Katy J. L. Bell

Value of routine monitoring of bone mineral density after starting bisphosphonate treatment: secondary analysis of trial data.

Objective: To assess the value of monitoring response to bisphosphonate treatment by means of measuring bone mineral density. Design Secondary analysis of trial data using mixed models. Data source The Fracture Intervention Trial, a randomised controlled trial that compared the effects of alendronate and placebo in 6459 postmenopausal women with low bone mineral density recruited between May 1992 and May 1993. Bone density measurements of hip and spine were obtained at baseline and at one, two, and three years after randomisation. Main outcome measures Between-person (treatment related) variation and within-person (measurement related) variation in hip and spine bone mineral density. Results The mean effect of three years’ treatment with alendronate was to increase hip bone mineral density by 0.030 g/cm2. There was some between-person variation in the effects of alendronate, but this was small in size compared with within-person variation. Alendronate treatment is estimated to result in increases in hip bone density ≥0.019 g/cm2 in 97.5% of patients. Conclusions: Monitoring bone mineral density in postmenopausal women in the first three years after starting treatment with a potent bisphosphonate is unnecessary and may be misleading. Routine monitoring should be avoided in this early period after bisphosphonate treatment is commenced.

Optimizing the Frequency of Follow-Up Visits for Patients Treated for Localized Primary Cutaneous Melanoma

PURPOSE To develop more evidence-based guidelines for the frequency of patient follow-up after treatment of localized (American Joint Committee on Cancer [AJCC] stage I or II) melanoma. METHODS We used data from Melanoma Institute Australia on an inception cohort of 3,081 consecutive patients first diagnosed with stage I or II melanoma between January 1985 and December 2009. Kaplan-Meier curves and Cox models were used to characterize the time course and predictors for recurrence and new primaries. We modeled the delay in diagnosis of recurrence or new primary as well as the number of monitoring visits required using two monitoring schedules: first, according to 2008 Australian and New Zealand guidelines and, second, with fewer visits, especially for those at lowest risk of recurrence. RESULTS For every 1,000 patients beginning follow-up, 229 developed recurrence and 61 developed new primary within 10 years. There was only a small difference in modeled delay in diagnosis (extra 44.9 and 9.6 patients per 1,000 for recurrence and new primary, respectively, with delay greater than 2 months) using a schedule that requires far fewer visits (3,000 fewer visits per 1,000 patients) than recommended by current guidelines. AJCC substage was the most important predictor of recurrence, whereas age and date of primary diagnosis were important predictors of developing new primary. CONCLUSION By providing less intensive monitoring, more efficient follow-up strategies are possible. Fewer visits with a more focused approach may address the needs of patients and clinicians to detect recurrent or new melanoma.

Prevalence of incidental prostate cancer: A systematic review of autopsy studies

Prostate cancer screening may detect nonprogressive cancers, leading to overdiagnosis and overtreatment. The potential for overdiagnosis can be assessed from the reservoir of prostate cancer in autopsy studies that report incidental prostate cancer rates in men who died of other causes. We aimed to estimate the age‐specific incidental cancer prevalence from all published autopsy studies. We identified eligible studies by searches of Medline and Embase, forward and backward citation searches and contacting authors. We screened the titles and abstracts of all articles; checked the full‐text articles for eligibility and extracted clinical and pathology data using standardized forms. We extracted mean cancer prevalence, age‐specific cancer prevalence and validity measures and then pooled data from all studies using logistic regression models with random effects. The 29 studies included in the review dated from 1948 to 2013. Incidental cancer was detected in all populations, with no obvious time trends in prevalence. Prostate cancer prevalence increased with each decade of age, OR = 1.7 (1.6–1.8), and was higher in studies that used the Gleason score, OR = 2.0 (1.1–3.7). No other factors were significantly predictive. The estimated mean cancer prevalence increased in a nonlinear fashion from 5% (95% CI: 3–8%) at age <30 years to 59% (95% CI: 48–71%) by age >79 years. There was substantial variation between populations in estimated cancer prevalence. There is a substantial reservoir of incidental prostate cancer which increases with age. The high risk of overdiagnosis limits the usefulness of prostate cancer screening.

Prevalence of Differentiated Thyroid Cancer in Autopsy Studies Over Six Decades: A Meta-Analysis

PURPOSE Differentiated thyroid cancer (DTC) incidence has been reported to have increased three- to 15-fold in the past few decades. It is unclear whether this represents overdiagnosis or a true increase in incidence. Therefore, the current study aimed to estimate the prevalence of incidental DTC in published autopsy series and determine whether this prevalence has been increasing over time. MATERIALS AND METHODS PubMed, Embase, and Web of Science were searched from inception to December 2015 for relevant studies. Two authors searched for all autopsy studies that had included patients with no known history of thyroid pathology and reported the prevalence of incidental DTC (iDTC). Two authors independently extracted the data, and discrepancies were resolved by another author. The pooled prevalence of iDTC was assessed using a fixed-effects meta-analysis model with robust error variance. The time effect was studied using an inverse-variance weighted logit-linear regression model with robust error variance and a time variable. RESULTS Thirty-five studies, conducted between 1949 and 2007, met the inclusion criteria and contributed 42 data sets and 12,834 autopsies. The prevalence of iDTC among the partial and whole examination subgroups was 4.1% (95% CI, 3.0% to 5.4%) and 11.2% (95% CI, 6.7% to 16.1%), respectively. Once the intensiveness of thyroid examination was accounted for in the regression model, the prevalence odds ratio stabilized from 1970 onward, and no time effect was observed. CONCLUSION The current study confirms that iDTC is common, but the observed increasing incidence is not mirrored by prevalence within autopsy studies and, therefore, is unlikely to reflect a true population-level increase in tumorigenesis. This strongly suggests that the current increasing incidence of iDTC most likely reflects diagnostic detection increasing over time.

Use of randomised trials to decide when to monitor response to new treatment

Is monitoring initial response to treatment always helpful in clinical management of patients? Bell and colleagues have developed a framework for deciding whether surrogate outcomes should be used to monitor initial response to treatment in chronic disease.

Mixed models showed no need for initial response monitoring after starting antihypertensive therapy

OBJECTIVE To demonstrate how mixed models may be used to estimate treatment effects, and inform decisions on the need for monitoring initial response. STUDY DESIGN AND SETTING Mixed models were used to analyze data from the Perindopril Protection Against Recurrent Stroke Study (PROGRESS), which examined the effects of perindopril and indapamide in 6,105 patients at high risk of a cerebrovascular event. RESULTS The mean effect of perindopril was to lower blood pressure (BP) (systolic/diastolic) by 6/3 mmHg. The mean effects of perindopril/indapamide varied according to baseline BP, and lowering of BP ranged from 9/5 to 14/5 mmHg (for individuals with a baseline systolic BP <140 and >150 mmHg, respectively). We found no variation in the effects of treatment on BP for either perindopril alone or in combination with indapamide. The effects of treatment on the individual can be predicted from the mean effect of treatment for the group (perindopril) or baseline systolic BP subgroup (perindopril/indapamide). CONCLUSION Monitoring initial treatment response is unnecessary for antihypertensives similar to those examined in this study. To address this issue for other therapies, we suggest that trials should report estimates of treatment effects from mixed models, and the CONSORT statement should be expanded to include this item.

Monitoring Initial Response to Angiotensin-Converting Enzyme Inhibitor–Based Regimens. An Individual Patient Data Meta-Analysis From Randomized, Placebo-Controlled Trials

Most clinicians monitor blood pressure to estimate a patients response to blood pressure–lowering therapy. However, the apparent change may not actually reflect the effect of the treatment, because a persons blood pressure varies considerably even without the administration of drug therapy. We estimated random background within-person variation, apparent between-person variation, and true between-person variation in blood pressure response to angiotensin-converting enzyme inhibitors after 3 months. We used meta-analytic mixed models to analyze individual patient data from 28 281 participants in 7 randomized, controlled trials from the Blood Pressure Lowering Trialists Collaboration. The apparent between-person variation in response was large, with SDs for change in systolic blood pressure/diastolic blood pressure of 15.2/8.5 mm Hg. Within-person variation was also large, with SDs for change in systolic blood pressure/diastolic blood pressure of 14.9/8.45 mm Hg. The true between-person variation in response was small, with SDs for change in systolic blood pressure/diastolic blood pressure of 2.6/1.0 mm Hg. The proportion of the apparent between-person variation in response that was attributed to true between-person variation was only 3% for systolic blood pressure and 1% for diastolic blood pressure. In conclusion, most of the apparent variation in response is not because of true variation but is a consequence of background within-person fluctuation in day-to-day blood pressure levels. Instead of monitoring an individuals blood pressure response, a better approach may be to simply assume the mean treatment effect.

Monitoring adherence to drug treatment by using change in cholesterol concentration: secondary analysis of trial data

Objective To estimate the accuracy of monitoring cholesterol concentration for detecting non-adherence to lipid lowering treatment. Design Secondary analysis of data on cholesterol concentration in the LIPID (long term intervention with pravastatin in ischaemic disease) study by using three measures of non-adherence: discontinuation of treatment, allocation to placebo arm, less than 80% of pills taken. Setting Randomised placebo controlled trial in Australia and New Zealand. Participants 9014 patients with previous coronary heart disease. Interventions Pravastatin 40 mg or placebo daily. Main outcome measures Sensitivity, specificity, area under the receiver operating characteristics (ROC) curve, post-test probability. Results Monitoring of cholesterol concentration had modest ability for detecting complete non-adherence. One year after the start of treatment, half (1957/3937) of the non-adherent patients and 6% (253/3944) of adherent patients had a rise in concentration of low density lipoprotein cholesterol. Accuracy was reasonable (area under the curve 0.89). Cholesterol monitoring, however, had weak ability for detecting partial non-adherence. One year after the start of treatment, 16% (34/213) of partially adherent and 4% (155/3585) of fully adherent patients had a rise in concentration of low density lipoprotein cholesterol. Accuracy was poor (area under the curve 0.65). For typical pre-test probabilities of non-adherence ranging from low (25%) to high (75%), the post-test probabilities indicate continuing uncertainty after lipid testing. A patient with no change in low density lipoprotein cholesterol concentration has a post-test probability of being completely non-adherent of between 67% and 95% and a post-test probability of being partially non-adherent of between 48% and 89%. A patient with a decrease in concentration of 1.0 mmol/L has a post-test probability of being completely non-adherent of between 7% and 40% and a post-test probability of being partially non-adherent of between 21% and 71%. Conclusions Monitoring concentration of low density lipoprotein (or total) cholesterol has modest ability to detect complete non-adherence or non-persistence with pravastatin treatment and weak ability to detect partial non-adherence. Results of monitoring should be considered as no more than an adjunct to careful discussion with patients about adherence.

The potential value of monitoring bone turnover markers among women on alendronate.

Biochemical markers of bone turnover have been proposed to monitor the response to bisphosphonate therapy for osteoporosis, but this requires true between‐person differences in the response to therapy. Using mixed models we analyzed three annual measurements of two markers (bone alkaline phosphatase [BAP] and cross‐linked N‐telopeptide of type I collagen [NTX]) from the Fracture Intervention Trial. We compared marker variation among women allocated to alendronate with that among women allocated to placebo to estimate how much variation was due to true between‐person differences in response to treatment, and how much was due to random within‐person fluctuations unrelated to treatment. For both markers we found that the mean effect of treatment differed by the baseline level of the marker. After allowing for this and other effects, we found large true between‐person differences in response to treatment for both markers, with a coefficient of variation (CV) for NTX of 25.1% and for BAP of 21.2%. However, random within‐person fluctuation was even larger, with a CV for change in NTX of 42.5% and for change in BAP of 25.8%. Although repeated measurements have the potential to reduce within person variability, even triplicate baseline marker measurements resulted in an averaged value that was only within 31% of the true value with 95% certainty. In summary, although bone turnover markers appear promising for monitoring between‐person differences in response to treatment, their use in clinical practice is currently limited by large random within‐person variation.

Monitoring Adherence to Medication by Measuring Change in Blood Pressure

After starting antihypertensives, blood pressure is monitored for several reasons, including assessment of adherence. We aimed to estimate the accuracy of blood pressure monitoring for detecting early nonadherence. We conducted a secondary analysis of the Perindopril Protection Against Recurrent Stroke Study (PROGRESS), a large randomized trial of blood pressure lowering to reduce the risk of recurrent stroke. We compared change in blood pressure 3 months after randomization in people who had discontinued treatment (nonadherent) with those who stayed on treatment (adherent). We also used an indirect method, assessing whether change in blood pressure discriminated between active (adherent) and placebo (nonadherent) groups. Both methods gave similar results. For the 3433 subjects, the mean (SD) of the change in systolic blood pressure was −15.8 mm Hg (SD 18.7 mm Hg) in the adherent group and −4.2 mm Hg (SD 18.1 mm Hg) in the nonadherent group. After recalibration of the mean change in the nonadherent group to 0 mm Hg and in the adherent group to −11.6 mm Hg, the absence of a fall in systolic blood pressure at 3 months had a sensitivity of 50% and a specificity of 80% for detecting nonadherence (50% of nonadherent patients and 20% of adherent patients had a rise in blood pressure). Discriminatory power was modest over the range of cutoffs (area under the receiver-operator curve 0.67). Monitoring blood pressure is poor at detecting nonadherence to blood pressure-lowering treatment. Further research should look at other methods of assessing adherence.