Predicting Quality of Life Changes in Hemodialysis Patients Using Machine Learning: Generation of an Early Warning System

Abstract

Objective
To predict changes in the quality of life scores of hemodialysis patients for the coming month
and the development of an early warning system using machine learning
Methods
It was a prospective cohort study (one-month duration) at the dialysis center of a tertiary care
hospital in Pakistan. The study started on 1
st October 2016. About 78 patients have been
enrolled till now. Bachelor of Medicine and Bachelor of Surgery (MBBS) qualified doctors
administered a proforma with demographics and the validated Urdu version of World Health
Organization Quality Of Life-BREF (WHOQOL-BREF). It was to be repeated after one month to
the same patient by the same investigator. Simple statistics were computed using SPSS version
24 (IBM Corp., Armonk, NY) while machine learning was performed using R (version 3.0) and
Orange (version 3.1).
Results
Using machine learning algorithms, two models (classification tree and Naïve Bayes) were
generated to predict an increase or decrease of 5% in a patient’s WHOQOL-BREF score over one
month. The classification tree was selected as the most accurate model with an area under
curve (AUC) of 83.3% (accuracy: 81.9%) for the prediction of 5% increase in QOL and an AUC of
76.2% (accuracy: 81.8%) for the prediction of 5% decrease in QOL over the coming month. The
factors associated with an increase of QOL by 5% or more over the next month included younger
age (<19 years) and higher iron sucrose doses (>278mg/month). Drops in psychological,
physical, and social domain scores lead to a decrease of 5% or more in QOL scores over the
following month.
Conclusion
An early warning system, dialysis data interpretation for algorithmic-prediction on quality of
life (DIAL) was built for the early detection of deteriorating QOL scores in the hemodialysis
population using machine learning algorithms. The model pointed out that working on psychological and environmental domains, in particular, may prevent the drop in QOL scores
from occurring. DIAL, if implemented on a larger scale, is expected to help patients in terms of
ensuring a better QOL and in reducing the financial burden in the long term.

Full text

Received 07/30/2017
Review began 09/11/2017
Review ended 09/21/2017
Published 09/25/2017
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Saadat et al. This is an open access
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Predicting Quality of Life Changes in
Hemodialysis Patients Using Machine
Learning: Generation of an Early Warning
System
Shoab Saadat , Ayesha Aziz , Hira Ahmad , Hira Imtiaz , Zara S. Sohail , Alvina Kazmi ,
Sanaa Aslam , Naveen Naqvi , Sidra Saadat
1. Department of Nephrology, Shifa International Hospital, Islamabad, Pakistan 2. Medicine, Aga Khan
University Hospital, Islamabad, Pakistan 3. Medicine, Shifa International Hospital, Islamabad, Pakistan
4. Medicine, Shifa College of Medicine, Islamabad, Pakistan 5. Medicine, Amna Inyat Medical College,
Lahore, Pakistan 6. Medicine, Rawalpindi Medical College, Rawalpindi, Pakistan
 Corresponding author: Shoab Saadat, dr.shoaibsaadat@gmail.com
Disclosures can be found in Additional Information at the end of the article
Abstract
Objective
To predict changes in the quality of life scores of hemodialysis patients for the coming month
and the development of an early warning system using machine learning
Methods
It was a prospective cohort study (one-month duration) at the dialysis center of a tertiary care
hospital in Pakistan. The study started on 1st October 2016. About 78 patients have been
enrolled till now. Bachelor of Medicine and Bachelor of Surgery (MBBS) qualified doctors
administered a proforma with demographics and the validated Urdu version of World Health
Organization Quality Of Life-BREF (WHOQOL-BREF). It was to be repeated after one month to
the same patient by the same investigator. Simple statistics were computed using SPSS version
24 (IBM Corp., Armonk, NY) while machine learning was performed using R (version 3.0) and
Orange (version 3.1).
Results
Using machine learning algorithms, two models (classification tree and Naïve Bayes) were
generated to predict an increase or decrease of 5% in a patient’s WHOQOL-BREF score over one
month. The classification tree was selected as the most accurate model with an area under
curve (AUC) of 83.3% (accuracy: 81.9%) for the prediction of 5% increase in QOL and an AUC of
76.2% (accuracy: 81.8%) for the prediction of 5% decrease in QOL over the coming month. The
factors associated with an increase of QOL by 5% or more over the next month included younger
age (<19 years) and higher iron sucrose doses (>278mg/month). Drops in psychological,
physical, and social domain scores lead to a decrease of 5% or more in QOL scores over the
following month.
Conclusion
An early warning system, dialysis data interpretation for algorithmic-prediction on quality of
life (DIAL) was built for the early detection of deteriorating QOL scores in the hemodialysis
population using machine learning algorithms. The model pointed out that working on
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Open Access Original
Article DOI: 10.7759/cureus.1713
How to cite this article
Saadat S, Aziz A, Ahmad H, et al. (September 25, 2017) Predicting Quality of Life Changes in
Hemodialysis Patients Using Machine Learning: Generation of an Early Warning System. Cureus 9(9):
e1713. DOI 10.7759/cureus.1713

psychological and environmental domains, in particular, may prevent the drop in QOL scores
from occurring. DIAL, if implemented on a larger scale, is expected to help patients in terms of
ensuring a better QOL and in reducing the financial burden in the long term.
Categories: Internal Medicine, Preventive Medicine, Nephrology
Keywords: machine learning, classification tree, naïve bayes, hemodialysis, prediction, quality of life
Introduction
Dialysis patients usually have a long commitment to a certain lifestyle. This, in turn, has a
significant impact on their quality of life (QOL), irrespective of the modality used [1]. Several
factors, such as environmental, social, psychological, financial, and physical, play an important
role in determining the QOL that an individual enjoys [1-3]. Several studies have been carried
out worldwide with the purpose of identifying the most significant correlates with a better QOL
[4-5]. Since there has been no study specifically aimed at the most important predictors of QOL
in order of their strength of association using modern machine learning techniques, the
purpose of this study is to produce an early warning system, dialysis data interpretation for
algorithmic-prediction on quality of life (DIAL), using machine learning to predict a change in
QOL in a hemodialysis patient over the coming month. This will be helpful in directing
resources toward the high-risk population group.
Materials And Methods
This was a prospective cohort study (of six months’ duration) at the hemodialysis unit of a
tertiary care center in Pakistan. It included all the consenting patients who are more than 15
years of age, diagnosed with end-stage renal disease (ESRD) for more than a year, have been on
a certain hemodialysis regimen (twice or thrice weekly) for at least three months, and don’t
have any disability in communication. All those who did not fulfill the inclusion criteria,
patients with a known psychological disorder, patients admitted to critical care units, and
patients who had recently (within the last three months) switched from one hemodialysis
regimen to the other were excluded from the study. Patients were included by non-probability
convenience sampling. Permission for commencement was taken from the local ethics
committee. The study started on 1st October 2016.
A total of 78 patients were enrolled. An MBBS qualified doctor administered a proforma with
demographic questions and the validated Urdu version of World Health Organization Quality of
Life-BREF (WHOQOL-BREF) by Khan MN et al. [6]. WHOQOL-BREF Urdu has already been
validated for the hemodialysis population in Pakistan; thus, it was a fitting choice for QOL
assessment. WHOQOL-BREF Urdu has 26 questions. Question one asks about an individuals’
overall perception of QOL and question two is about the overall perception of health. The
remaining questions pertain to four major domains of life, i.e., physical health, psychological
health, social relationships, and the environment. All domains have different raw score ranges;
for uniformity, all raw scores were transformed to the 4–20 range according to WHO guidelines.
Higher scores show a better QOL. Scores from all the four domains were later combined into one
final QOL score. The questionnaire was administered at the start of the study on day zero, then
repeated after one month to the same patient by the same investigator. The outcome variable
was the amount of change in the total QOL score (delta QOL) over the coming month. The
predictor variables were age, gender, income per month, iron sucrose dose per month, and total
QOL score at the beginning of the study. Other variables as predictors included changes over
the coming month for individual domain scores, hemoglobin, and serum albumin. A first
interim analysis was performed on 15th January 2017. Based on the results obtained from the
first interim analysis, the foundations of an early warning system, dialysis data interpretation
2017 Saadat et al. Cureus 9(9): e1713. DOI 10.7759/cureus.1713 2 of 10

for algorithmic-prediction of quality of life (DIAL), were also laid. DIAL’s sole purpose is to
make automated monthly data collection of QOL scores and other predictor variables. DIAL is
currently in the implementation phase and its impact on the improvement of the clinical and
financial aspects of QOL in dialysis patients will be assessed at a later date after the data is
collected. Descriptive statistics in the current study were done using SPSS version 24 (IBM
Corp., Armonk, NY). Mean and standard deviations were used to describe continuous variables
like age and QOL scores, while percentages and frequencies were used to describe categorical
variables. Machine learning was performed using R (version 3.0) and Orange (version 3.1) [7].
Results
A total of 78 patients were included in the interim analysis. The mean age in years was 51.00
(SD=20). Males comprised 53.8% (42/78) of the total population. The mean duration of
hemodialysis was 41.40 months (SD=28.90). The mean albumin levels at the start and end of
the one-month period were 3.61 g/dl (SD=0.52) and 3.63 g/dl (SD=0.53), respectively. The
means of the total QOL scores at the beginning and end of the one-month study period were
57.6 (SD=10.33) and 59.3 (SD=10.24), respectively, as seen in Table 1.
2017 Saadat et al. Cureus 9(9): e1713. DOI 10.7759/cureus.1713 3 of 10

Overall Male Female
Variables Mean SD1Mean SD Mean SD
Age (years) 51.00 20.00 54.00 19.00 47.00 22.00
Duration on hemodialysis (months) 41.40 28.90 47.50 31.30 34.30 24.50
Albumin (g/dl) - start 3.61 0.52 3.66 0.55 3.56 0.48
Albumin (g/dl) - end 3.63 0.53 3.70 0.56 3.56 0.49
Hemoglobin (g/dl) - start 10.42 1.70 10.56 1.84 10.25 1.80
Hemoglobin (g/dl) - end 10.06 1.55 10.07 1.85 10.04 1.13
Change2 in DOM1 - Physical 0.40 2.68 0.33 3.08 0.48 2.15
Change2 in DOM2 - Psychological 1.01 2.75 1.62 2.91 0.30 2.39
Change2 in DOM3 - Social -0.22 3.51 -0.16 3.89 -0.30 3.08
Change2 in DOM4 - Environmental 0.53 2.71 0.68 3.11 0.35 2.19
Change2 in QOL 1.71 7.65 2.47 8.59 0.82 6.39
Total QOL score - start 57.61 10.33 58.98 11.06 56.02 9.32
Total QOL score - end 59.32 10.24 61.44 9.05 56.84 11.11
1 Standard deviation
2 Change observed over the past month
TABLE 1: Descriptive details of variables included in the analysis
QOL: quality of life; DOM: domain
A series of student t-tests were carried out to find whether a similar difference exists among
genders, as shown in Table 2. It showed there was a significant difference in the mean scores of
psychological domains and the overall QOL score among males and females. Males had better
relative scores.
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Variables Mean Difference Sig. (2-tailed)
DOM1 - Physical -0.9 0.208
DOM2 - Psychological -1.6 0.044
DOM3 - Social -1.1 0.085
DOM4 - Environmental -1.1 0.083
Total QOL Score -4.6 0.047
Change in QOL Score -1.60 0.35
TABLE 2: Student T-test on differences in QOL domain scores among males vs
females undergoing hemodialysis
QOL: quality of life; DOM: domain
A linear regression model was then (p<0.000, r2=0.418) fit. Age, gender, income per month,
number of months on hemodialysis, and changes in values for variables like serum albumin,
potassium, calcium, phosphate, and hemoglobin were used as predictors for model
development. The overall change in total QOL score was selected to be the outcome variable.
The model showed monthly income (p<0.000) and serum albumin (p<0.000) to be positively and
significantly associated with better QOL, as shown in Table 3.
2017 Saadat et al. Cureus 9(9): e1713. DOI 10.7759/cureus.1713 5 of 10

Change in variable B1Sig.2
Income per family 4.52 <0.000
Albumin 8.14 <0.000
Age -0.08 0.153
Calcium -1.55 0.270
Months on HD -0.04 0.310
Hemoglobin 0.56 0.400
Gender 1.45 0.492
Phosphate 0.31 0.548
Potassium -0.45 0.682
(Constant) 27.35 0.029
Dependent variable: Positive change in total WHOQOL-BREF score 1 Beta coefficient 2 Level of significance (P-
value)
TABLE 3: Linear regression analysis - coefficients table
QOL: quality of life
Using machine learning algorithms (Figure 1), two models (classification tree and Naïve Bayes)
were generated to predict an increase or decrease of 5% in a patient’s WHOQOL-BREF score
over one month. The classification tree was selected as the most accurate model with an area
under curve (AUC) of 83.3% (accuracy: 81.9%) for the prediction of 5% increase in QOL and an
AUC of 76.2% (accuracy: 81.8%) for the prediction of 5% decrease in QOL over the coming
month. The factors that were associated with an increase in the QOL score by 5% over the next
month were a positive change in domain four (environmental), a total QOL score of <65 at the
beginning of the cohort study, age less than 19 years, and iron sucrose doses >278mg/month.
The factors associated with a decrease of 5% (Figure 2) in the QOL score over the following
month included a decrease in domains two (psychological), one (physical), and three (social),
and a greater than 61 total QOL score at the start of the cohort study in order of their
importance.
2017 Saadat et al. Cureus 9(9): e1713. DOI 10.7759/cureus.1713 6 of 10

FIGURE 1: Machine learning algorithm in use with confusion
matrices shown for both prediction models (increase or
decrease of 5% in QOL score)
QOL: quality of life
FIGURE 2: Classification tree: factors associated with decrease
in QOL scores by 5% or more
QOL: quality of life
Discussion
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Hemodialysis patients represent a special set of population. After hearing the diagnosis of end-
stage renal disease (ESRD), many patients undergo some level of depression [8]. There are
physical, social, and psychological impacts on their life, which are reflected in their overall
QOL [9-10]. There has always been a need to identify patients at high risk of dropping QOL
scores and working on specific domains to aid recovery.
In a tertiary care center of Islamabad, using the WHOQOL-BREF Urdu questionnaire, we
collected data regarding the most significant factors that might influence QOL scores in
hemodialysis populations. Using modern machine learning methods, we succeeded in building
a prediction model that can forecast a change in QOL score in either direction, one month in
advance. There have been many studies showing the factors associated with changes in QOL
[11]. One of the earlier studies performed in the local population showed that unemployment
and psychiatric disease were independently and significantly associated with lower scores of
QOL in the dialysis population [12]. To our knowledge, this is the first instance of using modern
data analytic techniques to this problem. There is no example of generating an early warning
system like DIAL, which may be used as a monthly surveillance system, in the long run, to
assess and shortlist patients with the highest risk of having a drop in QOL scores in the coming
month. The implementation of such a system is present in other fields [13] and is found to have
significant and positive impacts on the financial and clinical aspects of patient management
[14].
We also found that domain four (environmental domain) was positively associated with better
QOL scores. This is consistent with some earlier studies as well [15]. In an earlier study, age was
also found to be significantly associated with good QOL scores [16]. This is also evident in our
study. Higher doses of iron sucrose have been given in dialysis patients to replete iron stores
[17]. In our study, higher iron replacement doses (>278 mg per month) were found to
be associated with better QOL scores. Since the maximum dose given was 800 mg per month
intravenously, we could not ascertain whether doses greater than 800 mg are associated with a
negative change in QOL scores or not. Other studies have found optimum iron replacement
doses in terms of clinical improvement in hemoglobin and, thus, indirectly improving clinical
symptoms [18].
Among the patients who suffered a negative change in QOL scores, changes in the
psychological, physical, and social domains were the most important contributors. Older
patients, but younger than 60 years of age, were more prone to a negative change in QOL scores
over the coming month. This may be because these middle-aged patients feel limited and
restricted earlier in their life due to dialysis, leading to a greater burden of psychological
problems when compared to older (>60) and younger (<30) populations. The relation of
increasing age with QOL scores has already been shown in an earlier study [16]. Our study also
showed that males had better overall QOL and psychological domain scores when compared to
females. The findings were statistically significant but unadjusted for other covariates.
There were also a few limitations in our study. It was an observational study conducted on the
local Pakistani population. Also, most of the questions asked regarding QOL were subjective
measures of one’s own perception. Since we used the validated questionnaire for our
population in their own native language, this factor is expectedly addressed to the maximum
possibility. Also, this is an interim report on the ongoing project, which is expected to be
completed at the end of 2019. Some of the candidate covariates that are not assessed in the
interim analysis but will be used in the final report include serum iron, total iron binding
capacity (TIBC), usage of any supplementary medicine/multivitamins, diet regimens, history of
receiving any psychological or physical therapies, and so on. Despite the smaller sample size
and convenience sampling, the significant values of AUC and a high accuracy suggest a very
stable and highly dependable prediction and surveillance system. This leads our team to move
on to the publication of the interim results.
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Currently, the DIAL screening/surveillance system has been implemented in our institution. We
expect to find out if DIAL helps in reducing the long-term financial burden on dialysis patients.
The use of machine learning techniques in the health sector will help doctors make smart
decisions. This is expected to help doctors in the efficient management of their patients with
more confidence.
Conclusions
In this study, we built an early warning system, referred to as DIAL, for the early detection of a
deteriorating QOL score in the hemodialysis population using machine learning algorithms.
This model was able to identify a subset of the hemodialysis population at the highest risk of
this deterioration with an AUC of 83.3%. The model also suggested working on the
psychological and environmental domains, in particular, to prevent this drop from occurring.
DIAL, if implemented on a larger scale, is expected to help patients in terms of ensuring a
better QOL and a reduction in the financial burden in the long term.
Additional Information
Disclosures
Human subjects: Consent was obtained by all participants in this study. Animal subjects:
This study did not involve animal subjects or tissue. Conf licts of interest: The authors have
declared that no conflicts of interest exist.
References
1. Tannor EK, Archer E, Kapembwa K, van Schalkwyk SC, Davids MR: Quality of life in patients
on chronic dialysis in South Africa: a comparative mixed methods study. BMC Nephrol. 2017,
18:4. 10.1186/s12882-016-0425-1
2. Anees M, Malik MR, Abbasi T, Nasir Z, Hussain Y, Ibrahim M: Demographic factors affecting
quality of life of hemodialysis patients - Lahore, Pakistan. Pak J Med Sci Q. 2014, 30:1123–
1127. 10.12669/pjms.305.5239
3. Farragher JF, Polatajko HJ, Jassal SV: The relationship between fatigue and depression in
adults with end-stage renal disease on chronic in-hospital hemodialysis: a scoping review. J
Pain Symptom Manage. 2017, 53:783–803. 10.1016/j.jpainsymman.2016.10.365
4. Ginieri-Coccossis M, Theofilou P, Synodinou C, Tomaras V, Soldatos C: Quality of life, mental
health and health beliefs in haemodialysis and peritoneal dialysis patients: investigating
differences in early and later years of current treatment. BMC Nephrol. 2008, 9:14.
10.1186/1471-2369-9-14
5. Sapkota A, Sedhain A, Rai MK: Quality of life of adult clients on renal replacement therapies
in Nepal. J Ren Care. 2013, 39:228–235. 10.1111/j.1755-6686.2013.12021.x
6. Khan MNS, Akhter MS, Ayub M, Alam S, Laghari NU: Translation and validation of quality of
life scale, the brief version. J Coll Physicians Surg Pak. 2003, 13:98–100.
7. Bischl B, Lang M, Kotthoff L, et al.: mlr: machine learning in R . J Mach Learn Res. 2016, 1-5.
8. Hinrichsen GA, Lieberman JA, Pollack S, Steinberg H: Depression in hemodialysis patients.
Psychosomatics. 1989, 30:284–289. 10.1016/S0033-3182(89)72273-8
9. Lok P: Stressors, coping mechanisms and quality of life among dialysis patients in Australia . J
Adv Nurs. 1996, 23:873–881. 10.1046/j.1365-2648.1996.00893.x
10. Patel SS, Peterson RA, Kimmel PL: The impact of social support on end-stage renal disease .
Semin Dial. 2005, 18:98–102. 10.1111/j.1525-139X.2005.18203.x
11. Valderrábano F, Jofre R, López-Gómez JM: Quality of life in end-stage renal disease patients .
Am J Kidney Dis. 2001, 38:443–464. 10.1053/ajkd.2001.26824
12. Lopes AA, Bragg-Gresham JL, Goodkin DA, et al.: Factors associated with health-related
quality of life among hemodialysis patients in the DOPPS. Qual Life Res. 2007, 16:545.
10.1007/s11136-006-9143-7
13. Kononenko I: Machine learning for medical diagnosis: history, state of the art and
perspective. Artif Intell Med. 2001, 23:89–109. 10.1016/S0933-3657(01)00077-X
2017 Saadat et al. Cureus 9(9): e1713. DOI 10.7759/cureus.1713 9 of 10

14. Witten IH, Frank E, Hall MA, Pal CJ: Data mining: practical machine learning tools and
techniques. Morgan Kaufmann Publishers, Burlington, Massachusetts, United States; 2016.
15. Anees M, Hameed F, Mumtaz A, Ibrahim M, Saeed Khan MN: Dialysis-related factors affecting
quality of life in patients on hemodialysis. Iran J Kidney Dis. 2011, 5:9-14.
16. Lamping DL, Constantinovici N, Roderick P et al.: Clinical outcomes, quality of life, and costs
in the North Thames Dialysis Study of elderly people on dialysis: a prospective cohort study.
Lancet. 2000, 356:1543–1550. 10.1016/S0140-6736(00)03123-8
17. Aronoff GR, Bennett WM, Blumenthal S, et al.: Iron sucrose in hemodialysis patients: safety
of replacement and maintenance regimens. Kidney Int. 2004, 66:1193–1198. 10.1111/j.1523-
1755.2004.00872.x
18. Eschbach JW, Cook JD, Scribner BH, Finch CA: Iron balance in hemodialysis patients . Ann
Intern Med. 1977, 87:710-713. 10.7326/0003-4819-87-6-710
2017 Saadat et al. Cureus 9(9): e1713. DOI 10.7759/cureus.1713 10 of 10