Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
2000-05-11
2001-11-13
Powell, Mark R. (Department: 2122)
Data processing: artificial intelligence
Neural network
Learning task
C706S015000, C706S052000
Reexamination Certificate
active
06317731
ABSTRACT:
FIELD OF THE INVENTION
A method for facilitating the selection of a treatment regime and for monitoring the outcome of a particular treatment regime on a disease based upon the expected outcome is provided. The treatment is selected from a group of possible treatments based upon the pre-treatment diagnostic data where more than one treatment regime could be selected. The method finds utility, for example, in the treatment and monitoring of disease states wherein the symptoms of the disease can result from more than one physiological condition.
BACKGROUND OF THE INVENTION
While the method of the instant invention is useful for the treatment selection for more than one type of disorder which is diagnosed and treated based upon the symptoms, for simplicity, the treatment selection for a disorder wherein the diagnosis is made by a physician based upon somatic symptoms such as for example depression and especially unipolar depression, will be discussed therein.
Recent studies suggest that in the U.S. about 6-10% of the population exhibit varying symptoms of depression which costs society billions of dollars annually. Depression is an affective metal health disorder which is diagnosed based upon descriptive criteria or somatic symptoms which are set forth in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) (APA, 1994). The severity of the disorder is diagnosed using the Hamilton Depression Rating Scale (HDRS) (Hamilton, 1960) which is a clinical instrument devised by Hamilton which assesses the severity of the symptoms of the disorder. The instrument evaluates twenty-one psychological, physical, and performance deficits. Many different malfunctions may give rise to the same set of somatic symptoms and the physiological basis for these malfunctions is not thoroughly understood. Thus, it is difficult to determine the correct treatment regime for a patient.
In clinical research studies which are performed to assess the effect of a treatment, pre-treatment or baseline scores and post-treatment scores are typically compared. Several prior research efforts focused on the recovery pattern of depression symptoms. In 1984, Quitkin (Quitkin, F. M., et al.;
Arch General Psychiatry
(1984) 41: 782-786) analyzed the patterns of general improvement in depressed patients in response to treatment with drug therapy. He compared four antidepressant drug treatments with a placebo (N=318). The results showed that a “true drug response” was indicated by a pattern of delayed and persistent improvement. The delay was up to 4 weeks, but once improvement started it continued. These results were replicated by Quitkin et al. in 1987 (Quitkin, F. M., et. al.;
Arch. Gen. Psychiatry
(1987) 44: 259-264). They used a measurement of overall general improvement in the patient's condition (CGI: Clinical Global Impression scale).
Katz et al. (1987) (Katz, M., et.al.;
Psychological Medicine
(1987) 17: 297-309)found that specific changes in symptoms after one week of treatment were predictors of response to imipramine and amitriptyline treatments in bipolar and unipolar patients (N=104). As the symptom measure, they used “state constructs” which included HDRS as one of its measurements. According to their analysis (analysis of covariance), these measurements indicated week-one predictive symptoms to be a reduction in disturbed affects ((distressed expression and anxiety (p<0.001); depressed mood, hostility and agitation(p<0.01)); and cognitive functioning ((cognitive impairment(p<0.01)). Retardation drops only after these symptoms drop. Sleep disorder drops non-differentially from an early stage for responders and non-responders. These symptoms were the ones that dropped early and were predictive of the outcome. Sleep disorder dropped early too, but was not predictive of the outcome because it dropped both in responders and non-responders. Retardation dropped later in responders.
The advantages of time series analysis were illustrated by Hull et al. (Hull, J. W., et.al.;
Journal of Nervous and Mental Disease
(1993) 181: 48-53) in documenting the treatment effects of fluoxetine in a 58 week in-patient trial. The data analyzed were from a self-report symptom scale obtained for a single patient (N=1). Forty weeks of pre-treatment data were available for the analysis. The amount of data obtained was sufficient for time series (intervention analysis) of the time course of depression symptoms. The data before intervention was best fit by the model identified as (AR, I, MA)=(0, 1, 1). This is a first order moving average model that operates on the first degree differential of the time series data. Eight “dummy” variables corresponding to the intervention were then introduced. Each was a step function that changed from zero To one at week i after intervention (i=0, 1, . . . , 7). Most symptom scores dropped significantly during the second week. The most noticeable was depression (p<0.001). Some symptom scores showed additional drops by the fourth week. Psychoticism, characterized by delusions or hallucinations was an exception, in that is primary response occurred during the first week.
Recently, a method of diagnosing or confining a diagnosis of depression has been developed by Goldstein et. al. (U.S. Pat. No. 5,591,588; Goldstein et. al.; the disclosure of which is incorporated herein by reference). Based upon laboratory determined blood values of the neurohormone arginine vasopressin and on the thymic hormone thymopoietin taken from blood samples obtained in the afternoon from patients and using a logistic regression model which was confirmed using a linear discrimination analysis, this diagnostic criterion was found to be accurate in 81% of the patients who were diagnosed as depressed using Hamilton Depression Rating Scale.
The above described methods are useful for characterizing and diagnosing an affective disorder. However, assignment of a treatment based upon the diagnosis and characterization of the disorder is not achieved by these methods. Further, once a treatment is assigned to a patient based upon the currently used methods, no treatment specific recovery pattern is available to monitor the progress achieved by the patient at various time points of treatment in between pre- and post-treatment assessment.
The time resolution of the measurements is coarse. Data is collected weekly at best. Frequently data points are missing. Further, patient data gathered is rated on a five point scale and is qualitatively assessed. The population studied may not be representative of the entire range of the disorder; it may not be normally distributed in a statistical sense. In particular, the patient's progress is not compared with the pattern of recovery shown by patients who have received similar treatment regimes and who have been determined to be ‘recovered’ based on HDSR with respect to the time course of the disappearance of symptoms.
Several treatment regimes have proven effective in treatment depression when pre- and post-treatment are compared, but the response to the various treatments is highly variable. Within a group of patients all assessed to have the same HDSR, response to the same treatment is highly variable. Some people respond in the expected manner, while others do not. Further variability is added in that some patients response in the same manner to different treatments. These treatments include psychotherapy such as for example cognitive behavioral therapy (CBT) and/or drug treatment, such as for example with a tricyclic anti-depressant drug (TCA) such as for example despiramine (DMI) or such as for example with a selective serotonin reuptake inhibitor, such as for example, fluoxetine (FLU). Each treatment has proven successful with a certain subset of patients exhibiting somatic symptoms of depression derived from the Hamilton Depression Rating Scale. However, identification of members of a subset prior to the onset of treatment is difficult. Thus, optimal treatment selection is difficult for any given individual.
Currently, o
Day Sharon L
Khatri Anil
Powell Mark R.
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