Skip to main content
Download PDF

Agreement between patient reported outcomes and clinical reports after radical prostatectomy - a prospective longitudinal study

BMC Urology volume 19, Article number: 35 (2019) Cite this article

Abstract

Background

In clinical research information can be retrieved through various sources. The aim is to evaluate the agreement between answers in patient questionnaires and clinical reports in a study of patients after radical prostatectomy and patient characteristics associated with agreement between these two data sources.

Methods

In the prospective non-randomized longitudinal trial LAParoscopic Prostatectomy Robot Open (LAPPRO) 4003 patients undergoing radical prostatectomy at 14 centers in Sweden were followed. Analysis of agreement is made using a variety of methods, including the recently proposed Gwet’s AC1, which enables us to handle the limitations of Cohen’s Kappa where agreement depends on the underlying prevalence.

Results

The incidence of postoperative events was consistently reported higher by the patient compared with the clinical reports for all outcomes. Agreement regarding the absence of events (negative agreement) was consistently higher than agreement regarding events (positive agreement) for all outcome variables. Overall impression of agreement depends on which measure used for the assessment. The previously reported desirable properties of Gwet’s AC1 as well as the patient characteristics associated with agreement were confirmed.

Conclusion

The differences in incidence and agreement across the different variables and time points highlight the importance of carefully assessing which source of information to use in clinical research.

Trial registration

ISRCTN06393679 (www.isrctn.com). Date of registration: 07/02/2008. Retrospectively registered.

Peer Review reports

Background

In clinical research information can be retrieved through various sources. Three commonly used sources of information are the patient themselves (self-reported), professional caregivers (clinical reports) and medical records. The preferred source depends on the specific research objective and the kind of information retrieved from the different sources. However, there may be different sources of information to choose from for several types of data such as morbidity, symptoms and health care utilization [1].

Medical conditions are commonly assessed retrospectively through patient interviews or patient charts [2,3,4,5,6,7]. When assessing outcomes after cancer surgery, documenting patient’s comorbidity is crucial as it may influence outcomes [8], such as recurrence [9], symptoms, complications [10] and bodily dysfunctions [11]. Another aim is documentation of health care utilization [12, 13]. Whereas studies comparing self-reported questionnaires with medical records are common [3, 6, 14,15,16,17], evaluation of clinical reports has received less attention [2, 6, 8].

Knowledge of quality characteristics such as agreement and reliability of different sources of information is important. Evaluating agreement between different sources contributes to the choice of source for information in clinical research. The focus in this study is on the agreement between patient questionnaires and case report form in a study of patients after radical prostatectomy. Assessing agreement by a single measure is often insufficient [18]. The use of measures of agreement for continuous outcomes was explored in a systematic review [19]. For categorical outcomes where data is classified according to concordant (positive/positive or negative/negative) and discordant (positive/negative or negative/positive) pairs, any recent systematic review is to our knowledge currently not available. However, according to Wongpakaran [20] and by our current review of the literature, many studies only report Cohen’s Kappa [21], sometimes combined with positive and negative agreement [18]. Kappa is a measure of the level of agreement in excess of chance, expressed as the relative difference in proportions of concordant pairs between observed and what would have occurred by chance. Positive and negative agreement estimates the conditional probability that, given that one of randomly selected rater makes a positive and negative rating, respectively, the other rater will also do so. A limitation with kappa is its dependence on the underlying prevalence, giving rise to for example low kappa values despite high percentage of agreement [18, 22]. This originates from how chance agreement is computed [23]. Gwet [24] proposed a statistic, named AC1, which is similar to kappa but use an estimator of chance agreement that is less dependent on the prevalence [20]. In the correction for chance agreement of kappa, it is assumed that all observed ratings may potentially agree by pure chance. In Gwet’s AC1, the likelihood of chance agreement is instead related to the proportion of ratings that may lead to an agreement. This portion does in turn depend on the observed marginal prevalence’s. This enables Gwet’s AC1 to avoid the problem of over or under correction that Kappa suffer from.

Agreement can be analyzed by generalized linear models for contingency tables [25]. Log-linear models are a class of models which to enable to quantify the contribution of row, column and diagonal effects as well as covariates on the outcome. The quasi-independence model [26] enables a greater degree flexibility and a more in-depth study of the structure of agreement than simple summary measures. For the situation where a source provides the true conditions (positive/negative), referred to as gold standard, the sensitivity and specificity of the different sources can be evaluated. Sensitivity and specificity measures the rate of cases being correctly classified as positive and negative, respectively.

The aim of this study was to evaluate the agreement between patient questionnaires and clinical reports in a study of patients after radical prostatectomy and to study patient characteristics associated with agreement between these two data sources.

Methods

Laparoscopic Prostatectomy Robot Open (LAPPRO) is an ongoing open, controlled and non-randomized prospective longitudinal trial comparing open retropubic and robot-assisted laparoscopic radical prostatectomy for localized prostate cancer at 14 centers in Sweden. The trial has been previously described in detail [27]. Patient scheduled for prostatectomy at the participating centers fulfilling the inclusion criteria (informed consent, age < 75 yr., ability to read and write Swedish, tumour stage cT1, cT2, or cT3 (TNM Classification of Malignant Tumors, [28]) with no signs of distant metastases, and a prostate-specific antigen level of < 20 ng/ml) were included.

Information was collected at 6–12 weeks, 12 and 24 months postoperatively both from case report forms by study personnel (either the operating urologist or clinical nurses) and from questionnaires filled out by the patients with regard to the following five outcomes: 1. swelling of groin or lower extremities, 2. complications and re-admissions, 3. re-operations, 4. added pharmacological therapy after surgery or due to local or distant recurrence and 5. local recurrence and metastases, see Fig. 1.

Fig. 1
figure 1

Flow chart of study procedures

Full size image

The questionnaires were based on concepts introduced in previous research projects [11, 29] and were content validated by experts in the field of urology and then face validated face with patients with prostate cancer. The case report forms were face validated with professional caregivers. The case report forms and questionnaires were further tested in a pilot study, after which final revisions were made [27]. The different questions in the case report forms and the questionnaires are presented in Supplement 1.

Definition of outcomes and predictors

In the derivation of the outcome variables a broad approach was used with binary outcomes where the presence (positive) or absence (negative) of at least one occurrence of interest, for example a readmission, was required to be reported in both sources (case report form and questionnaire) to reach agreement. A higher level of similarity, for example the exact number of readmissions, was not required. The reason is that the recall of the patient and the case report form may not refer to exactly the same time period. The derived outcomes can hence be grouped according to concordant (positive/positive or negative/negative) and discordant (positive/negative or negative/positive) pairs.

When the recall of the patient and the case report form refer to the same time period a higher agreement is more likely compared to when there is no defined time period. However, in the study the personnel did to some extent complete the clinical record form retrospectively by investigating medical records. Therefore the time period cannot be accurately assed and is not used in the analysis. Lastly, the events studied here were anticipated to have relatively low incidence.

Swelling of groin or lower extremities

At 6–12 weeks follow-up the case report form addressed signs of swelling (lymph oedema) in groin (left or right side) and lower extremities (left or right) with response categories “Yes”/“No”. At 3 months the questionnaire addressed feeling of swelling and heaviness in (left or right) groin or leg and heaviness in legs. A positive outcome was defined as “Yes” on at least one of the questions. A negative outcome was defined as responding No on all the questions. Otherwise the outcome was set to missing.

Complications, readmissions and reoperations

The case report form collected information on complications related to surgery, complications occurring after 6–12 weeks, and if the patient had been re-admitted to hospital for other reasons than cancer treatment later than the 12 month follow-up. At 3, 12 and 24 months the questionnaire included a question on whether the patient had contacted healthcare for a specified list of reasons. If any reason included pain from the surgical wound, lower or upper part of abdomen, bleeding from surgical wound, urinary tract or catheter it was defined as an event. There was also a question on whether that patient had been readmitted to hospital. For the questionnaire, a positive outcome was defined as responding “Yes” on any of the questions. For the case report form positive and negative outcome was defined as responding “Yes” and “No”, respectively. Responding “No information” and a non-response outcome were defined as missing.

At 12 and 24 months the case report form addressed whether the patients had been re-operated after 6–12 weeks and 12 months follow-up, respectively. The questionnaire addressed if the patient had been operation during the last 12 months. For both the case report form and the questionnaire a positive and negative outcome was defined by responding “Yes” and “No”, respectively. A non-response outcome was defined as missing.

For a yet unpublished report, [30] data on all readmissions within 3 months of surgery were collected from the Patient registry, Swedish Board of Health and Welfare. These data will be compared with the questionnaire and the CRF at 3 months.

Adjuvant therapy and local recurrence and metastases

Signs of local recurrence and detection of distant metastases were assessed in the case report form at 12 and 24 months and at the same follow-up times the patients were also asked about these matters. For both the case report form and the questionnaire a positive and negative outcome was defined by responding “Yes” and “No”, respectively. For a non-response the outcome was defined as missing.

Predictor variables

Patient characteristics and demography were collected through the questionnaires preoperatively and throughout the study. Preoperatively, information on age, education, occupation and marital status was collected and evaluated with regard to association with agreement [7, 13, 16]. In addition, use of medication, alcohol consumption, quality of life, depressed mood and presence of negative intrusive thoughts were also evaluated. Use of medication was defined as use of sleeping pills or tranquilizers. Self-assessed quality of life, negative intrusive thoughts and alcohol consumption were characterized in the same way as in an earlier analysis [31]. Depressive mood was defined as either responding ‘Yes’ to the question ‘Would you call yourself depressed?’ [32] or use of anti-depressive medication.

Sensitivity and specificity

Sensitivity and specificity will be evaluated for two scenarios with regard to choice of gold standard. First, the questionnaire will be considered as gold standard and the sensitivity and specificity of the clinical reports will be evaluated. Secondly, the Patient registry will be considered as standard regarding readmissions and the questionnaire and clinical report will be evaluated.

Statistical analysis

Group sizes in LAPPRO were set to evaluate urinary incontinence [27] and were judged to be sufficient to assess the current aim. Agreement was evaluated by percent of concordant pairs, positive and negative agreement and Gwet’s AC1. For comparison the kappa coefficient was computed as well. Association was evaluated by the odds that the two observers agree rather than disagree using the marginal quasi-independence model. Due to the hierarchical design where the surgeons are operating on several patients, who are longitudinally followed, there are dependency structures in the data that should ideally be accounted for in the statistical model. However, due to computational difficulties a standard fixed effect model was estimated separately at each time point.

In the evaluation of factors associated with agreement (positive or negative) between the two data sources, the following were evaluated: age, education, occupation, marital status, medication (sleeping pills or tranquilizers), alcohol consumption, quality of life, depressed mood and negative intrusive thoughts. For swelling of groin or lower extremities a standard simple logistic regression was used. For the outcomes with repeated measures a random intercept logistic regression model was used and time was included as a fixed effect ([26]). Results were presented with 95% confidence intervals. In each of the analyses, for information to be evaluable, data from both sources had to be ‘non-missing’ according to the definitions described above. The same analyses were made for the additional comparisons with data from the Patient registry. Analyses were conducted in SAS v9.4 (SAS Institute Inc., Cary NC), the rel package [33] and the software described by [34].

Results

For the 3706 eligible patients, the number of patients with evaluable data from both case report forms and questionnaires varied between the different questions from 3385 (91%) for swelling of groin and lower extremities at 3 months to 1884 (51%) for complications and readmissions at 24 months (Fig. 2). Missing information was consistently higher in the case report forms and increased at later follow-up (Table 1).

Fig. 2
figure 2

Flow chart of patients included in the study

Full size image
Table 1 Missing data
Full size table

Patient characteristics and demography

Patient characteristics are reported in Table 2. The median age of the patients was 63 years. Thirty-eight percent were retired and 84% were married/cohabiting.

Table 2 Patient characteristics
Full size table

Agreement between case report forms and patient reported data

With the exception of local recurrence and metastases, all events were reported to a higher degree by the patient reports compared with the case report form (Table 3). The incidence of swelling of groin or lower extremities was 1 and 24% as reported by the case report form and questionnaire, respectively. Gwet’s AC1 was relatively stable and varied between 0.62 and 0.96 across outcomes and time points. Both kappa and the odds of agreement varied across a much wider range. Negative agreement was consistently higher than positive agreement for all the outcome variables.

Table 3 Evaluation of agreement between case report forms and patient reported data
Full size table

There was relatively high negative agreement across all variables and time points (84–97%) which rendered high odds of agreement. However, due to the low positive agreement for most of the variables and the low incidence as reported by the case report form, the kappa values were in general low. Gwet’s AC1 was less affected by the incidence. Agreement regarding additional (chemo) radiotherapy had higher agreement compared with the other variables.

Both reoperations and recurrence at 12 months had a relatively low incidence. However, despite being similar with regard to concordant pairs (91 and 95%, respectively) and negative agreement (95 and 97%, respectively), there was a large discrepancy in kappa (0.38 and 0.22) and odds of agreement (52 and 21) attributed to differences in incidence and positive agreement. Gwet’s AC1 were similar, 0.90 and 0.94, respectively. A similar pattern was observed at 24 months as well as for the comparison between additional (chemo) radiotherapy after surgery or due to recurrence at 12 and 24 months.

For the scenario where the questionnaire is regarded as gold standard the sensitivity of the case report form varied considerable between variables. Specificity was more stable at a high level which means that the case report forms have a higher likelihood of identifying absence rather than presence of events (Table 4).

Table 4 Evaluation of sensitivity and specificity of case report forms
Full size table

Agreement with patient registry data

In the comparisons of the questionnaire and CRF with Patient registry data, the estimated readmission rate was 1083 (29%), 373 (10%) and 291 (8%), respectively. The questionnaire had lower agreement with the registry compared with the CRF. The questionnaire had slightly higher sensitivity than the CRF. The CRF had higher specificity (Table 5).

Table 5 Re-admission within 3 months after surgery. Evaluation of agreement and sensitivity and specificity between patient registry and case report forms and patient reported data
Full size table

Factors associated with agreement

Being retired, using sleeping pills and/or tranquilizers as well as reporting an impaired quality of life, depressed mood and negative intrusive thoughts were all associated with a lower agreement for several of the outcome variables (Table 6). Being married/cohabiting was associated with a higher degree of agreement for swelling of the groin. Age was associated with reoperations, (chemo) radiotherapy and local recurrence and metastases with high age yielding poor agreement. No association was found for education or alcohol consumption.

Table 6 Evaluation of patient characteristics associated with agreement between patient self-assessed questionnaires and case report forms
Full size table

Discussion

This study indicates that in a clinical trial, patients in general report a higher frequency of events than professional caregivers do. In the current study of patients after radical prostatectomy, with the exception of tumor recurrence, the incidence of various postoperative symptoms or events after radical prostatectomy for prostate cancer was consistently more frequently reported by the patients than in clinical reports. Missing information was consistently higher in the case report forms and increased at later follow-up.

Positive agreement was consistently lower than negative agreement for all the outcome variables. This is probably due to the relatively low incidence estimates and the large discrepancies in estimates between the patient and the clinical reports. Whereas both kappa and the odds of agreement varied across a wide range across the different variables and time points, despite other data characteristics of agreement being relatively similar, Gwet’s AC1 was relatively stable, which confirm previous findings [20, 24].

Accuracy and agreement between the different modes depends on what information is being collected [8]. For symptoms as well as events such as readmissions, an advantage with the questionnaire is that data is collected directly from the patient it concerns. However, the accuracy of recall may be questionable, especially in a retrospective setting with a long recall period [1]. Patients are more likely to recall a disease requiring a surgical or intensive pathologic/laboratory diagnostic procedure than a disease without such a procedures [14]. The time elapsed since the illness occurred and the seriousness of the disease influence agreement [3]. Diabetes has generally high agreement whereas chronic obstructive pulmonary disease and diseases with less explicit diagnostic criteria have poor agreement [6, 15]. For medical records recall bias is less of an issue as information is generally prospectively documented during a hospital admission that is data is recorded instantaneously in the course of time. A major limitation is that medical records may not cover the necessary information relevant for specific research objectives and underestimate symptoms based conditions [35]. They may also lack coverage as they miss patients not seeking health care or patients seeking health care in another county, or due to differences in reporting inpatient and outpatient visits [36]. An advantage with clinical reports is that they can be fit for purpose as opposed to medical records. A drawback is that information is filtered through the physician [11]. In the collection of signs, different types of medical examinations by an experienced clinician may be the only viable option.

The patient may be less prone to report complications when the clinical personnel asks for information compared with when he or she completes an anonymous questionnaire at home [37]. In this trial questionnaires were sent out and returned to a third party, the trial secretariat and not to the hospital/department where surgery was performed [27]. The information documented in the clinical record form was collected when clinical personnel met the patient during the follow-up meetings as well as from medical records. This probably explains the observed high agreement between the clinical record forms and the patient registry. A higher agreement between patient reports and medical records compared with patient reports versus physician reports has also been observed [8].

It has been found that patients with prostate cancer reported a higher incidence of symptoms such as fatigue compared to their physicians [38, 39]. A similar pattern was found in [40] but with a higher degree of agreement. Several study design features may contribute to these differences as discussed in [40].

Several of the studies ([5, 6, 9, 14, 16]) have used medical records as the gold standard enabling assessment of sensitivity and specificity. However, it may be an invalid assumption for other settings [35]. For the scenario where the Patient registry was regarded as the gold standard in reporting readmissions, the case report forms had a higher agreement and specificity compared to the questionnaire, whereas the questionnaire had a slightly higher sensitivity.

In our study some patient characteristics were found to be associated with agreement such as age, socioeconomic factors and depressed mood, which confirmed previous results [7, 13, 16]. Younger patients, who had no self-reported impairment in quality of life, depressed mood or negative intrusive thoughts, appear to report symptoms events more in agreement with those reported by clinical personnel. However, the association with medication (sleeping pills and/or tranquilizers) has not been previously reported as far as we know.

At later follow ups (12 and 24 months) the compliance in submitting clinical record forms was significantly lower compared with compliance regarding patient reports. One reason for this could be that part of the cohort was referred for surgery to a department of urology some, or even a long, distance from their home. This would be expected to result in a lower surgeon-patient physical follow-up (out-patients visit). Other contributing explanations could be that after 12 or 24 months the follow-up was not always by the operating urologist, and thus completion of the CRF may have been missed.

This study has both strengths and limitations. Strengths include the large study cohort, the longitudinal design, a high compliance of patients and the use of validated questionnaires [27]. Limitations include a lack of information of the specific personnel who completed the case report forms at the different visits and on the specific time periods the case report forms and questionnaires covered. Difficulties in being able to account for the longitudinal structure in the statistical model must be regarded as a limitation.

Conclusions

The differences in incidence and agreement across the different variables and time points highlight the importance of carefully assessing which source of information to use in clinical research. This study confirms the importance of using several measures to assess the degree of agreement between the sources. The previously reported benefits of Gwet’s AC1 are confirmed and researchers should be encouraged to consider this method. In clinical research, much effort is often devoted to increasing patient response rates. However, preventing missing data in clinical reports also needs further attention. Long-term follow-up should make use of patient reports, as clinical record forms tend to be missed by the health care. As different patient characteristics were found to increase agreement, such background information is relevant for the choice of data collection procedure.

Abbreviations

CRF:

Case Report Form

LAPPRO:

Laparoscopic Prostatectomy Robot Open trial

References

  1. Harlow SD, Linet MS. Agreement between questionnaire data and medical records. The evidence for accuracy of recall. Am J Epidemiol. 1989;129(2):233–48.

    Article  CAS  Google Scholar 

  2. de Groot, V., et al., How to measure comorbidity. A critical review of available methods. J Clin Epidemiol, 2003. 56(3): p. 221–9.

  3. Jones MP, et al. Concordance between Sources of Morbidity Reports: Self-Reports and Medical Records. Front Pharmacol. 2011;2:16.

    Article  CAS  Google Scholar 

  4. Bush TL, et al. Self-report and medical record report agreement of selected medical conditions in the elderly. Am J Public Health. 1989;79(11):1554–6.

    Article  CAS  Google Scholar 

  5. Haapanen N, et al. Agreement between questionnaire data and medical records of chronic diseases in middle-aged and elderly Finnish men and women. Am J Epidemiol. 1997;145(8):762–9.

    Article  CAS  Google Scholar 

  6. Merkin SS, et al. Agreement of self-reported comorbid conditions with medical and physician reports varied by disease among end-stage renal disease patients. J Clin Epidemiol. 2007;60(6):634–42.

    Article  Google Scholar 

  7. Corser W, et al. Concordance between comorbidity data from patient self-report interviews and medical record documentation. BMC Health Serv Res. 2008;8:85.

    Article  Google Scholar 

  8. De-loyde KJ, et al. Which information source is best? Concordance between patient report, clinician report and medical records of patient co-morbidity and adjuvant therapy health information. J Eval Clin Pract. 2015;21(2):339–46.

    Article  Google Scholar 

  9. Phillips KA, et al. Agreement between self-reported breast cancer treatment and medical records in a population-based breast Cancer family registry. J Clin Oncol. 2005;23(21):4679–86.

    Article  Google Scholar 

  10. Wallerstedt A, et al. Short-term results after robot-assisted laparoscopic radical prostatectomy compared to open radical prostatectomy. Eur Urol. 2015;67(4):660–70.

    Article  Google Scholar 

  11. Steineck G, et al. Symptom documentation in cancer survivors as a basis for therapy modifications. Acta Oncol. 2002;41(3):244–52.

    Article  Google Scholar 

  12. Wallihan DB, Stump TE, Callahan CM. Accuracy of self-reported health services use and patterns of care among urban older adults. Med Care. 1999;37(7):662–70.

    Article  CAS  Google Scholar 

  13. Raina P, et al. Agreement between self-reported and routinely collected health-care utilization data among seniors. Health Serv Res. 2002;37(3):751–74.

    Article  Google Scholar 

  14. Clegg LX, et al. Comparison of self-reported initial treatment with medical records: results from the prostate cancer outcomes study. Am J Epidemiol. 2001;154(6):582–7.

    Article  CAS  Google Scholar 

  15. Zhu K, et al. Comparison of self-report data and medical records data: results from a case-control study on prostate cancer. Int J Epidemiol. 1999;28(3):409–17.

    Article  CAS  Google Scholar 

  16. Okura Y, et al. Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol. 2004;57(10):1096–103.

    Article  Google Scholar 

  17. Barber J, et al. Measuring morbidity: self-report or health care records? Fam Pract. 2010;27(1):25–30.

    Article  Google Scholar 

  18. Cicchetti DV, Feinstein AR. High agreement but low kappa: II. Resolving the paradoxes. J Clin Epidemiol. 1990;43(6):551–8.

    Article  CAS  Google Scholar 

  19. Zaki R, et al. Statistical methods used to test for agreement of medical instruments measuring continuous variables in method comparison studies: a systematic review. PLoS One. 2012;7(5).

    Article  CAS  Google Scholar 

  20. Wongpakaran N, et al. A comparison of Cohen’s kappa and Gwet’s AC1 when calculating inter-rater reliability coefficients: a study conducted with personality disorder samples. BMC Med Res Methodol. 2013;(13):61.

  21. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20(1):37–46.

    Article  Google Scholar 

  22. Feinstein AR, Cicchetti DV. High agreement but low kappa. 1. The problems of theTwo paradoxes. J Clin Epidemiol. 1990;43(6):543–9.

    Article  CAS  Google Scholar 

  23. Gwet KL. Kappa statistic is not satisfactory for assessing the extent of agreement between raters. Statistical Methods for Inter-Rater Reliability Assessment. 2002;1(6):1–6.

    Google Scholar 

  24. Gwet KL. Handbook of inter-rater reliability. 4 ed. In: Advanced analytics; 2010.

    Google Scholar 

  25. Tanner MA, Young MA. Modeling Agreement Among Raters. J Am Stat Assoc. 1985;80(389):175–80.

    Article  Google Scholar 

  26. Agresti A. Categorical data analysis. 2 ed. Wiley series in probability and mathematical statistics. Hoboken, New Jersey: Wiley-Interscience; 2002.

    Google Scholar 

  27. Thorsteinsdottir T, et al. LAPPRO: a prospective multicentre comparative study of robot-assisted laparoscopic and retropubic radical prostatectomy for prostate cancer. Scand J Urol Nephrol. 2011;45(2):102–12.

    Article  Google Scholar 

  28. Gospodarowicz MK, Brierly JD, Wittekind C. TNM classification of malignant Tumours: Wiley-Blackwell; 2017.

  29. Johansson E, et al. Long-term quality-of-life outcomes after radical prostatectomy or watchful waiting: the Scandinavian prostate Cancer Group-4 randomised trial. Lancet Oncol. 2011;12(9):891–9.

    Article  Google Scholar 

  30. Wallerstedt Lantz A, et al. 90-day readmission after radical prostatectomy - a prospective comparison between robot-assisted and open surgery. Scand J Urol. 2018; to appear.

  31. Bock D, et al. Habits and self-assessed quality of life, negative intrusive thoughts and depressed mood in patients with prostate cancer: a longitudinal study. Scand J Urol. 2017;51(5):353–9.

    Article  CAS  Google Scholar 

  32. Skoogh J, et al. 'A no means no'--measuring depression using a single-item question versus hospital anxiety and depression scale (HADS-D). Ann Oncol. 2010;21(9):1905–9.

    Article  CAS  Google Scholar 

  33. Team RC. R: a language and environment for statistical computing. R Foundation for statistical computing. Vienna, Austria; 2018.

  34. Mackinnon A. A spreadsheet for the calculation of comprehensive statistics for the assessment of diagnostic tests and inter-rater agreement. Comput Biol Med. 2000;30(3):127–34.

    Article  CAS  Google Scholar 

  35. Skinner KM, et al. Concordance between respondent self-reports and medical records for chronic conditions: experience from the veterans health study. J Ambul Care Manage. 2005;28(2):102–10.

    Article  Google Scholar 

  36. Katz JN, et al. Can comorbidity be measured by questionnaire rather than medical record review? Med Care. 1996;34(1):73–84.

    Article  CAS  Google Scholar 

  37. Mansson A, et al. Neutral third party versus treating institution for evaluating quality of life after radical cystectomy. Eur Urol. 2004;46(2):195–9.

    Article  CAS  Google Scholar 

  38. Litwin, M.S., et al., Differences in urologist and patient assessments of health related quality of life in men with prostate cancer: results of the CaPSURE database. J Urol, 1998. 159(6): p. 1988–92.

    Article  CAS  Google Scholar 

  39. Sonn GA, et al. Differing perceptions of quality of life in patients with prostate Cancer and their doctors. J Urol. 2009;182:2296–302.

    Article  Google Scholar 

  40. Svaboe Steinsvik EA, et al. Do perceptions of adverse events differ between patients and physicians? Findings from a randomized, controlled trial of radical treatment for prostate Cancer. J Urol. 2010;184:525–31.

    Article  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the participants in the LAPPRO trial, the members of the steering committee, the investigators at the participating hospitals and the personnel at the trial secretariat for their provision of study material and administrative support.

Funding

This study was supported by research grants from the Swedish Cancer Society (2008/922, 2010/593, 2013/497, 2016/362), The Swedish Research Council (2012–1770, 2015–02483), Region Västra Götaland, Sahlgrenska University Hospital (ALF grants 138751, 146201 and 4307771, HTA–VGR 6011; agreement concerning research and education of doctors), the Mrs. Mary von Sydow Foundation, the Anna and Edvin Berger Foundation and the Assar Gabrielsson’s Foundation (FB 16–24, 17–18). These funds were used in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

The datasets used and/or analyzed during the current study are available in a format without direct or indirect identifiers from the corresponding author on reasonable request.

Author information

Authors and Affiliations

  1. Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, SSORG (Scandinavian Surgical Outcomes Research Group), Sahlgrenska University Hospital/Östra, 416 85, Gothenburg, Sweden

    David Bock, Eva Angenete, Sofie Walming & Eva Haglind

  2. Health Metrics Unit, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    David Bock

  3. Department of Urology, Skåne University Hospital, Malmö, Sweden

    Anders Bjartell

  4. Department of Urology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden

    Jonas Hugosson

  5. Division of Clinical Cancer Epidemiology, Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg, Sweden

    Gunnar Steineck

  6. Department of Molecular Medicine and Surgery, Section of Urology, Karolinska Institutet, Stockholm, Sweden

    Peter Wiklund

Authors
  1. David Bock
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eva Angenete
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anders Bjartell
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jonas Hugosson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gunnar Steineck
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sofie Walming
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Peter Wiklund
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Eva Haglind
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Contributors to the conception and design of the study, or acquisition of data, or analysis and interpretation of data: DB, EH, GS, AB, JH and PW; drafting the article or revising it critically for important intellectual content: DB, EA, AB, JH, GS, SW, PW and EH; final approval of the version to be submitted: DB, EA, AB, JH, GS, SW, PW and EH. DB takes responsibility that this study has been reported honestly, accurately and transparently; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.

Corresponding author

Correspondence to David Bock.

Ethics declarations

Ethics approval and consent to participate

In compliance with national guidelines the regional ethics review board of the study secretariat, Gothenburg, Sweden, approved the study (approval 277–07). Written informed consent was obtained from all participants in the trial.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bock, D., Angenete, E., Bjartell, A. et al. Agreement between patient reported outcomes and clinical reports after radical prostatectomy - a prospective longitudinal study. BMC Urol 19, 35 (2019). https://doi.org/10.1186/s12894-019-0467-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12894-019-0467-3

Keywords

  • Agreement
  • Questionnaire
  • Prostate cancer
  • Prostatectomy
  • Case-report form

BMC Urology

ISSN: 1471-2490

Contact us