The long-term risk of prostate cancer–specific mortality (PCSM) after radical prostatectomy is poorly defined for patients treated in the era of widespread prostate-specific antigen (PSA) screening. Models that predict the risk of PCSM are needed for patient counseling and clinical trial design.
A multi-institutional cohort of 12,677 patients treated with radical prostatectomy between 1987 and 2005 was analyzed for the risk of PCSM. Patient clinical information and treatment outcome was modeled using Fine and Gray competing risk regression analysis to predict PCSM.
Fifteen-year PCSM and all-cause mortality were 12% and 38%, respectively. The estimated PCSM ranged from 5% to 38% for patients in the lowest and highest quartiles of predicted risk of PSA-defined recurrence, based on a popular nomogram. Biopsy Gleason grade, PSA, and year of surgery were associated with PCSM. A nomogram predicting the 15-year risk of PCSM was developed, and the externally validated concordance index was 0.82. Neither preoperative PSA velocity nor body mass index improved the model's accuracy. Only 4% of contemporary patients had a predicted 15-year PCSM of greater than 5%.
Few patients will die from prostate cancer within 15 years of radical prostatectomy, despite the presence of adverse clinical features. This favorable prognosis may be related to the effectiveness of radical prostatectomy (with or without secondary therapy) or the low lethality of screen-detected cancers. Given the limited ability to identify contemporary patients at substantially elevated risk of PCSM on the basis of clinical features alone, the need for novel markers specifically associated with the biology of lethal prostate cancer is evident.
A preoperative nomogram is an effective tool for assessing the risk of disease progression following radical prostatectomy for localized prostate cancer. To better understand the performance of nomograms for patients with a low PSA, we examined whether patients with PSA < 2.5 had different outcomes versus that predicted by a validated preoperative nomogram.
A cohort of 6130 patients from two referral centers was analyzed. Kaplan-Meier methods were used to estimate the recurrence-free probabilities based on PSA grouping (< 2.5 vs ≥ 2.5 ng/mL). Cox proportional hazards regression was used to evaluate whether PSA grouping was associated with biochemical recurrence controlling for preoperative nomogram probability.
A total of 399/6130 (6.5%) patients had PSA < 2.5. Patients with PSA ≤ 0.5 had a high rate of non-organ confined disease (33% vs. 15% for PSA 0.6 – 2.5). The median follow-up for recurrence-free patients was 2.4 years, and 10 patients with PSA < 2.5 and 597 patients with PSA > 2.5 recurred (total 607/6130). With adjustment for the preoperative nomogram probability, there was no significant difference in recurrence by PSA grouping (hazard ratio 0.78 for PSA <2.5 vs ≥2.5; 95% C.I. 0.42, 1.48; p=0.5).
Patients with a low PSA comprise a small proportion of those treated, and the majority have palpable disease. Patients with especially low PSA values (≤ 0.5) have a high rate of non-organ confined disease. We saw no evidence that patients with low PSA have worse outcomes, after stage and grade were taken into account.
prostate cancer; PSA; nomogram
Cancer is a growth process and it is natural that we should be concerned with how the routinely used marker of prostate cancer tumour burden – PSA – changes over time. Such change is measured by PSA velocity or PSA doubling time, described in general as “PSA kinetics”. However, it turns out that calculation of PSA velocity and doubling time is far from straightforward. More than 20 different methods have been proposed, and many of these give quite divergent results. There is clear evidence that PSA kinetics are critical for understanding prognosis in advanced or relapsed prostate cancer. However, PSA kinetics have no value for men with an untreated prostate: neither PSA velocity nor doubling time have any role in diagnosing prostate cancer or providing a prognosis for men before treatment.
Prostatic neoplasms; prostate specific antigen
Although acupuncture is widely used for chronic pain, there remains considerable controversy as to its value. We aimed to determine the effect size of acupuncture for four chronic pain conditions: back and neck pain, osteoarthritis, chronic headache, and shoulder pain.
We conducted a systematic review to identify randomized trials of acupuncture for chronic pain where allocation concealment was determined unambiguously to be adequate. Individual patient data meta-analyses were conducted using data from 29 of 31 eligible trials, with a total of 17,922 patients analyzed.
In the primary analysis including all eligible trials, acupuncture was superior to both sham and no acupuncture control for each pain condition (all p<0.001). After exclusion of an outlying set of trials that strongly favored acupuncture, the effect sizes were similar across pain conditions. Patients receiving acupuncture had less pain, with scores 0.23 (95% C.I. 0.13, 0.33), 0.16 (95% C.I. 0.07, 0.25) and 0.15 (95% C.I. 0.07, 0.24) standard deviations lower than sham controls for back and neck pain, osteoarthritis, and chronic headache respectively; the effect sizes in comparison to no acupuncture controls were 0.55 (95% C.I. 0.51, 0.58), 0.57 (95% C.I. 0.50, 0.64) and 0.42 (95% C.I. 0.37, 0.46). These results were robust to a variety of sensitivity analyses, including those related to publication bias.
Acupuncture is effective for the treatment of chronic pain and is therefore a reasonable referral option. Significant differences between true and sham acupuncture indicate that acupuncture is more than a placebo. However, these differences are relatively modest, suggesting that factors in addition to the specific effects of needling are important contributors to the therapeutic effects of acupuncture.
Recent evidence shows that acupuncture is effective for chronic pain. However we do not know whether there are characteristics of acupuncture or acupuncturists that are associated with better or worse outcomes.
An existing dataset, developed by the Acupuncture Trialists’ Collaboration, included 29 trials of acupuncture for chronic pain with individual data involving 17,922 patients. The available data on characteristics of acupuncture included style of acupuncture, point prescription, location of needles, use of electrical stimulation and moxibustion, number, frequency and duration of sessions, number of needles used and acupuncturist experience. We used random-effects meta-regression to test the effect of each characteristic on the main effect estimate of pain. Where sufficient patient-level data were available, we conducted patient-level analyses.
When comparing acupuncture to sham controls, there was little evidence that the effects of acupuncture on pain were modified by any of the acupuncture characteristics evaluated, including style of acupuncture, the number or placement of needles, the number, frequency or duration of sessions, patient-practitioner interactions and the experience of the acupuncturist. When comparing acupuncture to non-acupuncture controls, there was little evidence that these characteristics modified the effect of acupuncture, except better pain outcomes were observed when more needles were used (p=0.010) and, from patient level analysis involving a sub-set of five trials, when a higher number of acupuncture treatment sessions were provided (p<0.001).
There was little evidence that different characteristics of acupuncture or acupuncturists modified the effect of treatment on pain outcomes. Increased number of needles and more sessions appear to be associated with better outcomes when comparing acupuncture to non-acupuncture controls, suggesting that dose is important. Potential confounders include differences in control group and sample size between trials. Trials to evaluate potentially small differences in outcome associated with different acupuncture characteristics are likely to require large sample sizes.
Statistical prediction tools are increasingly common in contemporary medicine but there is considerable disagreement about how they should be evaluated. Three tools (Partin tables, the European Society for Urological Oncology (ESUO) criteria and the Gallina nomogram) have been proposed for the prediction of seminal vesicle invasion (SVI) in patients with clinically localized prostate cancer. We aimed to determine which of these tool, if any, should be used clinically.
The independent validation cohort consisted of 2584 patients treated surgically for clinically localized prostate cancer between 2002 and 2007 at one of four North American tertiary-care referral centers. Traditional (area-under-the-receiver-operating-characteristic-curve (AUC), calibration plots, the Brier score, sensitivity and specificity, positive and negative predictive value) and novel (risk stratification tables, the net reclassification index, decision curve analysis and predictiveness curves) statistical methods quantified the predictive abilities of the three tested models.
Traditional statistical methods (receiver operating characteristic (ROC) plots and Brier scores), as well as two of the novel statistical methods (risk stratification tables and the net reclassification index) could not provide clear distinction between the SVI prediction tools. For example, receiver operating characteristic (ROC) plots and Brier scores seemed biased against the binary decision tool (ESUO criteria) and gave discordant results for the continuous predictions of the Partin tables and the Gallina nomogram. The results of the calibration plots were discordant with those of the ROC plots. Conversely, the decision curve clearly indicated that the Partin tables represent the ideal strategy for stratifying the risk of SVI.
Based on decision curve analysis results, surgeons should consider using the Partin tables to predict SVI. Decision curve analysis provided clinically meaningful comparisons between predictive models; other statistical methods for evaluation of prediction models gave inconsistent results that were difficult to interpret.
prostate; prostatic neoplasms; prostatectomy; seminal vesicles; algorithms; statistics
Spiritual well-being and sense of meaning are important concerns for clinicians who care for patients with cancer. We developed Individual Meaning-Centered Psychotherapy (IMCP) to address the need for brief interventions targeting spiritual well-being and meaning for patients with advanced cancer.
Patients and Methods
Patients with stage III or IV cancer (N = 120) were randomly assigned to seven sessions of either IMCP or therapeutic massage (TM). Patients were assessed before and after completing the intervention and 2 months postintervention. Primary outcome measures assessed spiritual well-being and quality of life; secondary outcomes included anxiety, depression, hopelessness, symptom burden, and symptom-related distress.
Of the 120 participants randomly assigned, 78 (65%) completed the post-treatment assessment and 67 (56%) completed the 2-month follow-up. At the post-treatment assessment, IMCP participants demonstrated significantly greater improvement than the control condition for the primary outcomes of spiritual well-being (b = 0.39; P <.001, including both components of spiritual well-being (sense of meaning: b = 0.34; P = .003 and faith: b = 0.42; P = .03), and quality of life (b = 0.76; P = .013). Significantly greater improvements for IMCP patients were also observed for the secondary outcomes of symptom burden (b = −6.56; P < .001) and symptom-related distress (b = −0.47; P < .001) but not for anxiety, depression, or hopelessness. At the 2-month follow-up assessment, the improvements observed for the IMCP group were no longer significantly greater than those observed for the TM group.
IMCP has clear short-term benefits for spiritual suffering and quality of life in patients with advanced cancer. Clinicians working with patients who have advanced cancer should consider IMCP as an approach to enhance quality of life and spiritual well-being.
Prostate cancer is a heterogenous disease with a variable natural history that is not accurately predicted by currently used prognostic tools.
We genotyped 798 prostate cancer cases of Ashkenazi Jewish ancestry treated for localized prostate cancer between June 1988 and December 2007. Blood samples were prospectively collected and de-identified before being genotyped and matched to clinical data. The survival analysis was adjusted for Gleason score and PSA. We investigated associations between 29 single nucleotide polymorphisms (SNPs) and biochemical recurrence, castration-resistant metastasis, and prostate cancer-specific survival. Subsequently, we performed an independent analysis using a high resolution panel of 13 SNPs.
On univariate analysis, 2 SNPs were associated (p<0.05) with biochemical recurrence; 3 SNPs were associated with clinical metastases; and 1 SNP was associated with prostate cancer-specific mortality. Applying a Bonferroni correction (p<0.0017), one association with biochemical recurrence (p=0.0007) was significant. Three SNPs showed associations on multivariable analysis, although not after correcting for multiple testing. The secondary analysis identified an additional association with prostate cancer-specific mortality in KLK3 (p<0.0005 by both univariate and multivariable analysis).
We identified associations between prostate cancer susceptibility SNPs and clinical endpoints. The rs61752561 in KLK3 and rs2735839 in the KLK2-KLK3 intergenic region associated strongly with prostate cancer-specific survival, and rs10486567 in 7JAZF1 gene associated with biochemical recurrence. A larger study will be required to independently validate these findings and determine the role of these SNPs in prognostic models.
Single nucleotide polymorphisms; Prostate cancer; Prognosis
Background and Purpose
Published outcomes of pelvic lymph node dissection (PLND) during robot-assisted laparoscopic prostatectomy (RALP) demonstrate significant variability. The purpose of the study was to compare PLND outcomes in patients at risk for lymph node involvement (LNI) who were undergoing radical prostatectomy (RP) by different surgeons and surgical approaches.
Patients and Methods
Institutional policy initiated on January 1, 2010, mandated that all patients undergoing RP receive a standardized PLND with inclusion of the hypogastric region when predicted risk of LNI was ≥2%. We analyzed the outcomes of consecutive patients meeting these criteria from January 1 to September 1, 2010 by surgeons and surgical approach. All patients underwent RP; surgical approach (open radical retropubic [ORP], laparoscopic [LRP], RALP) was selected by the consulting surgeon. Differences in lymph node yield (LNY) between surgeons and surgical approaches were compared using multivariable linear regression with adjustment for clinical stage, biopsy Gleason grade, prostate-specific antigen (PSA) level, and age.
Of 330 patients (126 ORP, 78 LRP, 126 RALP), 323 (98%) underwent PLND. There were no significant differences in characteristics between approaches, but the nomogram probability of LNI was slightly greater for ORP than RALP (P=0.04). LNY was high (18 nodes) by all approaches; more nodes were removed by ORP and LRP (median 20, 19, respectively) than RALP (16) after adjusting for stage, grade, PSA level, and age (P=0.015). Rates of LNI were high (14%) with no difference between approaches when adjusted for nomogram probability of LNI (P=0.15). Variation in median LNY among individual surgeons was considerable for all three approaches (11–28) (P=0.005) and was much greater than the variability by approach.
PLND, including hypogastric nodal packet, can be performed by any surgical approach, with slightly different yields but similar pathologic outcomes. Individual surgeon commitment to PLND may be more important than approach.
Evidence of reduced prostate cancer mortality from randomized trials in Europe supports early detection of prostate cancer with prostate-specific antigen (PSA). Yet PSA screening has generated considerable controversy: it is far from clear that the benefits outweigh risks, in terms of overdiagnosis and overtreatment. One way to shift the ratio of benefits to harms is to focus on men at highest risk, who have more to benefit than average. Neither family history nor any of the currently identified genomic markers offer sufficient risk stratification for practical use. However, there is considerable evidence that the levels of PSA in blood are strongly prognostic of the long-term risk of aggressive prostate cancer. Specifically, it is difficult to justify continuing to screen men age 60 or older if they have a PSA less than 1 or 2 ng/ml; for men 45 – 60, intervals between PSA tests can be based on PSA levels, with 2 to 4 year re-testing interval for men with PSA of 1 ng/ml or higher, and tests every 6 to 8 years for men with PSA < 1 ng/ml. Men with the top 10% of PSAs at a young age (PSA ~1.5 ng / ml or higher below 50) are at particularly high risk and should be subject to intensive monitoring.
prostatic neoplasms; early detection of cancer; prostate-specific antigen
The National Comprehensive Cancer Network and American Urological Association guidelines on early detection of prostate cancer recommend biopsy on the basis of high prostate-specific antigen (PSA) velocity, even in the absence of other indications such as an elevated PSA or a positive digital rectal exam (DRE).
To evaluate the current guideline, we compared the area under the curve of a multivariable model for prostate cancer including age, PSA, DRE, family history, and prior biopsy, with and without PSA velocity, in 5519 men undergoing biopsy, regardless of clinical indication, in the control arm of the Prostate Cancer Prevention Trial. We also evaluated the clinical implications of using PSA velocity cut points to determine biopsy in men with low PSA and negative DRE in terms of additional cancers found and unnecessary biopsies conducted. All statistical tests were two-sided.
Incorporation of PSA velocity led to a very small increase in area under the curve from 0.702 to 0.709. Improvements in predictive accuracy were smaller for the endpoints of high-grade cancer (Gleason score of 7 or greater) and clinically significant cancer (Epstein criteria). Biopsying men with high PSA velocity but no other indication would lead to a large number of additional biopsies, with close to one in seven men being biopsied. PSA cut points with a comparable specificity to PSA velocity cut points had a higher sensitivity (23% vs 19%), particularly for high-grade (41% vs 25%) and clinically significant (32% vs 22%) disease. These findings were robust to the method of calculating PSA velocity.
We found no evidence to support the recommendation that men with high PSA velocity should be biopsied in the absence of other indications; this measure should not be included in practice guidelines.
New markers may improve prediction of diagnostic and prognostic outcomes. We review various measures to quantify the incremental value of markers over standard, readily available characteristics. Widely used traditional measures include the improvement in model fit or in the area under the receiver operating characteristic (ROC) curve (AUC). New measures include the net reclassification index (NRI) and decision–analytic measures, such as the fraction of true positive classifications penalized for false positive classifications (‘net benefit’, NB).
For illustration we discuss a case study on the presence of residual tumor versus benign tissue in 544 patients with testicular cancer. We assessed 3 tumor markers (AFP, HCG, and LDH) for their incremental value over currently standard clinical predictors. AUC and R2 values suggested adding continuous LDH and AFP whereas NB only favored HCG as a potentially promising marker at a clinically defendable decision threshold of 20% risk. Results based on the NRI fell in the middle, suggesting reclassification potential of all three markers.
We conclude that improvement in standard discrimination measures, which focus on finding variables that might be promising across all decision thresholds, may not detect the most informative markers at a specific threshold of particular clinical relevance. When a marker is intended to support decision making, calculation of the improvement in a decision–analytic measure, such as NB, is preferable over an overall judgment as obtained from the AUC in ROC analysis.
prediction; logistic regression model; performance measures; incremental value
Although case-control studies have identified numerous single nucleotide polymorphisms (SNPs) associated with prostate cancer, the clinical role of these SNPs remains unclear.
Evaluate previously identified SNPs for association with prostate cancer and accuracy in predicting prostate cancer in a large prospective population-based cohort of unscreened men.
Design, setting, and participants
This study used a nested case-control design based on the Malmö Diet and Cancer cohort with 943 men diagnosed with prostate cancer and 2829 matched controls. Blood samples were collected between 1991 and 1996, and follow-up lasted through 2005.
We genotyped 50 SNPs, analyzed prostate-specific antigen (PSA) in blood from baseline, and tested for association with prostate cancer using the Cochran-Mantel-Haenszel test. We further developed a predictive model using SNPs nominally significant in univariate analysis and determined its accuracy to predict prostate cancer.
Results and limitations
Eighteen SNPs at 10 independent loci were associated with prostate cancer. Four independent SNPs at four independent loci remained significant after multiple test correction (p < 0.001). Seven SNPs at five independent loci were associated with advanced prostate cancer defined as clinical stage ≥T3 or evidence of metastasis at diagnosis. Four independent SNPs were associated with advanced or aggressive cancer defined as stage ≥T3, metastasis, Gleason score ≥8, or World Health Organization grade 3 at diagnosis. Prostate cancer risk prediction with SNPs alone was less accurate than with PSA at baseline (area under the curve of 0.57 vs 0.79), with no benefit from combining SNPs with PSA. This study is limited by our reliance on clinical diagnosis of prostate cancer; there are likely undiagnosed cases among our control group.
Only a few previously reported SNPs were associated with prostate cancer risk in the large prospective Diet and Cancer cohort in Malmö, Sweden. SNPs were less useful in predicting prostate cancer risk than PSA at baseline.
Prostate cancer; Biomarkers; SNPs; PSA; Sensitivity and specificity
The performance of prediction models can be assessed using a variety of different methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic (ROC) curve), and goodness-of-fit statistics for calibration.
Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision–analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.
We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n=544 for model development, n=273 for external validation).
We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for making clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
To assess variation of total prostate-specific antigen (tPSA), free PSA (fPSA), percent fPSA, human glandular kallikrein 2 (hK2), and intact PSA measured three times within two weeks. Knowledge of the variation in an individual’s PSA level is important for clinical decision-making.
Patients and Methods
Study participants were 149 patients referred for prostate biopsy, of which 97 had benign disease and 52 had prostate cancer. Three blood samples were drawn with a median of four hours between first and second samples and 12 days between first and third samples. Variability was described by absolute differences, ratios and intra-individual coefficients of variation. Total PSA, fPSA, hK2, and intact PSA were measured in anti-coagulated blood plasma.
At baseline, the median tPSA was 6.8 (IQR 4.5, 9.6) ng/mL. The intra-individual variation was low for all biomarkers, and lowest for tPSA. For 80% of participants, the ratio between first and second time points for tPSA was between 0.91 and 1.09 and the ratio for percent fPSA was between 0.89 and 1.15. Total coefficients of variation between time 1 and 2 for tPSA, fPSA, percent fPSA, hK2 and intact PSA were 4.0%, 6.6%, 6.0%, 9.2%, and 9.5%, respectively. The measurements taken several days apart varied more than those taken on the same day, but the variation between both time points were not large.
The intra-individual variation for all the kallikrein-like markers studied was relatively small, especially for samples drawn the same day. Few cases are reclassified between the time points. This indicates high short-term biological and technical reproducibility of the tests in clinical use.
Free PSA; Prostate cancer; PSA; Screening; Variation
The introduction of total prostate specific antigen (total PSA) testing in blood has revolutionized the detection and management of men with prostate cancer (PCa). The objective of this review was to discuss the challenges of PCa biomarker research, definition of the type of PCa biomarkers, the statistical considerations for biomarker discovery and validation, and to review the literature regarding total PSA velocity and novel blood-based biomarkers.
An English-language literature review of the Medline database (1990 to August 2010) of published data on blood-based biomarkers and PCa was undertaken.
The inherent biological variability of total PSA levels affects the interpretation of any single result. Men who will eventually develop PCa have increased total PSA levels years or decades before the cancer is diagnosed. Total PSA velocity improves predictiveness of total PSA only marginally, limiting its value for PCa screening and prognostication. The combination of PSA molecular forms and other biomarkers improve PCa detection substantially. Several novel blood-based biomarkers such as human glandular kallikrein 2 (hK2), urokinase plasminogen activator (uPA) and its receptor (uPAR), transforming growth factor-beta 1 (TGF-β1); interleukin-6 (IL-6) and its receptor (IL-6R) may help PCa diagnosis, staging, prognostication, and monitoring. Panels of biomarkers that capture the biologic potential of PCa are in the process of being validated for PCa prognostication.
PSA is a strong prognostic marker for long-term risk of clinically relevant cancer. However, there is a need for novel biomarkers that aid clinical decision making about biopsy and initial treatment. There is no doubt that progress will continue based on the integrated collaboration of researchers, clinicians and biomedical firms.
Prostate neoplasms; molecular markers; prostate specific antigen
We have previously demonstrated that there is a learning curve for open radical prostatectomy. In this study we sought to determine whether the effects of the learning curve are modified by patient risk as defined by preoperative tumor characteristics.
The study included 7,683 eligible prostate cancer patients treated with open radical prostatectomy by one of 72 surgeons. Surgeon experience was coded as the total prior number of radical prostatectomies conducted by the surgeon prior to a patient’s surgery. Multivariable survival-time regression models were used to evaluate the association between surgeon experience and biochemical recurrence, separately for each preoperative risk group.
We saw no evidence that patient risk affects the learning curve: there was a statistically significant association between biochemical recurrence and surgeon experience in all analyses. The absolute risk difference for a patient receiving treatment from a surgeon with 10 compared to 250 prior radical prostatectomies was 6.6% (95% C.I. 3.4%, 10.3%), 12.0% (6.9%, 18.2%) and 9.7% (1.2%, 18.2%) for patients at low, medium and high preoperative risk patients. Recurrence-free probability for patients with low risk disease approached 100% for the most experienced surgeons
Cancer control after radical prostatectomy improves with increasing surgeon experience irrespective of patient risk. Excellent rates of cancer control for patients with low risk disease treated by the most experienced surgeons suggests that the primary reason such patients recur is inadequate surgical technique. The results have significant implications for clinical care.
Radical prostatectomy; prostate cancer; surgery
Prediction model; validation; nomogram; discrimination; calibration; decision curve
Women with localized breast cancer face difficult decisions about adjuvant therapy. Several decision aids are available to help women choose between treatment options. Decision aids are known to affect treatment choices and may therefore affect patient survival. The authors aimed to model the effects of the Adjuvant! decision aid on expected survival in women with early stage breast cancer.
Patients and Methods
Data were obtained from a randomized trial of Adjuvant! (n =395). To calculate the effects of the decision aid on survival, the authors used the Adjuvant! survival predictions as a surrogate endpoint. Data from each arm were entered separately into statistical models to estimate change in survival associated with receiving the Adjuvant! decision aid.
Most women (~85%) chose a treatment option that maximized predicted survival. The effects of the decision aid on outcome could not be modeled because a small number of women (n =12, 3%) chose treatment options associated with a large (5%–14%) loss in survival. These women—most typically estrogen receptor positive but refusing hormonal therapy—were equally divided between Adjuvant! and control groups and were not distinguished by medical or demographic factors.
Expected benefit from treatment is a key variable in understanding patient behavior. A small number of women refuse adjuvant treatment associated with large increases in predicted survival, even when they are explicitly informed about the degree of benefit they would forgo. Investigation of the effects of decision aids on cancer survival is unlikely to be fruitful due to power considerations.
Adjuvant!; breast cancer; decision aids; women’s health; oncology; outcomes research
Prediction is ubiquitous across the spectrum of cancer care from screening to hospice. Indeed, oncology is often primarily a prediction problem: many of the early stage cancers cause no symptoms, and treatment is recommended because of a prediction that tumor progression would ultimately threaten a patient's quality of life or survival. Recent years have seen attempts to formalize risk prediction in cancer care. In place of qualitative and implicit prediction algorithms, such as cancer stage, researchers have developed statistical prediction tools that provide a quantitative estimate of the probability of a specific event for an individual patient. Prediction models generally have greater accuracy than reliance on stage or risk groupings; can incorporate novel predictors such as genomic data; can be used more rationally to make treatment decisions. Several prediction models are now widely used in clinical practice, including the Gail model for breast cancer incidence or the Adjuvant! online prediction model for breast cancer recurrence. Given the burgeoning complexity of diagnostic and prognostic information there is simply no realistic alternative to incorporating multiple variables into a single prediction model. As such, the question should not be whether but how prediction models should be used to aid decision making. Key issues will be integration of models into the electronic health record, and more careful evaluation of models, particularly with respect to their effects on clinical outcomes.
Public adherence to cancer screening guidelines is poor. Patient confusion over multiple recommendations and modalities for cancer screening has been found to be a major barrier to screening adherence. Such problems will only increase as screening guidelines and timetables become individualized.
We propose to increase compliance with cancer screening through two-way rich media mobile messaging based on personalized risk assessment.
We propose to develop and test a product that will store algorithms required to personalize cancer screening in a central database managed by a rule-based workflow engine, and implemented via messaging to the patient’s mobile phone. We will conduct a randomized controlled trial focusing on prostate cancer screening to study the hypothesis that mobile reminders improve adherence to screening guidelines. We will also explore a secondary hypothesis that patients who reply to the messaging reminders are more engaged and at lower risk of non-adherence. We will conduct a randomized controlled trial in a sample of males between 40 and 75 years (eligible for prostate cancer screening) who are willing to receive text messages, email, or automated voice messages. Participants will be recruited from a primary care clinic and asked to schedule prostate cancer screening at the clinic within the next 3 weeks. The intervention group will receive reminders and confirmation communications for making an appointment, keeping the appointment, and reporting the test results back to the investigators. Three outcomes will be evaluated: (1) the proportion of participants who make an appointment with a physician following a mobile message reminder, (2) the proportion of participants who keep the appointment, and (3) the proportion of participants who report the results of the screening (via text or Web).
This is an ongoing project, supported by by a small business commercialization grant from the National Center for Advancing Translational Sciences of the National Institutes of Health.
We believe that the use of centralized databases and text messaging could improve adherence with screening guidelines. Furthermore, we anticipate this method of increasing patient engagement could be applied to a broad range of health issues, both inside and outside of the context of cancer. This project will be an important first step in determining the feasibility of personalized text messaging to improve long-term adherence to screening recommendations.
Early Detection of Cancer; Text Messaging; Prostatic Neoplasms