using
Pre-treatment In-home Sleep Studies", 8.sup.th World Congress on
OSA and Westbrook P, Levendowski D, Henninger D, Nicholson D, Smith
J, Zavora T, Yau A, Whitmoyer M, in "Predicting Effective
Continuous Positive Airway Pressure (CPAP) based on Laboratory
Titration and Auto-titrating CPAP", 8.sup.th World Congress on OSA,
both of which herein incorporated by reference. Alternatively
simply counting the number of times or number of times per hour
that the patient exceeds predefined thresholds of oxygen
desaturation may be use to identifying the risk for perioperative
complications.
[0040] When presenting SpO2 at 0.1% increments it is optimal to
establish minimum desaturation and resaturation criteria to
eliminate the counting of artifacts in the apnea/hypopnea index
(AHI). For example, an AHI-4% hypopnea criteria can include a
minimum 3.5% reduction in SpO2 (i.e., 3.5% rounds up to a 4%
desaturation using integer presentations) and at least a 1.0%
recovery. Hypopneas included in the AHI-3% and AHI-1% criteria can
require SpO2 desaturation and resaturation using stepped
thresholds. For the AHI-3%, if the SpO2 at the point of maximum
saturation prior to the event was greater than or equal to 95% then
a 2.2% reduction and a 2.2% recovery in SpO2 was required. For
maximum saturations of between 95-93% the required SpO2 change is a
2.5% reduction and 2.5% recovery; between 93-91% a 3.0% reduction
and 2.7% recovery; between 91-88% a 3.5% reduction and 3.0%
recovery; and below 88% a 4.0% reduction and 3.5% recovery. For the
AHI-1%, if the point of maximum saturation prior to the event was
greater than 93% then a 1.0% reduction and 1.0% recovery is
required; and for events with a starting SpO2 between 93-91%, a
1.2% reduction and 1.2% recovery is required. For an AHI-1% or
AHI-0% (no desaturation) event to be called, it is beneficial to
include confirmation by behavioral (i.e., an abrupt change in pulse
rate, snoring sound or a head movement) or cortical arousals. One
skilled in the art, however, can establish alternative desaturation
thresholds or classification rules that utilize these elements in
different combinations.
[0041] When measuring the blood oxygenation, the percentage of time
SpO2 is less than 90% can identify patients with a greater
likelihood of complications. Another measure of predictive
hypoxemia is the average percentage drop in SpO2 as a result of
sleep-disordered breathing. Some researchers have hypothesized that
hypoxemia may result in increased sensitivity to opioid medication,
thus patients who are hypoxic during sleep may require a lower
dosage to achieve the same reduction in pain. The altitude at the
location of surgery can influence the baseline oxygen saturation as
well as the potential for the patient to experience increased
hypoxemia when the respiratory system is suppressed.
[0042] In one embodiment, the sleep study result from processing
data in the processing unit illustrated in FIG. 3 or the server
illustrated in FIG. 4 includes a measurement of OSA severity (i.e.,
AHI, obstructive index, % time hypoxic, etc.) in the supine
position. Because gravity influences the collapse of the pharynx
the severity of OSA can be greater when a patient is on his/her
back. After hip or knee surgery it's very common for patients to be
hospitalized on their back for extended periods. Thus, the risk of
post-surgical complications as a result of OSA is more directly
related to the severity when supine as compared to the overall OSA
severity in all positions.
[0043] The identification of patients with obstructive vs. central
sleep apnea is also important in identifying the risk for
post-surgical complications. A patient with obstructive breathing
will present as a more difficult case during surgery (i.e.,
intubation and extubation). A patient with central sleep apnea
typically stops breathing until a carbon dioxide in the lungs reach
a level that triggers the brain to resume respiration. Thus, a
patient with central sleep apnea would likely have having an
underlying cause for the respiratory instability that might not yet
be identified (i.e., congestive heart failure or stroke, intense
pain, etc.). When central sleep apnea is identified, first the
underlying cause should be identified because of its contribution
to perioperative complications along with administering of
supplemental O2 and monitored with pulse-oximetry to ensure the
patient does not become hypoxic. For central sleep apnea, treatment
by CPAP would not be appropriate or reduce the risk for
complications. If an anesthesiologist is aware of the central
apnea, he/she may choose to delay extubation in the recovery
room.
[0044] The differentiation of obstructive and central apneas
generally requires a combination of physiological signals can be
useful to improve the prediction equations in the processed in the
server of FIG. 4 or the processing unit of FIG. 3. The shape of the
airflow signal may be used to identify changes in ventilation that
are not obstructive (i.e., flattened inspiration). The most direct
and invasive measure of respiratory effort is obtained by placement
of an esophageal catheter via the nose or mouth but the most
frequently used method is to record thoracic cage expansion.
Typically bands are placed around the chest and abdomen, and the
change in circumference of these two compartments with breathing is
measured. Inductive plethysmography, piezo electric crystals,
conductive elastomere, and polyvinylidine fluoride film,
magnetometers and strain gages have all been used. Alternatively, a
previously described embodiment that utilizes a non-invasive
measure of forehead venous pressure by the mean of
photoplethesmography (PPG) and/or a forehead pressure transducer
can be used to measure respiratory effort (40). Finally, a
quantified measure of snoring level (in decibels) can be used as a
measure of respiratory effort and included as a means to
differentiate patients with obstructive vs. central sleep
disordered breathing. It is presumed that patients louder and
prolonged snoring will have a greater likelihood of perioperative
complications, consistent with the capability of quantified snoring
to assist in the prediction of successful treatment of OSA with a
mandibular advancement device. See Westbrook P, Levendowski D,
Morgan T, Patrickus J, Berka, C, Olmstead R, Zavora T, Whitmoyer M;
"Predicting Treatment Outcomes for Oral Appliance Therapy for Sleep
Apnea using Pre-treatment In-home Sleep Studies", 8.sup.th World
Congress on OSA.
[0045] In one embodiment, the sleep study data used to predict the
likelihood of perioperative complications is automatically written
to a database, for example the patient database illustrated in FIG.
3. Rather than monitoring the patient continuously, sleep data can
be gathered routinely or periodically to assess changes in OSA
severity and the percentage of the sleep study with REM and SWS (to
monitor for a rebound). When additional sleep studies are conducted
during the post-surgery period, the database can be updated and a
revised perioperative risk assessment computed. Alternatively, data
obtained from continuous pulse-oximetry monitoring (i.e., time
below 90% or standard deviation of the SpO2) or from a CPAP machine
when worn during the perioperative period (i.e., CPAP pressure and
AHI) can be used to update the risk of complications.
Outputs from the System:
[0046] The likelihood of perioperative complications can be
predicted for each of five risk groups: 1) during induction of
anaesthesia 2) during surgery, 3) in the recovery room, 4) while
the patient is on the medical/surgical ward, and 5) post-surgery
resulting in a significant change in morbidity and/or readmission
to the hospital. Complications likely to occur during surgery
include difficulty in intubation and extubation and hypoxemia.
Recovery room complications include persistent transient events
including but not limited to: a) hypoxemia (i.e., SpO2<90%) that
requires supplemental oxygen to stabilize; b) high or low blood
pressure; c) cardiac dysrhythmia which is new for the patient; d)
aspiration pneumonia (with or without hypoxemia); and/or e) post-op
atalectasis (hypoventilation and/or ventilation/profusion
mismatch). Complications that occur while the patient is on the
medical/surgical ward include: a) indications of shortness of
breath or chest pain; b) a post-surgical chest x-ray abnormality;
and/or c) an internist, pulmonologist, or cardiologist consultant
was requested. Post-surgical complications include strokes, change
in co-morbid conditions, or readmission for any reason within 30
days post-surgery. Likelihood ratios can be computed for each of
these types of complication given a large database of patient
data.
[0047] Other outputs from the system include data that can lead to
recommendations that can reduce the likelihood of perioperative
complications. The output from the system can be displayed or sent
to an output device similar to that illustrated in FIG. 3. For
example, initiation of treatment with CPAP is recommended for
bariatric patients approximately one month prior to surgery to help
to stabilize the patient's respiratory system and improve their
immune system. Thus, the number of hours per night and the number
of nights of CPAP use can interact to reduce the risk of
perioperative complications. So if the predictive model includes
updated information about CPAP time-on-pressure, the likelihood of
complications can be updated. Patients who are non-compliant (i.e.,
less than 4 hours/night) would be expected to have a greater risk
of complications than a compliant patient (all other factors held
constant). The impact of a change in the type, dose and
administration of a sedative or narcotic on perioperative risk can
be determined when compared to a large database of similar case
findings. The use of a computer- and data-based system as described
above will allow a physician or health-care provider to model
different conditions to assess the optimal perioperative plan.
[0048] In addition to predicting the risk of OSA from processing
data in the processing unit or server illustrated in FIGS. 3 and 4
respectively, those systems identify patients who require special
monitoring post operatively. For example, a patient who suffers
from hypoxia during sleep will have a greater likelihood of
becoming hypoxic perioperatively. Thus, a referral to the intensive
care unit for the recovery period and/or continuous monitoring by
pulse-oximetry can be recommended by the system for the
medical/surgical ward and/or during the patient's entire hospital
stay. The report generated by the system can also advise the
surgeon and hospital staff to carefully monitor the administration
of narcotics since hypoxic patients may have a more sensitive
dose-response curve. Patients with moderate or severe OSA can be
recommended for CPAP while on the medical/surgical floor or
immediately upon discharge. CPAP may also be recommended for
patients with severe positional OSA who will have limited
post-surgical positioning options. If the patient is identified
with central or mixed obstructive/central sleep apnea, or the
predicted CPAP pressure (38) is greater than 15, a bi-level CPAP
may be recommended rather than a fixed- or auto-pressure CPAP to
improve comfort and the likelihood of successful treatment.
[0049] The identification of moderate or severe supine-positional
OSA in a patient who will have a surgery that limits their ability
to be positioned laterally should result in the recommendation for
continuous pulse-oximetry monitoring. Alternatively, an extended
hospital stay can be recommended if continuous monitoring is
required but would unavailable after the patient is discharged.
[0050] All of the risk factors and risk predictions derived from
the systems or methods described above can be included in a report
to be placed in the patient's medical record and/or provided to the
patient. Thus, physicians and hospital administrators can notify
the patient, his/her family and the insurer, in advance, so all
stake-holders are aware of the perioperative risks and associated
complications and recommendations to reduce risk. Advanced
disclosure using an evidence-based process can be used to reduce
the hospital and surgeon's exposure to liability if complications
do occur and/or mitigate poor outcome scores that hospitals may
receive when they serve the sickest patients.
[0051] The mere existence of a pre-operative evaluation system
should be reassuring to the patient. The application of the system
and interventions based on the information provided will provide
evidence of care and interest by the surgical team in the patient's
welfare during a time when patient may feel exceedingly
vulnerable.
[0052] Those of skill in the art will appreciate that the various
illustrative modules and method steps described in connection with
the above described figures and the embodiments disclosed herein
can often be implemented as electronic hardware, software, firmware
or combinations of the foregoing. To clearly illustrate this
interchangeability of hardware and software, various illustrative
modules and method steps have been described above generally in
terms of their functionality. Whether such functionality is
implemented as hardware or software depends upon the particular
application and design constraints imposed on the overall system.
Skilled persons can implement the described functionality in
varying ways for each particular application, but such
implementation decisions should not be interpreted as causing a
departure from the scope of the invention. In addition, the
grouping of functions within a module or step is for ease of
description. Specific functions can be moved from one module or
step to another without departing from the invention.
[0053] Moreover, the various illustrative modules and method steps
described in connection with the embodiments disclosed herein can
be implemented or performed with a general purpose processor, a
digital signal processor ("DSP"), an application specific
integrated circuit ("ASIC"), field programmable gate array ("FPGA")
or other programmable logic device, discrete gate or transistor
logic, discrete hardware components, or any combination thereof
designed to perform the functions described herein. A
general-purpose processor can be a microprocessor, but in the
alternative, the processor can be any processor, controller,
microcontroller, or state machine. A processor can also be
implemented as a combination of computing devices, for example, a
combination of a DSP and a microprocessor, a plurality of
microprocessors, one or more microprocessors in conjunction with a
DSP core, or any other such configuration.
[0054] Additionally, the steps of a method or algorithm described
in connection with the embodiments disclosed herein can be embodied
directly in hardware, in a software module executed by a processor,
or in a combination of the two. A software module can reside in RAM
memory, flash memory, ROM memory, EPROM memory, EEPROM memory,
registers, hard disk, a removable disk, a CD-ROM, or any other form
of storage medium including a network storage medium. An exemplary
storage medium can be coupled to the processor such the processor
can read information from, and write information to, the storage
medium. In the alternative, the storage medium can be integral to
the processor. The processor and the storage medium can also reside
in an ASIC.
[0055] The above description of the disclosed embodiments is
provided to enable any person skilled in the art to make or use the
invention. Various modifications to these embodiments will be
readily apparent to those skilled in the art, and the generic
principles described herein can be applied to other embodiments
without departing from the spirit or scope of the invention. Thus,
it is to be understood that the description and drawings presented
herein represent exemplary embodiments of the invention and are
therefore representative of the subject matter which is broadly
contemplated by the present invention. It is further understood
that the scope of the present invention fully encompasses other
embodiments and that the scope of the present invention is
accordingly limited by nothing other than the appended claims.
* * * * *