The Heidelberg Decision Aid for Patients With Lung Cancer (HELP)

Findings of a Randomized Controlled Trial

Background: Advanced lung cancer typifies the challenges of shared decision-making in oncology. With a limited prognosis for survival, the increasingly numerous and complex treatment options must continually be weighed against issues of fragility, quality of life, and the end of life.

Methods: This randomized, controlled trial, carried out on 138 patients, concerned the use of a decision aid combined with decision coaching, versus standard care. The primary endpoint was clarity of the patient’s personal attitude, as assessed on the Decisional Conflict Scale. The secondary endpoints were self-efficacy, decisional conflict, perceived preparedness and participation in decision-making, and anxiety/depression. The data were analyzed with descriptive statistics and intergroup comparisons. The trial was entered into the German registry of clinical trials (DRKS00028023).

Results: No statistically significant difference with regard to the primary endpoint (clarity of the patient’s personal attitude concerning the decision) was found in a comparison between the intervention group and the control group (IG: median/IQR: 41.67/47.92; CG: median/IQR: 33.33/43.75; p = 0.35). The descriptive statistics revealed a high level of decisional conflict in the overall group of study participants: 57.6% had a very high level of decisional conflict, composed in particular of the dimensions of feeling inadequately informed (64.4%) and of uncertainty (58.9%). Most participants judged the intervention to be helpful in preparing them to make a decision.

Conclusion: Even though the intervention was perceived as helpful preparation for decision-making, it did not bring about any improvement in the high level of decisional conflict. With the continual development of new treatments and the associated increase in prognostic uncertainty, there is an important role for individualized patient information and the training of physicians in how to deal with uncertainty.

Even though guidelines recommend strengthening patient orientation by fostering the patient’s participation in decision-making, nationwide implementation strategies are still missing (1, 2, 3, 4, 5).

The European Respiratory Society has recently published a recommendation for the use of patient decision tools in its guideline on various aspects of quality in lung cancer care. The authors highlight the urgent need for further research to support the development and effectiveness evaluation of such tools and particularly point out the lack of decision aids for patients with lung cancer (6). And this despite the fact that metastatic lung cancer typifies the challenges of participatory decision-making in oncology. As this cancer mostly affects older adults, it is often associated with particular fragility (7). In recent years, new cancer treatments have been developed at a rapid pace, resulting in improvements in prognosis for subgroups and fundamental changes to treatment algorithms (8). This increasing complexity poses a major challenge to those providing care for cancer patients. Thus, it is crucial in discussions with lung cancer patients to continuously weigh the hope for successful cancer treatments against issues of fragility, quality of life, and the end of life so that, for example, palliative care can timely be integrated in the overall treatment strategy.

The first step of the HELP project was to develop a decision aid for patients, their relatives and care providers. In this article, we present the findings of the trial conducted to evaluate its effectiveness.

Methods

Designed as a randomized, controlled trial, this open, single-center, two-arm superiority study had “clarity of the patient’s personal attitude “ as the primary endpoint. A subscale of the Decisional Conflict Scale (DCS) was used for data collection (9). Stratified block randomization (1:1) was used to randomly assign participants to the intervention or standard care group, based on the preferred decision style (paternalistic, participatory or informative) at study inclusion, assuming that the effect of the intervention is dependent on this patient characteristic (Figure 1). The secondary endpoints included general decisional conflict and its dimensions (Decisional Conflict Scale, DCS), self-efficacy (Self-efficacy Scale, DSES) (10), preparation for decision-making (Preparation for Decision-Making Scale, PDMS) (11), patient involvement (Patient Involvement in Care Scale, PICS) (12), quality of life (EQ-5D-5L) (13), and anxiety/depression (Hospital Anxiety and Depression Scale, HADS) (14).

Figure 1

Study procedure—CONSORT flow chart

The study was conducted in the Department of Internal Oncology, Thorax Clinic at Heidelberg University Hospital (UKHD); with about 1000 primary cases per year, the department is one of the largest lung cancer centers in Germany. The inclusion criteria comprised age ≥ 18 years, capacity to consent, sufficient proficiency in German, diagnosis of metastatic lung cancer (stage IV) and stage IIIB/IIIC without the option of surgical treatment. Patients were recruited at the time of first diagnosis or disease progression after treatment. All participants provided written informed consent. The trial was conducted in accordance with the Helsinki Declaration and was approved by the Ethics Committee of the Medical Faculty of Heidelberg University (S977/2021) and registered at the German Registry of Clinical Trials (DRKS00028023).

A non-stratified Mann-Whitney U test was used for sample size planning, as the assumptions regarding the individual strata were still uncertain in the planning phase. A sample size of 71 participants was calculated based on the assumption of a relevant difference of 10 points in the primary endpoint (range of the scale 0–100) and a standard deviation of 20.6, obtained from the literature (9, 15), in order to demonstrate the assumed difference at a two-sided significance level of 5% with a power of 80%. Taking into account a dropout rate of approximately 20%, we planned to include 71/(1–0.20) = 89 participants in each group (total sample size of 178). For the calculation, we used the PASS sample size software v16.0.4 with 100 000 simulation runs.

We performed a descriptive analysis of all participant data collected. The primary endpoint—the sum score of the dimension “clarity of the patient’s personal attitude“ of the DCS—was analyzed using group comparisons (nonparametric comparison: van Elteren test), taking the preferred decision style used for the stratified randomization (paternalistic, participatory or informative) in account. The results are reported below along with descriptive p-values. A p-value of less than 0.05 is considered statistically significant. The analysis of the secondary endpoints was conducted exclusively on an exploratory basis and also based on group comparisons, using the van Elteren test adjusted for stratified randomization. In cases with missing primary endpoint data, multiple imputation was performed, using predictive mean matching. The analysis variables “treatment arm” and “decision style” were taken into account when performing the imputation. A total of 1000 imputed datasets are generated; for each of them, ten iterations are performed and “29082024” is used as the seed. Subsequently, the p-values of the van Elteren tests as well as the median, Q1 and Q3 values were pooled in both groups by means of median generation. The statistical software packages SPSS (version 27, IBM) and R (version 4.0, http://r-project.org) were used for the analyses.

Intervention

The intervention included the use of a decision aid specifically developed for this trial and was accompanied by a decision coaching session. The decision aid is a brochure (available both paper-based and as a web application) which queries information needs and decision style (preference in participation) and identifies the patient’s personal values. In addition, the brochure includes an overview of the types of cancer treatments available, including palliative care. Physicians and decision coaches received communication training that comprised a preparatory online module, followed by a shared-decision-making workshop of several hours with simulation patients, special interview techniques and the use of the decision aid. Further information on study planning and procedure can be found in the eBox and the published study protocol (16). The decision aid is provided as eSupplement (eSupplements 1 + 2).

eBox

Supplementary study description

Results

From April 2022 to March 2023, a total of 138 participants were included in the trial and randomized. The mean age of the patient sample studied was 67 years (range 46–88 years), with 57.2% of the participants being male. 84.1% of the patients had stage IV lung cancer. In 87% of patients, the observed treatment decision was made in connection with disease progression. With regard to the personal decision type, a participatory, paternalistic and informative type was reported by 60.9%, 27.6% and 11.6% of participants, respectively (eTable). The informative type is solely responsible for the decision (information model), i.e. the physician provides the entire medical information and abstains from the decision-making process. A higher dropout rate of 0.33 was observed for the 138 patients included in the trial. Even though the recruitment period was extended, the initially calculated sample size of 178 was not achieved. In a total of 47 patients, the primary endpoint was not observed and instead imputed, using the procedure described above. No statistically significant difference with regard to the primary endpoint (clarity of the patient’s personal attitude concerning the decision) was found as the result of the intervention in a comparison between the intervention group and the control group (intervention group n = 66; median/IQR: 41.67/47.92; control group: median/IQR: 33.33/43.75; p = 0.35) (Table). The descriptive analysis of the DCS data found in 57.6% a high level of decisional conflict, with feeling inadequately informed (64.4%) and uncertainty (58.9%) as the key issues. In both study arms, high levels of decisional conflict were revealed (intervention group: 41.25; control group: 38.65; high level of decisional conflict defined as values >37.5) (Figure 2). Following the intervention, the participants evaluated it, using the PDMS questionnaire (n = 40). On a 5-point Likert scale, the intervention has in particular (taking into account the answer options “to some extent”, “quite a lot” and “a lot”): “prepared you for the next appointment with your doctor “ (83.7%), “prepared you to talk to your doctor about what matters most matters most to you“ (81.6%), “helped you to think about the extent to which you want to participate in the decision” (81.6%), “made it clear that the decision depends on what matters most to you “ (81.6%) (Figure 3).

Figure 2

Frequency distribution of the Decisional Conflict Scale and their subscales for the entire group of patients (n = 92)

Figure 3

Evaluation of the intervention using the Preparation for Decision-Making Scale (PDMS-D) (German version)

Table

Medians and intergroup comparisons of the primary and secondary endpoints

eTable

Patient characteristics (baseline information)

Discussion

The statistical analysis of the data from this randomized, controlled trial found not significant difference for the primary endpoint between the intervention and control groups. The Decisional Conflict Scale, which we used in this trial, is an instrument for assessing the quality of medical decisions. It has been available in particular for the evaluation of decision aids for almost 30 years. The underlying assumption is that effective decisions are characterized by a low-level of decisional conflict and made in line with personal values (9, 17). The HELP trial revealed a high level of decisional conflict in patients with advanced lung cancer. The measured DCS scores are considered a high level of decisional conflict and, to our knowledge, have not previously been reported at this level. In contrast to other types of cancer, such as breast cancer or prostate cancer, only a few papers on decision-making in lung cancer have been published so far, possibly due to the fact that treatment options have been considerable more limited until recently. Additionally, studies on the decisional conflict in older adults, who account for the majority of lung cancer patients, are rare (18). The few studies that specifically address the decisional conflict in lung cancer patients are limited to special settings, e.g., treatment decisions in early stages of the disease (19) or prophylactic cranial irradiation (PCI) in patients with small cell lung cancer (SCLC) (20). The fact that advanced lung cancer is characterized by a very high level of physical, psychosocial and existential burden could be one reason for the high level of decisional conflict in these patients (21). The emotional response to being diagnosed with lung cancer is commonly associated with a feeling of powerlessness. As a result, the burden associated with the disease may not be communicated and thus lead to cancer treatment decisions characterized by passive acceptance of aggressive treatment options (22).Our own research (23) in preparation of the HELP trial supports these statements as it showed that lung cancer patients commonly regard the few available cancer treatment options as offering no alternative and that even the perception of actual decision-making is missing. This situation brings about a real risk of over-treatment and disregarding the timely integration of palliative care.

In our trial, the decisional conflict was primarily due to a strong feeling of being uninformed. Cancer patients’ need for information has been growing steadily over the last few decades and often it is left unfulfilled (24, 25).This need for information takes many forms and differs greatly between individuals wanting disease-specific information, both in terms of the desired extent and depth, especially in patients with advanced cancer (26). Early studies reported a rather passive information behavior (22) and a reluctance to address end of life-related issues (27) among patients with lung cancer, possible partly due to stigmatization (28). In contrast, the majority of participants in our study stated that it was a burden to them to not have enough information and that they wished to participate in decision-making. In recent years, the rapid increase in the number of new types of treatment has made the provision of adequate information even more challenging. Based on current study data, the European Society of Medical Oncology highlighted in 2021 the fact that patients often feel overwhelmed by the speed of this development and do not sufficiently understand these new treatments (29). This has a negative impact on the doctor-patient communication, for example, in the perception of the prognosis. Since helping patients to achieve a realistic understanding of their prognosis is a key element of the communication with patients with advanced cancer, this point requires special attention. In 2010, Temel et al. published a study on early integration of palliative care in the oncological management of patients with metastatic lung cancer which highlighted the fact that the intervention led to a realistic understanding of the prognosis and had a positive impact by reducing the number of patients exposed to aggressive treatments at the end of life (30). However, more recent studies have revealed that the identification of a molecular target increases the proportion of lung cancer patients with an unrealistic expectation with regard to their prognosis (27% compared to 17% among patients without molecular alteration) (31). Since the introduction of immunotherapies, this misjudgment has only become more prevalent. In a study by McLouth et al., almost half of the participants wrongly assumed a curative treatment goal. One of the consequences was that only a very small number of patients received adequate palliative care at the end of life (32).

This raises another issue linked to the high level of decisional conflict observed in our trial: uncertainty. A German study revealed uncertainty about the future as the most important issue related to the support needs of patients with lung cancer (33). Thus, the prognostic uncertainty surrounding the new treatments is certainly posing an increasing challenge. This is compounded by uncertainties experienced by clinicians in the face of the rapid development of treatment algorithms. A survey of the American Society of Clinical Oncology found that more than one third of physicians working in thoracic oncology had difficulties keeping up with developments. There is a significant correlation between uncertainty and poorer communication about the prognosis (34).

When interpreting the results of our trial, a number of important limitations must be taken into account. First of all, the failure to recruit the planned sample size, the high dropout rate and the high level of selection are to be mentioned. Because of these limitations, it is not possible to conclusively determine the effect of the intervention. Furthermore, our study was designed as a single-center trial conducted at a highly specialized center—certainly limiting the generalizability of the findings. Another weakness results from the fact that the algorithms for lung cancer treatment changed continuously and relevantly during the study period. For this reason, we did not choose a specific decision situation. All patients with advanced lung cancer were included in the trial, regardless of histology or molecular pathological alterations and in various phases of the disease, from first diagnosis to several lines of therapy. This explains the high degree of heterogeneity in the study population. There is certainly a need to develop additional decision aids for specific disease situations once a further consolidation of therapeutic algorithms has taken place.

Apart from the high level of decisional conflict experienced by the patients, an important finding of our trial was that the participants were positive about the decision aid and the supplementary decision coaching. Since decisions in patients with advanced disease should be made based on the preferences and values of the patients, it is relevant that most of the patients in the intervention group became aware of the fact that a decision had to be made and that this decision was dependent on their personal needs. In addition, the intervention fostered self-reflection and raised patient awareness of the personal degree of participation in the treatment decision and their own preferences. In patients with advanced disease, however, this confrontation can also have negative effects. In some studies, the level of decisional conflict even increased after the intervention (20) or the process was described as upsetting (35). For this aspect, our study does not show a deterioration in anxiety and depression. It is worth noting that there is still uncertainty as to the ideal timing of the DCS measurement (36). Furthermore, the perception of an “effective” decision can be influenced by the fact that the treatment has not (yet) been successful. Similarly, it certainly takes time until patients have achieved clarity of their personal attitude in complex, palliative settings, commonly characterized by ambivalence or fluctuations (37).

Conclusion

Clinicians should adopt a pro-active attitude when addressing values, individual preferences and participation in decision-making. However, given the challenging developments in the field of cancer therapy, the importance of dynamic adaptation and the provision of personalized information is growing. Tools such as our decision aid support the reflection of personal values and the awareness that there is an important decision to be made and the possibility of participation. However, if the implementation of these insights is not pro-actively supported by physicians in discussions with patients, they have no adequate effect. The same applies to offering palliative care interventions. The study findings indicate that the issue of “dealing with uncertainty“ is of special importance and should be strengthened in the communication training for shared decision-making that is required of physicians involved in cancer care. In particular the space, which should be opened when the available evidence is complex and it is not possible to determine with certainty what would be a “right“ or “wrong” decision, can create an opportunity to address existential worries and conflicts; something, that is often described as inadequate in modern medicine (38, 39).

Funding

This trial was funded by BMS Stiftung Immunonkologie from October 2021 to June 2023. The foundation did not influenced the research content and the research process.

The research comprised the following work packages: preparation of the study protocol and application to the ethics committee; training of physicians and decision coaches; recruiting for RCT/data collection/data entry, data analysis/data interpretation/discussion, preparation and conduct of a symposium, and publications.

Data sharing

The authors are open to enquiries from researchers directed to the corresponding author [e-mail]. Anonymized data can be provided three months to two years after publication in response to proposed analyses which are methodologically sound.

Conflict of interest statement
The authors declare no conflict of interest.

Manuscript received on 15 May 2024, revised version accepted on 17 October 2024

Translated from the original German by Ralf Thoene, M.D.

Corresponding author

Dr. med. Matthias Villalobos

Thoraxklinik Heidelberg

Röntgenstraße 1, 69126 Heidelberg, Germany

Cite this as:
Villalobos M, Unsöld L, Deis N, Behnisch R, Siegle A, Thomas M: The Heidelberg decision aid for patients with lung cancer (HELP)—findings of a randomized, controlled trial. Dtsch Arztebl Int 2024; 121: 861–7.

DOI: 10.3238/arztebl.m2024.0228

References

1. Nationaler Krebsplan: https://www.bundesgesundheitsministerium.de/fileadmin/Dateien/5_Publikationen/Praevention/Broschueren/Broschuere_Nationaler_Krebsplan.pdf. https://www.bundesgesundheitsministerium.de/themen/praevention/nationaler-krebsplan.html (last accessed on 10 May 2024).

2. Bieber C, Loh A, Ringel N, Eich W, Härter M: Manual zur Partizipativen Entscheidungsfindung (Shared Decision-making) Heidelberg: Selbstverlag 2007.

3. Härter M, Buchholz A, Nicolai J, et al.: Shared decision making and the use of decision aids. Dtsch Arztebl Int 2015; 112: 672–9.

4. Rosca A, Krones T, Biller-Andorno N: Shared decision making: patients have a right to be informed about possible treatment options and their risks and benefits. Swiss Med Wkly 2020; 150: w20268.

5. Hahlweg P, Bieber C, Brutt AL, et al.: Moving towards patient-centered care and shared decision-making in Germany. Z Evid Fortbild Qual Gesundhwes 2022; 171: 49–57

6. Blum TG, Morgan RL, Durieux V, et al.: European Respiratory Society guideline on various aspects of quality in lung cancer care. Eur Respir J 2023; 61: 2103201

7. Komici K, Bencivenga L, Navani N, et al.: Frailty in patients with lung cancer: a systematic review and meta-analysis. Chest 2022; 162: 485–97.

8. Schuler M, Bölükbas S, Darwiche K, Theegarten D, Herrmann K, Stuschke M: Personalized treatment for patients with lung cancer. Dtsch Arztebl Int 2023; 120: 300–10.

9. Buchholz AHL, Kriston L, Simon D, Härter M: Die decisional conflict scale in deutscher Sprache (DCS-D) — Dimensionale Struktur in einer Stichprobe von Hausarztpatienten. Klin Diagn Eval 2011; 4: 15–30.

10. Bunn H, O’Connor A: Validation of client decision-making instruments in the context of psychiatry. Can J Nurs Res 1996; 28: 13–27

11. Buchholz ASD, Härter M: Die preparation for decision-making scale in deutscher Sprache (PDMS-D) Faktorielle und konkurrente Validität in einer Onlinestichprobe von Personen mit Kreuzschmerz oder Depression. Klin Diagnostik u Evaluation 2011; 4: 31–45.

12. Scheibler F, Freise D, Pfaff H: Einbeziehung von Patienten in die Behandlung: Validierung der deutschen PICS-Skalen. J Public Health 2004; 12: 199–209.

13. Herdman M, Gudex C, Lloyd A, et al.: Development and preliminary testing of the new five-level version of EQ-5D-5L. Qual Life Res 2011; 20: 1727–36.

14. Zigmond AS, Snaith RP: The hospital anxiety and depression scale. Acta Psychiatr Scand 1983; 67: 361–70.

15. Rahn AC, Jull J, Boland L, et al.: Guidance and/or decision coaching with patient decision aids: scoping reviews to inform the International Patient Decision Aid Standards (IPDAS). Med Decis Making 2021; Vol.41: 938–53

16. Deis N, Unsold L, Siegle A, et al.: HELP—Heidelberg decision aid for lung cancer patients: a randomized controlled clinical trial. Trials 2023; 24: 356.

17. O’Connor A: Validation of a decisional conflict scale. Med Decis Making 1995; 15: 25–30.

18. Kates JM: Treatment-related decisional conflict, quality of life, and comorbidity in older adults with cancer. Asia Pac J Onc Nurs 2018; 5: 421–9

19. Mokhles S, Nuyttens JJME, de Mol M, et al.: Treatment selection of early stage non-small cell lung cancer: the role of the patient in clinical decision making. BMC Cancer 2018; 18: 79.

20. Ankolekar A, De Ruysscher D, Reymen B, et al.: Shared decision-making for prophylactic cranial irradiation in extensive-stage small-cell lung cancer: an exploratory study. Transl Lung Cancer Res 2021; 10: 3120–3131.

21. Morrison EJ, Novotny PJ, Sloan JA, et al.: Emotional problems, quality of life, symptom burden in patients with lung cancer. Clin Lung Cancer 2017; 18: 497–503.

22. Stanze H, Schneider N, Nauck F, Marx G: „I can´t get it into my head that I have cancer…”—a qualitative interview study on needs of patients with lung cancer. PLoS One 2019; 14: e0216778.

23. Unsöld L, Deis N, Siegle A, et al.: „There is no alternative.“ Treatment decision-making in lung cancer patients with limited prognosis: results of a qualitative interview study. Eur J Cancer Care 2024.

24. Rutten LJ, Arora NK, Bakos AD, Aziz N, Rowland J: Information needs and sources of information among cancer patients: a systematic review of research (1980–2003). Patient Educ Couns 2005; 57: 250–61.

25. Hart NH, Crawford-Williams F, Crichton M, et al.: Unmet supportive care needs of people with advanced and their caregivers: a systematic scoping review. Crit Rev Oncol Hematol 2022; 176: 103728.

26. Russel BJ, Ward AM: Deciding what information is necessary: do patients with advanced cancer want to know all the details? Cancer Manag Res 2011; 3: 191–9.

27. Horne G, Seymour J, Payne S: Maintaining integrity in the face of death: a grounded theory to explain the perspectives of people affected by lung cancer about the expression of wishes for end of life care. Int J Nurs Stud 2012; 49: 718–26.

28. Cataldo JK, Brodsky JL: Lung cancer stigma, anxiety, depression and symptom severity. Oncology 2013; 85: 33–40.

29. ESMO Congress 2021 press release: patients and non-oncology doctors struggle to keep up with the pace of change in oncology, research shows. https://www.esmo.org/newsroom/press-and-media-hub/esmo-media-releases/patients-and-non-oncology-doctors-struggle-to-keep-up-with-the-pace-of-change-in-oncology-research-show (last accessed on 10 May 2024).

30. Temel JS, Greer JA, Muzikansky A, et al.: Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med 2010; 363: 733–42.

31. Kuon J, Blasi M, Unsöld L, et al.: Impact of molecular alterations on quality of life and prognostic understanding over time in patients with incurable lung cancer: a multicenter, longitudinal, prospective cohort study. Support Care Cancer 2022; 30: 3131–40.

32. McLouth LE, Gabbard J, Levine BJ, et al.: Prognostic awareness, palliative care use, and barriers to palliative care in patients undergoing immunotherapy of chemo-immunotherapy for metastatic lung cancer. J Palliat Med 2023; 26: 831–6.

33. Kuon J, Vogt J, Mehnert A, et al.: Symptoms and needs of patients with advanced lung cancer: early prevalence assessment. Oncol Res Treat 2019; 42: 650–9

34. Habib AR, Chen R, Magnavita ES, et al.: Prevalence and tolerance of prognostic uncertainty among thoracic oncologists. Oncologist 2021; 26: e1480–2.

35. Leighl NB, Shepherd FA, Zawiska D, et al.: Enhancing treatment decision-making: pilot study of a treatment decision aid in stage IV non-small cell lung cancer. Br J Cancer 2008; 98: 1769–73.

36. Garvelink MM, Boland L, Klein, et al.: Decisional conflict scale use over 20 years: the anniversary review. Med Decis Making 2019; 39: 301–14.

37. Broom A, Parker RB, Kenny K: Authenticity, ambivalence and recognition in caring at the end of life and beyond. Soc Sci Med 2019; 239: 112554.

38. Agledahl KM, Gulbrandsen P, Førde R, Wifstad Å: Courteous but not curious: how doctors´ politeness masks their existential neglect. A qualitative study of video-recorded patient consultations. J Med Ethics 2011; 37: 650–4.

39. Assing Hvidt E, Sondergaard J, Hansen DG, et al.: „We are the barriers“: Danish general practitioners´ interpretations of why existential and spiritual dimensions are neglected in patient care. Commun Med 2017; 14: 108–20.