Network Analysis of the Multidimensional Symptom Experience of Oncology

Nikolaos Papachristou, Payam Barnaghi, Bruce Cooper, Kord M Kober, Roma Maguire, Steven M Paul, Marilyn Hammer, Fay Wright, Jo Armes, Eileen P Furlong, Lisa McCann, Yvette P Conley, Elisabeth Patiraki, Stylianos Katsaragakis, Jon D Levine, Christine Miaskowski, Nikolaos Papachristou, Payam Barnaghi, Bruce Cooper, Kord M Kober, Roma Maguire, Steven M Paul, Marilyn Hammer, Fay Wright, Jo Armes, Eileen P Furlong, Lisa McCann, Yvette P Conley, Elisabeth Patiraki, Stylianos Katsaragakis, Jon D Levine, Christine Miaskowski

Abstract

Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We present findings from the first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients undergoing chemotherapy. Using two different models of Pairwise Markov Random Fields (PMRF), we examined the nature and structure of interactions for three different dimensions of patients' symptom experience (i.e., occurrence, severity, distress). Findings from this study provide the first direct evidence that the connections between and among symptoms differ depending on the symptom dimension used to create the network. Based on an evaluation of the centrality indices, nausea appears to be a structurally important node in all three networks. Our findings can be used to guide the development of symptom management interventions based on the identification of core symptoms and symptom clusters within a network.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
A Pairwise Markov Random Field (PMRF) or an undirected graphical model with 6 nodes, A to F. The presence of edges between nodes indicates the conditional dependency between them.
Figure 2
Figure 2
The estimated networks of 38 cancer symptoms across the “occurrence” dimension (a) without the identified communities and (b) with the identified communities (walktrap algorithm). Nodes represent symptoms and edges represent pairwise dependencies between the symptoms, after controlling for all of the other correlations of a given node. The 38 cancer symptoms represented in the nodes above are coded in the following fashion: difcon: Difficulty Concentrating, pain: Pain, energy: Lack of Energy, cough: Cough, nervous: Feeling Nervous, hotflash: Hot Flashes, drymouth: Dry Mouth, nausea: Nausea, drowsy: Feeling Drowsy, numb: Numbness or Tingling in Hands or Feet, chest: Chest Tightness, difbreath: Difficulty Breathing, difsleep: Difficulty Sleeping, bloat: Feeling Bloated, urinate: Problems with Urination, vomit: Vomitting, sob: Shortness of Breath, diarrhea: Diarrhea, sad: Feeling Sad, sweats: Sweats, sexual: Problems with Sexual Interest or Activity, worry: Worrying, itch: Itching, appetite: Lack of Appetite, abdominal: Abdominal Cramps, increaseapp: Increased Appetite, wtgain: Weight Gain, dizzy: Dizziness, swallow: Difficulty Swallowing, irritable: Feeling Irritable, mouthsore: Mouth Sore, wtloss: Weight Loss, hairloss: Hair Loss, constipat: Constipation, swelling: Swelling, taste: Change in the Way Food Tastes, myself: I Do Not Look Like Myself, skin: Changes in Skin.
Figure 3
Figure 3
The estimated networks of 38 cancer symptoms across the “severity” dimension (a) without the identified communities and (b) with the identified communities (walktrap algorithm). Nodes represent symptoms and edges represent a partial correlation between the symptoms, after controlling for all of the other correlations of a given node. The 38 cancer symptoms represented in the nodes above are coded in the following fashion: difcon: Difficulty Concentrating, pain: Pain, energy: Lack of Energy, cough: Cough, nervous: Feeling Nervous, hotflash: Hot Flashes, drymouth: Dry Mouth, nausea: Nausea, drowsy: Feeling Drowsy, numb: Numbness or Tingling in Hands or Feet, chest: Chest Tightness, difbreath: Difficulty Breathing, difsleep: Difficulty Sleeping, bloat: Feeling Bloated, urinate: Problems with Urination, vomit: Vomitting, sob: Shortness of Breath, diarrhea: Diarrhea, sad: Feeling Sad, sweats: Sweats, sexual: Problems with Sexual Interest or Activity, worry: Worrying, itch: Itching, appetite: Lack of Appetite, abdominal: Abdominal Cramps, increaseapp: Increased Appetite, wtgain: Weight Gain, dizzy: Dizziness, swallow: Difficulty Swallowing, irritable: Feeling Irritable, mouthsore: Mouth Sore, wtloss: Weight Loss, hairloss: Hair Loss, constipat: Constipation, swelling: Swelling, taste: Change in the Way Food Tastes, myself: I Do Not Look Like Myself, skin: Changes in Skin.
Figure 4
Figure 4
The estimated networks of 38 cancer symptoms across the “distress” dimension (a) without the identified communities and (b) with the identified communities (walktrap algorithm). Nodes represent symptoms and edges represent a partial correlation between the symptoms, after controlling for all of the other correlations of a given node. The 38 cancer symptoms represented in the nodes above are coded in the following fashion: difcon: Difficulty Concentrating, pain: Pain, energy: Lack of Energy, cough: Cough, nervous: Feeling Nervous, hotflash: Hot Flashes, drymouth: Dry Mouth, nausea: Nausea, drowsy: Feeling Drowsy, numb: Numbness or Tingling in Hands or Feet, chest: Chest Tightness, difbreath: Difficulty Breathing, difsleep: Difficulty Sleeping, bloat: Feeling Bloated, urinate: Problems with Urination, vomit: Vomitting, sob: Shortness of Breath, diarrhea: Diarrhea, sad: Feeling Sad, sweats: Sweats, sexual: Problems with Sexual Interest or Activity, worry: Worrying, itch: Itching, appetite: Lack of Appetite, abdominal: Abdominal Cramps, increaseapp: Increased Appetite, wtgain: Weight Gain, dizzy: Dizziness, swallow: Difficulty Swallowing, irritable: Feeling Irritable, mouthsore: Mouth Sore, wtloss: Weight Loss, hairloss: Hair Loss, constipat: Constipation, swelling: Swelling, taste: Change in the Way Food Tastes, myself: I Do Not Look Like Myself, skin: Changes in Skin.
Figure 5
Figure 5
Centrality indices for the estimated network of 38 cancer symptoms shown in Figs 2a to 4a.

References

    1. Papachristou N, et al. Congruence between latent class and k-modes analyses in the identification of oncology patients with distinct symptom experiences. J Pain Symptom Manage. 2018;55:318–333. doi: 10.1016/j.jpainsymman.2017.08.020.
    1. Miaskowski C, et al. Latent class analysis reveals distinct subgroups of patients based on symptom occurrence and demographic and clinical characteristics. J Pain Symptom Manage. 2015;50:28–37. doi: 10.1016/j.jpainsymman.2014.12.011.
    1. Esther Kim JE, Dodd MJ, Aouizerat BE, Jahan T, Miaskowski C. A review of the prevalence and impact of multiple symptoms in oncology patients. J Pain Symptom Manage. 2009;37:715–736. doi: 10.1016/j.jpainsymman.2008.04.018.
    1. Miaskowski, C. et al. Advancing symptom science through symptom cluster research: Expert panel proceedings and recommendations. J. Natl. Cancer Inst. 109 (2017).
    1. Miaskowski C. Future directions in symptom cluster research. Semin Oncol Nurs. 2016;32:405–415. doi: 10.1016/j.soncn.2016.08.006.
    1. Barsevick A. Defining the symptom cluster: How far have we come? Semin Oncol Nurs. 2016;32:334–350. doi: 10.1016/j.soncn.2016.08.001.
    1. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D. Complex networks: Structure and dynamics. Phys. Reports. 2006;424:175–308. doi: 10.1016/j.physrep.2005.10.009.
    1. Albert R, Barabási AL. Statistical mechanics of complex networks. Reviews of modern physics. 2002;74:47. doi: 10.1103/RevModPhys.74.47.
    1. Strogatz SH. Exploring complex networks. Nature. 2012;410:268. doi: 10.1038/35065725.
    1. Wang RS, Maron BA, Loscalzo J. Systems medicine: evolution of systems biology from bench to bedside. Wiley Interdiscip Rev Syst Biol Med. 2015;7:141–161. doi: 10.1002/wsbm.1297.
    1. Loscalzo J, Barabasi AL. Systems biology and the future of medicine. Wiley Interdiscip Rev Syst Biol Med. 2011;3:619–627. doi: 10.1002/wsbm.144.
    1. Bringmann LF, Lemmens LH, Huibers MJ, Borsboom D, Tuerlinckx F. Revealing the dynamic network structure of the beck depression inventory-ii. Psychol. Med. 2015;45:747–757. doi: 10.1017/S0033291714001809.
    1. Fried EI, Epskamp S, Nesse RM, Tuerlinckx F, Borsboom D. What are ‘good’ depression symptoms? Comparing the centrality of dsm and non-dsm symptoms of depression in a network analysis. J. Affect Disord. 2016;189:314–320. doi: 10.1016/j.jad.2015.09.005.
    1. Frewen, P. A., Schmittmann, V. D., Bringmann, L. F. & Borsboom, D. Perceived causal relations between anxiety, posttraumatic stress and depression: extension to moderation, mediation, and network analysis. Eur J Psychotraumatol4 (2013).
    1. Robinaugh DJ, LeBlanc NJ, Vuletich HA, McNally RJ. Network analysis of persistent complex bereavement disorder in conjugally bereaved adults. J. Abnorm. Psychol. 2014;123:510–522. doi: 10.1037/abn0000002.
    1. Kossakowski JJ, et al. The application of a network approach to health-related quality of life (hrqol): introducing a new method for assessing hrqol in healthy adults and cancer patients. Qual. Life. Res. 2016;25:781–792. doi: 10.1007/s11136-015-1127-z.
    1. Zou, J. & Wang, E. Etumorrisk, an algorithm predicts cancer risk based on co-mutated gene networks in an individual’s germline genome. bioRxiv, 10.1101/393090 (2018).
    1. McNally RJ. Can network analysis transform psychopathology? Behav. Res. Ther. 2016;86:95–104. doi: 10.1016/j.brat.2016.06.006.
    1. Fried EI, et al. Mental disorders as networks of problems: a review of recent insights. Soc. Psychiatry Psychiatr. Epidemiol. 2017;52:1–10. doi: 10.1007/s00127-016-1319-z.
    1. Boschloo L, van Borkulo CD, Borsboom D, Schoevers RA. A prospective study on how symptoms in a network predict the onset of depression. Psychother. Psychosom. 2016;85:183–184. doi: 10.1159/000442001.
    1. Boschloo L, et al. The network structure of symptoms of the diagnostic and statistical manual of mental disorders. PLoS One. 2015;10:e0137621. doi: 10.1371/journal.pone.0137621.
    1. Borsboom D, Cramer AO. Network analysis: an integrative approach to the structure of psychopathology. Annu. Rev. Clin. Psychol. 2013;9:91–121. doi: 10.1146/annurev-clinpsy-050212-185608.
    1. Rhemtulla M, et al. Network analysis of substance abuse and dependence symptoms. Drug Alcohol. Depend. 2016;161:230–237. doi: 10.1016/j.drugalcdep.2016.02.005.
    1. Bhavnani SK, et al. The nested structure of cancer symptoms. implications for analyzing co-occurrence and managing symptoms. Methods Inf. Med. 2010;49:581–591. doi: 10.3414/ME09-01-0083.
    1. Fortunato S. Community detection in graphs. Phys. Rep. 2010;486:75–174. doi: 10.1016/j.physrep.2009.11.002.
    1. Qiao J, Meng YY, Chen H, Huang HQ, Li GY. Modeling one-mode projection of bipartite networks by tagging vertex information. Physica A: Statistical Mechanics and its Applications. 2016;457:270–279. doi: 10.1016/j.physa.2016.03.106.
    1. Epskamp, S., Maris, G. K., Waldorp, L. J. & Borsboom, D. Network psychometrics. arXiv preprint arXiv:1609.02818 (2016).
    1. Epskamp, S. & Fried, E. I. A tutorial on regularized partial correlation networks. Psychol Methods (2018).
    1. Koller, D. & Friedman, N. Probabilistic graphical models: principles and techniques (MIT press, 2009).
    1. McCorkle R. The measurement of symptom distress. Semin. Oncol. Nurs. 1987;3:248–256. doi: 10.1016/S0749-2081(87)80015-3.
    1. McCorkle R, Young K. Development of a symptom distress scale. Cancer Nurs. 1978;1:373–378. doi: 10.1097/00002820-197810000-00003.
    1. Portenoy RK, et al. Symptom prevalence, characteristics and distress in a cancer population. Qual. Life. Res. 1994;3:183–189. doi: 10.1007/BF00435383.
    1. Portenoy RK, et al. The Memorial Symptom Assessment Scale: an instrument for the evaluation of symptom prevalence, characteristics and distress. Eur. J. Cancer. 1994;30A:1326–1336. doi: 10.1016/0959-8049(94)90182-1.
    1. Miaskowski, C. et al. The symptom phenotype of oncology outpatients remains relatively stable from prior to through 1 week following chemotherapy. Eur J Cancer Care (Engl) 26 (2017).
    1. Wright F, et al. Inflammatory pathway genes associated with inter-individual variability in the trajectories of morning and evening fatigue in patients receiving chemotherapy. Cytokine. 2017;91:187–210. doi: 10.1016/j.cyto.2016.12.023.
    1. Kober KM, et al. Subgroups of chemotherapy patients with distinct morning and evening fatigue trajectories. Support. Care Cancer. 2016;24:1473–1485. doi: 10.1007/s00520-015-2895-2.
    1. Barabási, A. L. & Pósfai, M. Network science (Cambridge university press, 2016).
    1. Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: a tutorial paper. Behav. Res. Methods. 2018;50:195–212. doi: 10.3758/s13428-017-0862-1.
    1. Van Borkulo CD, et al. A new method for constructing networks from binary data. Sci. Rep. 2014;4:5918. doi: 10.1038/srep05918.
    1. Epskamp S, Cramer AO, Waldorp L, Schmittmann V, Borsboom D. qgraph: Network visualizations of relationships in psychometric data. J. Stat. Softw. 2012;48:1–18. doi: 10.18637/jss.v048.i04.
    1. Friedman, J., Hastie, T. & Tibshirani, R. glasso: Graphical lasso- estimation of gaussian graphical models, (2014).
    1. Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2008;9:432–441. doi: 10.1093/biostatistics/kxm045.
    1. Fruchterman T, Reingold E. Graph drawing by force-directed placement. Software: Practice and experience. 1991;21:1129–1164.
    1. Opsahl T, Agneessens F, Skvoretz J. Node centrality in weighted networks: Generalizing degree and shortest paths. Soc. Networks. 2010;32:245–251. doi: 10.1016/j.socnet.2010.03.006.
    1. Lewis-Beck, M., Bryman, A. & Liao, T. F. The Sage encyclopedia of social science research methods (Sage Publications, 2003).
    1. Orman, G. & Labatut, V. A comparison of community detection algorithms on artificial networks. In International Conference on Discovery Science, 242–256 (2009).
    1. Yang Z, Algesheimer R, Tessone CJ. A comparative analysis of community detection algorithms on artificial networks. Sci. Rep. 2016;6:30750. doi: 10.1038/srep30750.
    1. Rhodes VA, McDaniel RW, Homan SS, Johnson M, Madsen R. An instrument to measure symptom experience. symptom occurrence and symptom distress. Cancer Nurs. 2000;23:49–54. doi: 10.1097/00002820-200002000-00008.
    1. McClement SE, Woodgate RL, Degner L. Symptom distress in adult patients with cancer. Cancer Nurs. 1997;20:236–243. doi: 10.1097/00002820-199708000-00002.
    1. Brant JM, Beck S, Miaskowski C. Building dynamic models and theories to advance the science of symptom management research. J. Adv. Nurs. 2010;66:228–240. doi: 10.1111/j.1365-2648.2009.05179.x.
    1. Humphreys, J. et al. Middle range theory for nursing, chap. A middle range theory of symptom management, 141–164 (2014).
    1. Lenz ER, Pugh LC, Milligan RA, Gift A, Suppe F. The middle-range theory of unpleasant symptoms: an update. ANS Adv. Nurs. Sci. 1997;19:14–27. doi: 10.1097/00012272-199703000-00003.
    1. Lenz ER, Suppe F, Gift AG, Pugh LC, Milligan RA. Collaborative development of middle-range nursing theories: toward a theory of unpleasant symptoms. ANS Adv. Nurs. Sci. 1995;17:1–13. doi: 10.1097/00012272-199503000-00003.
    1. Tantoy IY, et al. Differences in symptom occurrence, severity, and distress ratings between patients with gastrointestinal cancers who received chemotherapy alone or chemotherapy with targeted therapy. J. Gastrointest. Oncol. 2017;8:109–126. doi: 10.21037/jgo.2017.01.09.
    1. Oksholm T, et al. Does age influence the symptom experience of lung cancer patients prior to surgery? Lung Cancer. 2013;82:156–161. doi: 10.1016/j.lungcan.2013.06.016.
    1. Hofsø K, Miaskowski C, Bjordal K, Cooper BA, Rustøen T. Previous chemotherapy influences the symptom experience and quality of life of women with breast cancer prior to radiation therapy. Cancer Nurs. 2012;35:167–177. doi: 10.1097/NCC.0b013e31821f5eb5.
    1. Farrell C, Brearley SG, Pilling M, Molassiotis A. The impact of chemotherapy-related nausea on patients’ nutritional status, psychological distress and quality of life. Support. Care Cancer. 2013;21:59–66. doi: 10.1007/s00520-012-1493-9.
    1. Molassiotis A, et al. Validation and psychometric assessment of a short clinical scale to measure chemotherapy-induced nausea and vomiting: the mascc antiemesis tool. J. Pain Symptom Manage. 2007;34:148–159. doi: 10.1016/j.jpainsymman.2006.10.018.
    1. Molassiotis A, Stricker CT, Eaby B, Velders L, Coventry PA. Understanding the concept of chemotherapy-related nausea: the patient experience. Eur. J. Cancer Care (Engl.) 2008;17:444–453. doi: 10.1111/j.1365-2354.2007.00872.x.
    1. Borsboom D. A network theory of mental disorders. World Psychiatry. 2017;16:5–13. doi: 10.1002/wps.20375.
    1. Borsboom D, Epskamp S, Kievit RA, Cramer AO, Schmittmann VD. Transdiagnostic networks: Commentary on nolen-hoeksema and watkins (2011) Perspect. Psychol. Sci. 2011;6:610–614. doi: 10.1177/1745691611425012.
    1. Bringmann LF, et al. A network approach to psychopathology: new insights into clinical longitudinal data. PLoS One. 2013;8:e60188. doi: 10.1371/journal.pone.0060188.
    1. Isvoranu AM, Borsboom D, van Os J, Guloksuz S. A network approach to environmental impact in psychotic disorder: Brief theoretical framework. Schizophr. Bull. 2016;42:870–873. doi: 10.1093/schbul/sbw049.
    1. Liu YY, Slotine JJ, Barabasi AL. Controllability of complex networks. Nature. 2011;473:167–173. doi: 10.1038/nature10011.
    1. Cramer AO, Waldorp LJ, van der Maas HL, Borsboom D. Comorbidity: a network perspective. Behav Brain Sci. 2010;33:137–150. doi: 10.1017/S0140525X09991567.
    1. Gonzalez BD, et al. Sleep disturbance in men receiving androgen deprivation therapy for prostate cancer: The role of hot flashes and nocturia. Cancer. 2018;124:499–506. doi: 10.1002/cncr.31024.
    1. Savard MH, Savard J, Caplette-Gingras A, Ivers H, Bastien C. Relationship between objectively recorded hot flashes and sleep disturbances among breast cancer patients: investigating hot flash characteristics other than frequency. Menopause. 2013;20:997–1005. doi: 10.1097/GME.0b013e3182885e31.
    1. Mazor M, et al. Differences in symptom clusters before and twelve months after breast cancer surgery. Eur. J. Oncol. Nurs. 2018;32:63–72. doi: 10.1016/j.ejon.2017.12.003.
    1. Sullivan CW, et al. Stability of symptom clusters in patients with breast cancer receiving chemotherapy. J. Pain Symptom Manage. 2018;55:39–55. doi: 10.1016/j.jpainsymman.2017.08.008.
    1. Wong ML, et al. Differences in symptom clusters identified using ratings of symptom occurrence vs. severity in lung cancer patients receiving chemotherapy. J. Pain Symptom Manage. 2017;54:194–203. doi: 10.1016/j.jpainsymman.2017.04.005.
    1. Huang J, et al. Symptom clusters in ovarian cancer patients with chemotherapy after surgery: A longitudinal survey. Cancer Nurs. 2016;39:106–116. doi: 10.1097/NCC.0000000000000252.
    1. Hwang KH, Cho OH, Yoo YS. Symptom clusters of ovarian cancer patients undergoing chemotherapy, and their emotional status and quality of life. Eur. J. Oncol. Nurs. 2016;21:215–222. doi: 10.1016/j.ejon.2015.10.007.

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