Surgery – Diagnostic testing
Reexamination Certificate
1998-11-19
2001-02-27
O'Connor, Cary (Department: 3736)
Surgery
Diagnostic testing
C600S301000, C600S481000
Reexamination Certificate
active
06193654
ABSTRACT:
BACKGROUNDS OF THE INVENTION
Monitoring and treatment of premature infants or critically ill newborns is complicated and expensive. The efficacy of the monitoring system, among other things, depends on which parameters are being monitored, and how many of the measurements can be made automatic without human intervention, without sacrificing system reliability.
Several approaches to measurement of illness severity have been known to be used hitherto, and those have varying degrees of cost and reliability. Most known approaches have been devised, however, without specific emphasis on the patient to be monitored being a neonatal intensive care unit (NICU) patient.
DESCRIPTION OF PRIOR ART
Nearly all IC (intensive care) illness severity scores based on physiologic derangements have been directly or indirectly derived from the Acute Physiology and Chronic Health Evaluation (APACHE) score by Knaus et al. (see Knaus et al. “APACHE—acute physiologically based classification system” Crit Care Med, 1981;9(8):51-597). He reasoned that derangements from physiologic norm are a measure of illness, and the more severe the derangements, the more severe the illness. He selected and weighted 34 vital signs and laboratory results routinely available in the first 24 hours of admission to form the APACHE. He showed that higher scores correlated with death, morbidity, and resource use (see Knaus et al. “APACHE—acute physiologically based classification system” Crit Care Med, 1981;9(8):591-597). The advantage generally of such physiology-based measures is that they are objective, reliable, and credible. APACHE was simplified to APACHE-II (see Knaus et al. “APACHE II: A severity of disease classification system” Crit Care Med, 1985; 13(10):818-829), and was copied and simplified into the Simplified Acute Physiology Score (SAPS) (see Le Gall et al. “A simplified acute physiology score for ICU patients” Crit Care Med, 1984;12:975-7). With increasing sophistication, each of these was revised into APACHE-III (see Knaus et al. “The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults” Chest 1991;100:1619-36) and SAPS-II respectively (see Le Gall et al. “A new simplified Acute Physiology Score {SAPS II} based on a European/North American multicenter study” JAMA 1993;270:2957-63).
In pediatric intensive care, APACHE was modified to create the Physiologic Stability Index (PSI)(see Yeh et al. “Validation of a physiologic stability index for use in critically ill infants and children” Pediatr Res 1984; 18:445-451), which was then simplified to create the Pediatric Risk of Mortality Score (PRISM) (see Pollack et al. “Pediatric Risk of Mortality {PRISM} score” Crit Care Med, 1988; 16:1110-1116), and later the PRISM-III (see Pollack et al. “PRISM III: an updated Pediatric Risk of Mortality score” Crit Care Med, 1996; 24:743-52) and PRISM-III APS (for “Acute Physiology Score”) (see Pollack et al. “The Pediatric Risk of Mortality III—Acute Physiology Score {PRISM III-APS}: a method of assessing physiologic instability for pediatric intensive care unit patients” J Pediatr 19971 131:575-81).
Prior Art: Illness Severity Scores for Newborns
The importance of measuring illness severity became clear in neonatal intensive care which prompted similar score development. In 1993, Richardson, et al. used the APACHE concepts but all new variables in creating and validating the Score for Neonatal Acute Physiology (SNAP) (see Richardson et al. “Score for Neonatal Acute Physiology {SNAP}: Validation of a new physiology-based severity of illness index” Pediatrics 1993; 91:617-623). William Tarnow-Mordi, et al. used the PRISM concept and all new variables in creating and validating the Clinical Risk Index for Babies (CRIB) in 1993 (see International Neonatal Network “The CRIB {clinical risk index for babies score}—a tool for assessing initial neonatal risks and comparing performance of neonatal intensive care units” Lancet 1993; 342:193-198). SNAP is a 34-item physiology-based score measuring severity of illness, applicable to all newborn intensive care unit (NICU) admissions.
Based on the same concepts published for APACHE and PRISM, Dr. William Tarnow-Mordi derived and validated the CRIB (Lancet 1993:342;193-198) for very low birth weight infants (<1500 grams) treated in NICUs in Great Britain. CRIB uses only three physiologic variables, derived from routine vital signs and laboratory values, along with three standard markers for newborn risk, i.e., birth weight, gestational age and the presence of a life-threatening congenital anomaly. CRIB is in widespread use for research in Europe. There are, however, several important shortcomings of CRIB:
a. Validated only for very premature infants (<1500 gm): While this is valuable for research purposes, it is unacceptably restrictive for hospital and ICU managers who need to assess performance of all admissions, not just a special subset. The present invention, in contrast, is validated for all birth weights.
b. Questionable performance for outborn babies: The CRIB makes assumptions about incomplete records that are untenable for infants under emergency transport conditions. The present invention begins scoring only after the infant enters the ICU, thereby avoiding measurement assumptions.
c. Admission-only score, no sequential scoring: CRIB is designed to reflect severity only in the first day of life. Half of its components are fixed at birth (birth weight, gestational age, presence of anomalies). The present invention has a much broader dynamic range, designed to measure changing condition over time. All of the adult and pediatric scoring systems have this broader dynamic range characteristic.
d. Single organ-system: CRIB's physiologic items sample only the respiratory system. This may be adequate in a homogeneous population of very premature infants all of whom will have degrees of respiratory failure as their illness on admission. For full term infants, a much wider variety of organ-system failures requires a broader sample of organ-system items. The present invention samples several organ systems.
The “Berlin” score is a recently reported German score (Maier RF, Arch Dis Child 1997; 76:F146-F151) and is more of an epidemiologic adjustment tool than a true neonatal illness severity score. It too is an admission-only score and applies only to very premature babies (<1500 grams).
There are several other important adult ICU scores that require mention, because they have used parallel techniques to construct the scoring systems.
Mortality Prediction Model (MPM): A series of mathematically sophisticated adult ICU risk order was developed by Teres and Lemeshow (Care Med 1987; 15:208-213). A brief attempt at commercializing these appeared to be unsuccessful.
SAPS I and SAPS II: The unwieldiness of the original APACHE led to an independent revision by LeGall et al. (Le Gall JR: Crit Care Med 1984; 12:975-7) into the Simplified Acute Physiology Score (SAPS), creating a direct competitor to the concurrently derived APACHE II. Later, with the collaboration of the inventors of the MPM, the SAPS inventors revised and simplified their score to create SAPS II (LeGall JR: JAMA 1993; 270:2957-63). The SAPS has been developed in France and used widely throughout Europe.
A need still exists in healthcare to provide a system and method which obviates the shortcomings and disadvantages of known systems and methodology for determining severity of illness of patients, in particular, neonatal ICU patients from the moment of admission.
SUMMARY OF THE INVENTION
It is an object of the invention to provide a computerized method and system for determining severity of illness of a neonatal patient, using a computer method and process, and other measurement hardware. The invention, in its broad form, resides in a computerized method of making an on-line determination of illness-severity of a neonatal patient in a predetermined time span, by using a software program and optimal weighted measuremen
Escobar Gabriel J.
Lee Shoo
Richardson Douglas K.
Beth Israel Deaconess Medical Center
Hamilton Brook Smith & Reynolds P.C.
O'Connor Cary
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