1 clinical investigation and outcomes research analysis of physiologic and pharmacologic data marcia...
TRANSCRIPT
1
Clinical Investigation and Outcomes Research
Analysis of Physiologic and Pharmacologic Data
Marcia A. Testa, MPH, PhD
Department of Biostatistics
Harvard School of Public Health
2
Objective of Presentation
• Introduce analytical methods for the special case where biomedical data are collected during a session which contains:– repeated observations over time – numerous, frequently sampled data points– measures collected over a relatively short
interval of time (several hours or days) within one session
– commonly, more measures per session per subject, than subjects overall
3
Intensively Sampled Data
• Data collected during a physiology, monitoring or pharmacologic study over several hours or days with measurement every 1, 5, 10, 15, 30 or 60 minutes, or as a continuous function
• Each session may be repeated at weekly or monthly intervals to investigate the effects of interventions as part of clinical trials or treatment assessment, and to correlate session summary parameters with clinical events, morbidity and mortality
• In physiologic research, these data are often referred to as “complex physiologic signals”
4
Why Study Signals?
Physiologic signals and time series reveal aspects of health, disease, biotoxicity and aging not captured by static measures.
Raw (original) signals are of interest as means of
developing new biomarkers measuring parameters of known interest developing new insights into basic mechanisms of human physiology
ECG BP
Time = 2 seconds
5
Periodic Functions
Time (minutes)
Ph
ysio
log
ic R
esp
on
se
Response may represent a periodic function such as this graph of interstride intervals for a patient with Huntington’s disease, or a smooth function in response to a stimulus such as oral drug administration.
6
0
2
4
6
8
10
12
14
0 5 10 15 20
TIME (hours)
Pla
sma
con
cen
trat
ion Ka/Ke=10
Ka/Ke=0.1
Ka/Ke=0.01
Ka/Ke=1
Smooth Functions
Ka = Absorption Constant
Ke = Elimination Constant
Oral Drug
7
Intensive Data: Cardiology Studies
• Continuous recording: ECG is recorded continuously during the entire testing period.
• Event monitor, or loop recording: ECG is recorded only when the patient starts the recording, when symptoms are felt.
8
Physiologic time series, such as this series of cardiac interbeat (RR) intervals measured over 24 hours, can capture some of the information lost in summary statistics.
Data from the NHLBI Cardiac Arrhythmia Suppression Trial (CAST) RR Interval Sub-study Database
A Complex Signal Dataset
9
Example 1: Heart Rate Dynamics
Pathology can affect physiologic recordings in unexpected and interesting ways.
Analysis of complex signals can extract information hidden in data. Figure shows shows the instantaneous heart rates of four subjects. The plot of heart rate (beats/min) versus time (min) is called a tachogram.
Of the four tachograms shown, only one signal is from a healthy person. Can you tell which it is?
10
In A and C we can see a rather periodic signal, with low variability of its values. In case C, there is a pattern of periodic oscillations (1/min), which is associated with Cheyne-Stokes breathing.
The healthy record B is characterized by a rather rough and ‘patchy’ configuration, attributed to fractal properties of the heart rate signal.
The breakdown of such behavior (fractal dynamics) can lead to either excessive regularity (A &C) or uncorrelated randomness (D).
Excessive regularity
Excessive regularity
Uncorrelated Randomness
Healthy heart rate
11
Example 2: Ambulatory ECG
Schedule of study events is shown in panel A.
Panel B shows in-hospital activity schedules on the two activity days. AEM indicates ambulatory ECG monitoring.
Vertical arrows represent timing of venous sampling.
A
B
12
Example 2: Rates of Ambulatory Ischemia – Bar Graphs and Polynomial Regression
Parker JD, Testa MA, Jimenez AH, Tofler GH, Muller JE, Parker JO and Stone PH. Morning increase in ambulatory ischemia in patients with stable coronary artery disease: Importance of physical activity and increased cardiac demand. Circulation 1994;89:604-614.
Regular Activity Day
13
Example 2: Rates of Ambulatory Ischemia – Bar Graphs and Polynomial Regression
Parker JD, Testa MA, Jimenez AH, Tofler GH, Muller JE, Parker JO and Stone PH. Morning increase in ambulatory ischemia in patients with stable coronary artery disease: Importance of physical activity and increased cardiac demand. Circulation 1994;89:604-614.
Delayed Activity Day
14
Example 2: Ambulatory ECG
Bar graphs show frequency of episodes of ambulatory ischemia during therapy with placebo and nadolol on the two activity days. Panel A, Regular activity day;panel B, delayed activity day.
A
B
Regular Activity Day
Delayed Activity Day
15Parker JD, Testa MA, Jimenez AH, Tofler GH, Muller JE, Parker JO and Stone PH. Morning increase in
ambulatory ischemia in patients with stable coronary artery disease: Importance of physical activity and increased cardiac demand. Circulation 1994;89:604-614.
Example 2: Minute by Minute Heart Rate
Placebo
Nadolol
16
Example 2: Minute by Minute Heart Rate
Parker JD, Testa MA, Jimenez AH, Tofler GH, Muller JE, Parker JO and Stone PH. Morning increase in ambulatory ischemia in patients with stable coronary artery disease: Importance of physical activity and increased cardiac demand. Circulation 1994;89:604-614.
Placebo
Nadolol
17
Example 3: Continuous Glucose Monitoring in Diabetes
Continuing Glucose Monitoring Systems
Each colored line represents 5-minute glucose samples for a different day of the week.
18
Intensively sampled data can arise from many sources during the same clinical study
Continuing Glucose Monitoring Systems
E-DiaryGlucose Meter
19
Example 4: Pharmacokinetics
• Pharmacokinetics provides good general framework for the family of models which involves extracting parameters representative of biological processes– Drug absorption, distribution, metabolism and
excretion– Intensity and duration of therapeutic and toxic
effects of many drugs are closely related to their biological availability and disposition
20
Example 4: Plasma Concentration of Drug after Oral Administration
Time in Hours
403020100-10
Pla
sma
Con
cent
ratio
n5
4
3
2
1
0
-1
Absorption phase
Elimination phase
21
Steps in Analysis1. Collect raw signal data (e.g., heart rate, glucose,
plasma concentration) and transfer to relational database for estimation of parameters
2. Estimate signal parameters (e.g., heart rate variability, glucose variability, pharmacokinetic rate constants) using analytical programs
3. Use estimated parameters as dependent measures for prediction of health outcome or mortality (Exposed vs Unexposed), or determine how treatment (e.g., beta blocker) changes signal and how that change impacts health outcome, clinical event or mortality (Experimental vs. Control)
22
Disease Authors Study Methods Clinical Population Findings
Hypertension
Guzzetti 1991
Langewitz 1994
49 with hypertension
versus 30 controls34 with
hypertension vs 54 controls
Autoregressive modeling (AR)
Fast Fourier
transformation (FFT)
LF in hypertension, HF component and loss of circadian variation (both studies)
Heart failureNYHA III &IV
Saul 1998
Biknley 1991
Townend 1992
25 with heart failure vs 21
controls10 with heart failure vs 10
controls12 with heart
failure
Statistical methods
4 minutes FFTFFT and
statistical methods
Low HRV HF ( 0,1 Hz)
LF/HF↑ HRV with treatment with
inhibitors of converting activation enzyme (ACEs)
CardiomyopathiesCounihan
1993104 patients with myo-cardiopathy
FFT and statistical methods
HF ( 0,1 Hz)
Sudden death-heart attack
Algra 1993
Huikuri 1992
193 survivors vs 230 controls
22 survivors vs 22 controls
Statistical methods in 24
recordingsAutoregressive modelling in 24
hour Holter
↓HRV induces ↑in mortality by a factor of 2.6↓ HF in survivors
Ventricular arrhythmias Huikuri 199318 patients with
ventricular fibrillation
Autoregressive modelling in 24
hour Holter recordings
↓ of all HRV components before the arrhythmic episode
23
Estimating HRV Parameters
Adapted from Goldberger AL. Fractals dynamics in phy-siology: Alterations with disease and aging. PNAS 2002; 99: 2466-2472, downloaded from www.physionet.org.
Hear Rate Variability (HRV)
24
HRV: Time-Domain Methods
• Based upon beat-to-beat or RR intervals– SDRR: standard deviation (SD) of RR
intervals over 24 hours– SDARR: SD of average RR intervals
calculated over short periods ( 5 mins)– RR50: number of pairs of successive RRs
that differ by more than 50 minutes.
25
HRV: Frequency-Domain Methods
• Fast Fourier transform• High Frequency band (HF) between 0.15
and 0.4 Hz. HF is driven by respiration and appears to derive mainly from vagal activity (parasympathetic nervous system).
• Low Frequency band (LF) between 0.04 and 0.15 Hz. LF derives from both parasympathetic and sympathetic activity and has been hypothesized to reflect the delay in the baroreceptor loop.
26
HRV: Frequency-Domain Parameters• Fast Fourier transform• Very Low Frequency band (VLF) band between
0.0033 and 0.04 Hz. The origin of VLF is not well known.
• Ultra Low Frequency (ULF) band between 0 and 0.0033 Hz. The major background of ULF is day–night variation and therefore is only expressed in 24-hour recordings.
• The ratio of low-to-high frequency spectra power(LF/HF) has been proposed as an index of sympathetic to parasympathetic balance of heart rate fluctuation, but this is controversial because of the lack of understanding of the mechanisms for the LF component.
27
HRV: Non-linear Methods
• Poincaré plot. Each data point represents a pair successive beats, the x-axis is the current RR interval, while the y-axis is the previous RR interval.
• HRV is quantified by fitting mathematically defined geometric shapes to the data.
• Other methods used are the correlation dimension, nonlinear predictability, point wise correlation dimension and approximate entropy.
28
The abscissa represents the RR interval of the current normal beat and ordinate represents the RR interval of the succeeding normal beat.
An ellipse is fitted to the data points and the Poincaré plot indices are calculated by estimating the short diameter (SD1), the long diameter (SD2) and the ratio of the short and long diameters (SD1/SD2 ratio) of the fitted ellipse
Poincaré plot
29
Pharmacokinetic Processes
• Liberation – the release of the drug from its dosage form
• Absorption – the movement of drug from the site of administration to the blood circulation
• Distribution – the process by which the drug diffuses or is transferred from intravascular space to extravascular space (body tissues)
• Metabolism – the chemical conversion of drugs into compounds that cab be eliminated
• Excretion – the elimination of unchanged drug or metabolite from the body via renal, biliary, or pulmonary processes.
30
Elimination Constant
First order elimination, rate is proportional to concentration. The elimination rate constant Kel represents the portion of the drug eliminated per unit time.
31
Elimination Constant
The slope of the line of the concentration plotted on the log scale correlates with Kel.
Kel = ln(Peak/Trough)/time (P-T))
(Log scale)
32
First Order Process
dC
dt
T = 0, C = 100
SIDE A SIDE B
COMP 1 COMP 2
L(2, 1)
Loss from 1 to 2 is proportional to C
First order rate constant
33
Calculation of Parameters
34
How do you estimate parameters?
There are several software packages that can be used to estimate parameters – such as those from
www.adinstruments.com as shown here.
35
How do I estimate parameters?
There are several software packages that can be used to estimate parameter – such as those from
www.adinstruments.com as shown here.
36
Pharmacokinetic
Analysis
Software
Several different packages may be used.
e.g.,(shown)
http://www.summitpk.com/
38
What is Physionet?
• NIH-sponsored Research (Harvard, BU, McGill) established in 1999
• Freely available physiologic data and open-source software
• PhysioBank: 4000 recordings of digitized physiologic signals and time series, over 40 databases
• PhysioToolkit: Open source software
39
Physionet Tutorials and Datahttp://www.physionet.org/tutorials/hrv/
40
Continuous Glucose Monitoring (CMG)
41
Continuous Glucose Monitoring(CGM)
42
Data for Sample Patient – 4 Days
Is the “mean” the best way to summarize these data?
43
Data for Sample Patient – Session Week 12-- there are many parameters that could be estimated for each subject
44
Summarize the Raw Data
• The individual daily curves should be summarized to obtain signal parameters meaningful to the research objectives
• Examples – Mean, Max, Minimum for each day– Percent > 180 mg/dl (hyperglycemia)– Percent < 36 mg/dl (severe hypoglycemia)– Intraday standard deviation (glucose variability)– Area above and below defined thresholds
45
Simple Numeric Transformations/BREAK=Patient_ID by CGMS_num by Date by Nocturnal /Sensor_Glucose = NU(Sensor_Glucose) /Sensor_1 = MEAN(Sensor_Glucose) /Sensor_2 = MEDIAN(Sensor_Glucose) /Sensor_3 = SD(Sensor_Glucose) /Sensor_4 = MIN(Sensor_Glucose) /Sensor_5 = MAX(Sensor_Glucose) /Sensor_6 = PGT(Sensor_Glucose 140) /Sensor_7 = PLT(Sensor_Glucose 70) /Sensor_8 = PGT(Sensor_Glucose 180) /Sensor_9 = PLT(Sensor_Glucose 60)/Sensor_10 = PLT(Sensor_Glucose 50)/Sensor_11 = PGT(Sensor_Glucose 300)/Sensor_12 = MEAN(Sens_gluHI)/Sensor_13= MEAN(Sens_gluLO)/Sensor_14 = SD(Sens_gluHI)/Sensor_15= SD(Sens_gluLO)
Data Reduction from 1000’s to only 15 measures per subject – all representing a different parameter of the CGMS profile curve
Code Shown – using functions from a common statistics package or Excel.
46
More Sophisticated Modeling Techniques:Fourier Series
The theory of Fourier series lies in the idea that most signals, can be represented as a sum of sine waves
Start with a sine wave:
Build a model using Fourier Series
47
CGM Daily Measures
• Mean Glucose (24-hour, day-time, nocturnal) • Mean Glucose Standard Deviation• Mean amplitude glucose excursions (MAGE)• Low blood glucose index (LBGI)• High blood glucose index (HBGI)• AUC of BG < 70 mg/dL (3.9 mmol/L) and < 50
mg/dL (2.8 mmol/L)• Nocturnal hypoglycemia – measures < 36,
50, or 70 mg/dL during late night and early morning (sleep time)
48
CGM Post-Prandial Measures
• Meal Interval Start Glucose• Meal Interval Start Time• Pre-Meal Insulin Dose• Meal Type • Glucose (C0 (mg/dl), Time (0) • Glucose Cmax (mg/dl), Glucose Tmax (min),
Glucose (Cmax - C0), Glucose (Tmax - T0), • Glucose Cmin (mg/dl - trough) • Glucose Tmin (min) • Glucose (Cmax – Cmin )• Glucose Upstroke (Appearance Rate)• Glucose Downstroke ( Elimination Rate)
Some summary parameters may be in response to meals.
49
100000.0 0 XYZ 20-OCT-2009 185.33100000.0 12 XYZ 15-JAN-2010 133.63100000.0 24 XYZ 06-APR-20`0 133.90
Data for Sample Patient
• The patient had three sessions of continuous glucose monitoring with each session lasting several days.
• Below are the overall mean glucoses for each of the sessions
Case Week Initials Mid Interval Date Glucose
50
Graph of Mean Glucose at Weeks 0, 12 and 24 for Patient 100000
1 2 3
CGMS-Session
120
130
140
150
160
170
180
190
Me
an
of S
en
so
r_
Glu
co
se
0 12 24
Weeks
51
15 patient feasibility study
Each patient is measured during 3 session (Week 0, 12 and 24). Each session lasts r 3 – 5 days with measures taken every 5 minutes yielding a maximum of 288 values per day.
Clinic 1 ID 200000’s
Clinic 2 ID 400000’s
What is the mean glucose, glucose variability and hyper and hypoglycemia parameters for the subjects at Week 12?
There are a total of 13,050 glucose measures for 15 patients.
Number of Glucose Values
52
15 patient feasibility study
The 13,050 glucose measures for these 15 patients are reduced to 4 summary parameters for each patient -- yielding 60 summary parameters in total for the 15 patients.
Summary Parameters
1. Mean Glucose
2. Glucose Variability (SD Glucose)
3. Percent values > 140 mg/dL
(hyperglycemia)
4. Percent values < 70 mg/dL
(hypoglycemia)
53
Glycemia CGM Parameter Estimates at Week 12
Here we summarize the parameters for the 15 subjects.
In the next session we will learn how to construct confidence intervals and develop different hypotheses for these measures.
54
Summary
• Identified the types of clinical research studies requiring analytical methods for complex data signals and parameter estimation
• Reviewed various analytical techniques and software packages for obtaining clinical physiology and pharmacologic methods
• Introduced examples in cardiology (HRV) and CGM (diabetes) where such techniques are useful