ASYMS

-SERAT: A SIDE-EFFECT RISK ASSESSMENT TOOL

TO PREDICT CHEMOTHERAPY RELATED TOXICITY IN

PATIENTS WITH CANCER RECEIVING CHEMOTHERAPY

Julie Cowie, Kevin Swingler, Clare Leadbetter

Department of Computing Science and Mathematics, University of Stirling, Stirling, FK9 4LA, UK

Roma Maguire, Kathryn McCall, Nora Kearney

Cancer Care Research Centre, Department of Nursing and Midwifery, University of Stirling, Scion House

Stirling Innovation Park, Stirling, FK9 4NF, UK

Keywords: Risk modelling, side-effect prediction, cancer chemotherapy.

Abstract: Patients undergoing chemotherapy want specific information on potential toxicities of their treatment. Such

information includes what side-effects they are likely to experience, how severe these side-effects will be,

how long they will experience them for, and the best ways of managing them. As well as improving the

experiences of patients, information about potential side-effects may also be of significant benefit clinically,

as patients who are ‘at risk’ of developing certain toxicities may be identified, facilitating more targeted,

cost-effective interventions. This paper describes research that uses risk-modelling techniques for

identifying patterns in patient side-effect data to aid in predicting side-effects patients are likely to

experience. Through analysis of patient data, a patient can receive information specific to the symptoms

they are likely to experience. A user-friendly software tool ASyMS©-SERAT (Advanced Symptom

Management System-Side-Effect Risk Assessment Tool) has been developed, which presents side-effect

information to the patients both at the start of treatment and reviews and monitors predictions with each new

cycle of chemotherapy received.

1 INTRODUCTION

In this paper we discuss the development of a user-

friendly software tool ASyMS

©-SERAT (Advanced

Symptom Management System-Side-Effect Risk

Assessment Tool), designed to provide patients

undergoing chemotherapy treatment with a

personalised prediction of possible side-effects they

are likely to experience. Prediction of possible

toxicities is achieved through the use of risk

modelling techniques, which facilitate a better

understanding of how a patient’s personal

information, the chemotherapy regime they are

undertaking, and any previously experienced

symptoms (if appropriate) contribute to the

likelihood of future symptoms occurring.

Section 2 provides the reader with an overview

of the project; detailing the current state of symptom

modelling, risk modelling and how the approach can

be applied to prediction modelling. The

methodology adopted is also discussed as well as the

actual aims of our research. In Section 3 we present

the software ASyMS

©-SERAT, discuss possible

ways in which the system might be used and show

the type of prediction information which can be

provided. We conclude with Section 4, discussing

work-to-date and ways in which we hope to develop

ASyMS

©-SERAT in the future.

2 PROJECT BACKGROUND

2.1 Symptom Modelling

In the UK approximately 277,000 individuals are

diagnosed with cancer each year (CRUK, 2003) and

this figure is projected to significantly increase over

the next decade (SEHD, 2005). A majority of these

225

Cowie J., Swingler K., Leadbetter C., Maguire R., McCall K. and Kearney N. (2008).

ASYMS-SERAT: A SIDE-EFFECT RISK ASSESSMENT TOOL TO PREDICT CHEMOTHERAPY RELATED TOXICITY IN PATIENTS WITH CANCER

RECEIVING CHEMOTHERAPY.

In Proceedings of the First International Conference on Health Informatics, pages 225-230

 SciTePress

individuals are likely to receive chemotherapy

treatment at some stage of their illness. The toxic

effects of chemotherapy puts patients at risk of

developing a number of side-effects, some of which

can become serious and life threatening if not

detected and managed early. Approximately 9-21%

of patients receiving chemotherapy are hospitalised

due to such severe treatment related toxicity (Chen-

Hardee et al. 2006; Du, Osborne, & Goodwin

2002;Kuderer et al. 2006; Polednak 2004) and 10%

of patients die as a result of them (Kuderer, Dale,

Crawford, Cosler, & Lyman 2006).

The effective monitoring and management of

symptoms in this patient group is vital. However, it

is now recognised that symptoms in patients with

cancer are often poorly assessed and managed

(National Institute for Health, 2002). Factors such as

inadequate patient provider communication

(Cleeland et al, 1986), and poor symptom

assessment (Cleeland et al, 1994) have been cited as

being contributory factors. The recent changes to

the organisation of cancer services may also

contribute to the sub optimal management of

symptoms. With the focus of care now being in the

home and out-patient setting, patients are left to

manage the majority of side-effects on their own

without direct supervision from health care

professionals; this may leave them feeling anxious

and having lack of control over their illness and

treatment (McCaughan & Thomson, 2000).

Furthermore, patients with cancer often find the

unpredictability and diversity of potential side

effects difficult to deal with (Cohn 1982; Tierney,

Taylor, & Closs 1992).

Patient education is therefore fundamental to

effective symptom control. It is widely

acknowledged that patients with cancer want

information on how to manage the symptoms and

side effects associated with their disease and

treatment (McCaughan & Thompson 2000; Skalla,

Bakitas, Furstenberg, Ahles, & Henderson 2004).

However, they often report feeling overloaded with

the wealth of information provided and as a result

experience problems with retaining and retrieving it

(Skalla et al. 2004). As a consequence, there have

been calls for the provision of information on cancer

therapies, which is tailored to patients’ individual

characteristics and needs (Skalla et al.

2004; Dikken

& Sitzia 1998). Patients want more specific

information on potential toxicities of treatment, such

as what side-effects they are likely to experience,

their severity, and duration and how to manage them

(Skalla et al.

2004). The provision of such

information is likely to make them feel more in

control of their disease by knowing what to expect

and how to deal with problems when they occur.

Furthermore, it may prevent unnecessary worry and

anxiety over side-effects that are less likely to arise

(Skalla et al.

2004). Whilst not only having the

potential to greatly improve the experiences of

patients with cancer receiving chemotherapy, a more

accurate prediction of potential side effects of such

treatments, based on patients individual and disease

related characteristics, may also be of significant

benefit clinically. By knowing the likelihood of

potential side effects occurring, patients who are ‘at

risk’ of developing certain toxicities may be

identified, facilitating more targeted and cost

effective interventions, to those in greatest need and

who are most likely to benefit. It may also guide

clinicians in the selection of appropriate treatments

for individual patients based on their characteristics

and needs.

2.2 Risk Modelling

Within health care, there is increasing use of

predictive models to identify patients who are most

likely to experience specific disease and/or treatment

related events. Relative to cancer care, such models

have tended to focus on predictors of survival and

life threatening toxicities such a febrile neutropenia

(Chow, Harris, & Fung 2006; Donohue 2006;

Lyman et al. 2005; Sanchez et al. 2006; Vigano et al.

2000). In relation to the prediction of symptoms,

there is limited work that has been performed in this

area, particularly in relation to the area of

chemotherapy side-effects (Armer et al. 2003;

Talcott et al. 2003; Poleshuck et al. 2006).

Risk modelling provides a powerful mechanism

for identifying patterns in data and predicting what

will happen in the future. A variety of techniques

can be employed to analyse data and the results of

such analysis can be used to provide likelihood

information relating to the prevalence of similar data

occurring in the future. This information can relate

to the likelihood of specific data values occurring

together, or perhaps the frequency with which whole

records of information may occur again.

The potential for using mathematical techniques

to identify risk is reinforced by their prevalence in

the literature: Cowie et al (2006) discussed the use

of Bayesian belief networks in aiding in dementia

diagnosis, where patterns are identified in data from

patients who are potentially dementia sufferers and

such patterns used to help predict whether dementia

is present; Werner and Fogarty (2001) developed

mathematical models to allow simulation of future

events based on past medical records. Using this

technique, the occurrence of thrombosis was

predicted in sufferers of collagen disease; De Toro et

al (

2003) used neural networks to predict hospital

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mortality of patients in intensive care more

accurately than traditional regression models;

Dybowski et al (1996) successfully applied multi-

objective optimisation to analyse electrocardiogram

(ECG) traces to provide a non-invasive technique for

diagnosing potential signs of atrial disease; García-

Pérez et al (1998) use data mining and neural

network techniques and Mani et al (1997) apply

decision-trees and rule-based approaches to

differentiate between different dementias types.

The risk modelling techniques available differ in

the way in which the data is analysed, how much

data the technique requires to make significant

predictions, and how much information is revealed

regarding the patterns that exist. In general, it is

advisable to use a variety of different methods to

ensure that as much prediction data can be obtained

as possible.

2.3 Project Aims

In order to identify patterns in side-effects

experienced by patients receiving chemotherapy,

data was analysed with a view to answering the

following key questions:

• Does the chemotherapy regime impact on the

side-effects experienced?

• Can we predict later symptoms from the pattern

of early symptoms?

• If side-effects are experienced in an early cycle

does this increase the likelihood of experiencing

the same side-effects in later cycles?

• Do some side-effects always occur together and

does the presence of some symptoms make

others less likely to occur?

• Does the severity to which a side-effect is

experienced impact on the likelihood of that

side-effect occurring again?

The principal aim of the project was to provide new

patients with a prediction of side-effects they are

likely to experience across all cycles of

chemotherapy. The secondary aim was to provide

patients with ongoing side-effect information. By

monitoring their side-effects over a period of time,

we hoped to provide up-to-date predictive

information which is revised and reviewed

(according to how their side-effects change) over the

cycle of treatments. Currently, the study focuses on

six symptoms associated with chemotherapy:

mucositis, nausea, vomiting, fatigue, diarrhoea, and

hand-foot syndrome.

2.4 Research Methodology

2.4.1 Data Collection

Thirty-three retrospective cases of patients with

breast cancer undergoing chemotherapy have been

used in the study. Risk modelling analysis was

performed on this data in an attempt to answer the

questions posed in Section 2.2. Current data

collection is also taking place from three sites across

Scotland,

which will form a prospective data set

consisting of forty patients. These patients have been

diagnosed with breast cancer and are commencing

adjuvant chemotherapy.

Data is being collected using a series of daily

patient self-reporting paper-based symptom

questionnaires collected throughout 4 cycles of

chemotherapy. The daily symptom questionnaire is

being used in addition to the clinical use of two

existing questionnaires commonly used in practice

to assess and grade chemotherapy related symptoms

– the Common Toxicity (CTC) grading system

(National Cancer Institute, 2003) and the

Chemotherapy Symptom Assessment Scale (C-SAS)

(Brown et al, 2001). This data will be used to further

assess the accuracy of the risk-modelling tool.

2.4.2 Data Analysis

The data analysis performed uses both traditional

statistical techniques and a class of more advanced,

powerful techniques collectively known as 'data

mining'. Data mining is task oriented, which means

that analysis begins with the definition of a task and

progresses through the use of data and software to

develop a system for performing the chosen task. In

this study, the task is to predict future symptoms

from a combination of patient data and current

symptoms. The pattern of symptoms experienced as

a patient progresses through a chemotherapy regime

is not random, and as such can be predicted. Data

mining tools are designed to find the structure that

allows such predictions to be made.

The data from this study was analysed in two

distinct forms. One which treats the data as a time

series on the assumption that there is a pattern in the

way symptoms evolve over time (trends or cycles,

for example) and the other being static, working on

the assumption that the patient's initial state and the

chemotherapy regime alone are sufficient to predict

when and with what severity symptoms will occur.

For the time series analysis, we used dynamic

Bayesian networks, Markov models and a

decompositional approach. For the static prediction

the principal tools used were neural networks,

cluster analysis, and Bayesian belief networks.

ASYMS©-SERAT: A SIDE-EFFECT RISK ASSESSMENT TOOL TO PREDICT CHEMOTHERAPY RELATED

TOXICITY IN PATIENTS WITH CANCER RECEIVING CHEMOTHERAPY

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3 ASYMS©-SERAT

3.1 Introduction to ASyMS©-SERAT

The ASyMS©-SERAT tool will be incorporated into

a mobile phone based, advanced symptom

management system (ASyMS

©-C) which has been

developed to remotely monitor the side effects of

chemotherapy in patients with cancer receiving

chemotherapy (Maguire et al, 2005).

The ASyMS

©-SERAT tool employs the use of risk

modelling techniques to provide patients and

clinicians with predictions of likely side effects. The

prototype tool can be used to provide predictive

information to both new patients, and those currently

undergoing treatment. For new patients, the tool

allows patient specific data to be entered and

provides feedback as to likely side-effects (along

with severity details) that will occur. As patients

undergo treatment and side effects are monitored,

the tool can measure these against the original

prediction model. Prior to each cycle of treatment, a

patient’s predicted model can be reviewed and

revised to provide a new predictive model if felt

necessary.

The development of the tool has been split into

two phases: Phase I which concentrates on the

provision of information for new patients, and Phase

II which provides information for returning patients

part-way through their chemotherapy regime. To

date, Phase I has been completed and it is envisaged

that Phase II will be completed by February 2008.

3.2 ASyMS©-SERAT in Use

This description of ASyMS©-SERAT will focus on

Phase I of the tool as this has now been completed.

In Phase I of ASyMS

©-SERAT, the tool uses

information it has learnt from the data and combines

this with patient specific information to predict the

likely side effects a patient will experience over the

course of their treatment. The patient can receive

predictions relating to possible symptoms they are

likely to experience in their first cycle of treatment

as well as possible symptoms they are likely to

experience across all cycles of treatment.

In Figure 1 we provide a sample screenshot

depicting information about side-effects a patient

may experience in their first cycle of treatment.

From the textual scrolling area at the top of the

screen, it is evident that this patient has a high

chance of experiencing both nausea and fatigue

during cycle 1, and some possibility of experiencing

mucositis. The pie charts show how severe each of

these symptoms could be. For example, about 17%

of cases of nausea will be severe, about 33% will be

moderate, and about 50% will be mild.

Figure 1: Screenshot of ASyMS©-SERAT showing likely

side-effects in cycle 1 of treatment

Figure 2: Screenshot of ASyMS©-SERAT showing

predicted severity of nausea symptoms across cycles 1-4.

The screenshot in Figure 2 provides longitudinal

information across all cycles. Such information can

be used to inform the patient about likely patterns of

symptoms over time. In the example shown, it is

evident that although the patient is experiencing

severe nausea in cycle one, this symptom will

become moderate during cycles two and three,

falling to mild by cycle four.

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4 CONCLUSIONS AND FUTURE

WORK

Although research on the project is still in its infancy

and the ASyMS©-SERAT tool is very much a

prototype system, initial results from the risk

modelling analysis are very promising. From initial

testing it would seem that through use of ASyMS

©-

SERAT, accurate, personalised predictions of

possible side-effects can be made, providing patients

with a more informed view of their treatment, and

clinicians with the information required for

preventative measures or management of side-

effects to be applied where possible.

Once Phase II of ASyMS

complete, we hope to incorporate the tool in existing

ASyMS

complete symptom prediction and management tool

will hopefully allow patients to feel more in control

of their symptoms, knowing in advance what to

expect, and how to manage the symptoms

accordingly. A larger, more comprehensive

evaluation of the ASyMS

conducted as part of this work. We are currently in

the process of applying for further funding to

facilitate this next stage of the project.

ACKNOWLEDGEMENTS

We are grateful to the following organisations for

funding this project: Stirling University Research

and Enterprise (SURE) Ltd. and Fife & Forth Valley

Enterprise.

REFERENCES

Armer, J. M., Radina, M. E., Porock, D., & Culbertson, S.

D. 2003, "Predicting breast cancer-related

lymphedema using self-reported symptoms", Nursing

Research, vol. 52, no. 6, pp. 370-379.

Chen-Hardee, S., Chrischilles, E. A., Voelker, M. D.,

Brooks, J. M., Scott, S., Link, B. K., & Delgado, D.

2006, "Population-based assessment of

hospitalizations for neutropenia from chemotherapy in

older adults with non-Hodgkin's lymphoma (United

States)", Cancer Causes & Control, vol.17, no.5, pp.647-654.

Chow, E., Harris, K., & Fung, K. 2006, "Sucessful

validation of a survival prediction model in patients

with metastases of the spinal column", International

Journal of Radiation Oncology, Biology, Physics, vol.

65, no. 5, pp. 1522-1527.

Cohn, K. H. 1982, "Chemotherapy from the insiders

perspective.", The Lancet, vol. 319, pp. 1006-1009.

Cowie J., Oteniya, L., Coles, R. 2006 DIAGNOSIS OF

DEMENTIA AND ITS PATHOLOGIES USING

BAYESIAN BELIEF NETWORKS. In Proceedings

of the 8th International Conference on Enterprise

Information Systems: Artificial Intelligence and

Decision Support Systems, INSTICC Press, Paphos,

Cyprus. pp 291-295.

De Toro F, Ros E, Mota S, Ortega J. 2003 Non-invasive

Atrial Disease Diagnosis Using Decision Rules: A

Multi-objective Optimisation Approach. In: Fonseca

CM, Flemming PJ, Zitzler E, Kalyanmoy D, Thiele L,

eds. Evolutionary Multi-Criterion Optimization. Proc.

of 2nd Int. EMO Conference, Faro, Portugal. Springer:

638-647.

Dikken, C. & Sitzia, J. 1998, "Patients' experiences of

chemotherapy: side-effects associated with 5-

flurouracil and folinic acid in the treatment of

colorectal cancer", Journal of Clinical Nursing, vol. 7,

pp. 371-379.

Donohue, R. B. 2006, "Development and Implementation

of a Risk Assessment Tool for Chemotherapy-Induced

Neutropenia", Oncology Nursing Forum, vol. 33, no.

2, pp. 347-352.

Du, X. L., Osborne, C., & Goodwin, J. S. 2002,

"Population-based assessment of hospitalizations for

toxicity from chemotherapy in older women with

breast cancer", Journal of Clinical Oncology, vol. 20,

no. 24, pp. 4636-4642.

Dybowski R, Weller P, Chang R, Gant V. Prediction of

outcome in the critically ill using an artificial neural

network synthesised by a genetic algorithm. The

Lancet (vol 347): Elsevier, 1996: 1146-1150.

García-Pérez E, Violante A, Cervantes-Pérez F. 1998

Using neural networks for differential diagnosis of

Alzheimer disease and vascular dementia. Expert

Systems with Applications (vol.14). Elsevier; 219-225.

Kuderer, N. M., Dale, D. C., Crawford, J., Cosler, L. E., &

Lyman, G. H. 2006, "Mortality, morbidity, and cost

associated with febrile neutropenia in adult cancer

patients", Cancer, vol. 106, no. 10, pp. 2258-2266.

Lyman, G. H., Lyman, C. H., Agboola, O., & for the ANC

Study Group 2005, "Risk Models for Predicting

Chemotherapy-Induced Neutropenia", The Oncologist,

vol. 10, pp. 427-437.

Maguire, R., Miller, M., Sage, M., Norrie, J., McCann, L.,

Taylor, L., & Kearney, N. 2005, "Results of a UK

based pilot study of a mobile phone based advanced

symptom management system (ASyMS) in the remote

monitoring of chemotherapy related toxicity", Clinical

Effectiveness in Nursing, vol. 2005 Sep-Dec; 9, no. 3-

4, pp. 202-210

Mani S, Shankle WR, Pazzani MJ, Smyth P, Dick MB.

1997 Differential Diagnosis of Dementia: A

Knowledge Discovery and Data Mining (KDD)

Approach. In: Masys DR, ed. Journal of American

Medical Informatics Association supplement. Full

paper in extended proceedings of Proc. of AMIA.,

Hanley and Belfus : 875-880.

McCaughan, E. M. & Thompson, K. A. 2000,

"Information needs of cancer patients receiving

chemotherapy at a day-case unit in Northern Ireland",

TOXICITY IN PATIENTS WITH CANCER RECEIVING CHEMOTHERAPY

229

Journal of Clinical Nursing, vol. 2000 Nov; 9, no.6,

pp. 851-858.

National Cancer Institute (2003) Common Toxicity

Adverse Events Criteria

Polednak, A. P. 2004, "Surveillance for hospitalizations

with infection-related diagnoses after chemotherapy

among breast cancer patients diagnosed before age

65", Chemotherapy.Vol.50(4)()(pp 157-161), 2004. no.

4, pp. 157-161.

Poleshuck, E. L., Katz, J., Andrus, C. H., Hogan, L. A.,

Jung, B. F., Kulick, D. I., & Dworkin, R. H. 2006,

"Risk factors for chronic pain following breast cancer

surgery: a prospective study", Journal of Pain, vol. 7,

no. 9, pp. 626-634.

Sanchez, C. d. M., Elustondo, S. G., Estirado, A., Sanchez,

F. V., Cooper, C. G., Romero, A. L., Otero, A., &

Olmos, L. G. 2006, "Palliative Performance Status,

Heart Rate and Respiratory Rate as Predictive Factors

of Survival Time in Terminally Ill Cancer Patients",

Journal of Pain & Symptom Management, vol. 31, no.

6, pp. 485-492.

Skalla, K. A., Bakitas, M., Furstenberg, C. T., Ahles, T., &

Henderson, J. V. 2004, "Patients' need for information

about cancer therapy", Oncology Nursing Forum, vol.

2004 Mar; 31, no. 2, pp. 313-319.

Talcott, J. A., Manola, J., Clark, J. A., Kaplan, I., Beard,

C. J., Mitchell, S. P., Chen, R. C., O'Leary, M. P.,

Kantoff, P. W., & D'Amico, A. V. 2003, "Time

Course and Predictors of Symptoms After Primary

Prostate Cancer Therapy", Journal of Clinical

Oncology, vol. 21, no. 1, pp. 3979-3986.

Tierney, A. J., Taylor, J., & Closs, S. J. 1992,

"Knowledge, expectations and experiences of patients

receiving chemotherapy for breast cancer.",

Scandanavian Journal of Caring Sciences, vol. 6, no.

2, pp. 75-80.

Vigano, A., Dorgan, M., Buckingham, J., Bruera, E., &

Suarez-Almazor, M. 2000, "Survival prediction in

terminal cancer patients: a systematic review of the

medical literature", Palliative Medicine, vol. 14, pp.

363-374.

Werner J.C & Fogarty T.C. 2001. Genetic programming

applied to Collagen disease and thrombosis. In: De

Raedt L, Siebes A, eds. Discovery challenge on

Thrombosis Data. European Conf. on Principles and

Practice of Knowledge Discovery in Databases.

Springer-Verlag, 14-20.

HEALTHINF 2008 - International Conference on Health Informatics

230