BMC Public Health Evaluating real-time internet therapy and online self-help for problematic alcohol consumers: a three-arm RCT protocol

Matthijs Blankers email@example.com
Academic Medical Center
Department of Psychiatry
Amsterdam Institute for Addiction Research (AIAR)
University of Amsterdam
Room PB 0.425PO Box 758671070 AWAmsterdamthe Netherlands

Maarten Koeter email@example.com
Academic Medical Center
Department of Psychiatry
Amsterdam Institute for Addiction Research (AIAR)
University of Amsterdam
Room PA 1.181PO Box 758671070 AWAmsterdamthe Netherlands

Gerard M Schippers
Academic Medical Center
Department of Psychiatry
Amsterdam Institute for Addiction Research (AIAR)
University of Amsterdam
Room PA 1.190PO Box 758671070 AWAmsterdamthe Netherlands

BMC Public Health Evaluating real-time internet therapy and online self-help for problematic alcohol consumers: a three-arm RCT protocol
10.1186/1471-2458-9-16BioMed Central Page 1 of 9 (page number not for citation purposes) Study protocol * Corresponding author
Background:Only a minority of all alcohol-and drug abusers is receiving professional care. In an attempt to narrow this treatment gap, treatment facilities experiment with online healthcare. Therefore, it is important to test the (cost-)effectiveness of online health interventions in a randomized clinical trial.Methods: This paper presents the protocol of a three-arm randomized clinical trial to test the (cost-) effectiveness of online treatment for problem drinkers. Self-help online, therapy online and a waiting list are tested against each other. Primary outcome is change in alcohol consumption. Secondary outcome measures include quality of life and working ability. Incremental costeffectiveness ratios for self-help online alcohol and therapy online alcohol will be calculated. The predictive validity of participant characteristics on treatment adherence and outcome will be explored.Discussion: To our best knowledge, this randomized clinical trial will be the first to test the effectiveness of therapy online against both self-help online and a waiting-list. It will provide evidence on (cost-) effectiveness of online treatment for problem drinkers and investigate outcome predictors.Trial registration: This trial is registered in the Dutch Trialregister (Cochrane Collaboration) and traceable as

Background

The Netherlands have, compared to most other countries, a fairly accessible network of substance abuse treatment centres (SATCs). However, the target population of these SATCs is not sufficiently addressed: Only a minority of all current alcohol-and drug abusers is receiving professional care [1]. This so-called treatment gap is mainly due to two reasons. As substance dependence is strongly stigmatized, the threshold to visit a treatment centre is high [2]. And as most SATCs offer outpatient facilities during working hours only, many people with jobs cannot find to attend SATCs face-to-face therapy.

Another challenge to healthcare in general and addiction treatment in particular is optimising cost-effectiveness. As the future health spending is expected to increase at a much higher rate than in the past [3,4], cost-effectiveness is an important parameter in the development of new treatment programs. In general, early interventions, preventive care, and disease self-management, together with the correct implementation of health information technology and internet applications, is believed to have the potential to enhance health status while improving efficiency and reducing healthcare costs [3].

Substance abuse treatment over the internet has the potential to address these issues: The influence of both stigmatization and restricted opening times of healthcare institutions is reduced when clients do not have to visit a SATC during working hours, but can visit their web-based treatment environment from any place and at any time. Earlier evaluations have shown that a majority of online substance abuse treatment participants manage to hold their jobs [5,6]. Therefore, improvement in job performance may be expected as a result of successful participation, as problematic drinking habits tend to interfere with working ability in general.

The increasing number of online treatment options worldwide shows that treatment facilities are willing to experiment with online healthcare. A review of the current state of the art in substance abuse treatment over the internet is promising [7]. Some authors report online substance abuse treatment to be successful in addressing an underserved population [5,6]. Results of the first randomized clinical trials support the use of the internet to extend the treatment options for substance abusers [8][9][10]. There is preliminary evidence on the cost-effectiveness of early interventions and computer-aided therapy for alcohol related problems [11,12]. However, as the number of randomized clinical trials for online substance abuse treatment is still sparse, its (cost-)effectiveness is yet to be supported more firmly. This article presents a randomized clinical trial that will extend the knowledge on effectiveness and cost-effectiveness of online treatment for problem drinkers.

The SATC whose interventions are subjected to this trial has pioneered in the development of internet applications for problematic alcohol and substance users. Since 2003, its online treatment programs have been based on two evidence-based techniques: cognitive-behavioural therapy (CBT) and motivational enhancement training. These early experiences have led to the development of online interventions for alcohol-, cannabis-, cocaine-and tobacco-users, and for pathological gamblers. Two online treatment programs for problem drinkers will be compared in this trial. One is an anonymous, online, noncounsellor involved, fully automated self-guided treat-ment program, referred to as Self-help Online Alcohol (SOA). The other is a real time, online, non anonymous, counsellor-guided therapy program for problem drinkers referred to as Therapy Online Alcohol (TOA).

SOA introduces participants to various CBT elements as a means to monitor and help them change their alcohol consumption. Registration of alcohol consumption, online diary, goal setting, and relapse prevention are among them. A recent study has shown that SOA is highly attractive and promising in its effects [6]. However, participants of SOA did report they missed the opportunity to interact with a counsellor on a regular basis. In response to the participants' suggestions, the online therapy program (TOA) has been developed. Like SOA, TOA is based on CBT and motivational enhancement training, but has a more intensive and stricter structured treatment course and regular counsellor interaction. Contact between client and counsellor takes place online in a one-on-one, private chat-room. TOA resembles to a great extent the conventional face-to-face manual based CBT training program [13], used in many SATCs in the Netherlands. Treatment elements of TOA include consumption registration, online diary, tailored feedback of recent alcohol consumption, contemplating advantages and disadvantages of drinking, goal setting, self-control methods, investigating risk-situations, coping with craving, emotions and social pressure, and the formulation of emergency plans in case of relapse. These elements are introduced by the counsellor. Working with the treatment elements is part of clients' weekly homework assignments.

The most important difference between SOA and TOA are seven real-time, one-on-one chat sessions with an experienced counsellor trained in TOA. Each chat session lasts for approximately 40 minutes. During these sessions, feedback on homework assignments is provided and the client is motivated to enhance his or her attempt to stop or reduce drinking. Because of it's interactive nature, chatsessions resemble to a big extent the conversational dynamics of regular, face-to-face therapy contacts. This new element meets the expressed needs of participants of SOA and makes the intervention more similar to the successful face-to-face CBT treatment.

Methods

Aims of the trial

This study aims to test the effectiveness and cost-effectiveness of SOA and TOA against a waiting list control group, in a three-arm randomized clinical trial. Primary outcome measure is the change in alcohol consumption from baseline (t0) to the three month follow-up (t1) and to the six month follow-up (t2). Secondary outcome measures are changes in AUDIT scores, quality of life and quality of functioning at work. Incremental cost-effectiveness ratios for SOA and TOA will be calculated. The predictive valid-ity of participant characteristics on treatment adherence and outcome will be explored.

Study population

Our study population will be recruited through the SATCs website, that was viewed by 650.000 unique visitors in 2007 [14]. If necessary, additional attention for this study will be attracted through advertisements at family doctors' offices, internet-forums, and news-paper adds. Applicants meeting inclusion criteria defined in Table 1 are invited to participate.

Research questions

Q1a. Do TOA participants reduce their alcohol consumption from t0 to t1 and t2 to a larger extent than SOA and waiting list control participants?

Q1b. Do TOA participants improve to a larger extent on secondary outcome measurement scores from t0 to t1 and t2 than SOA and waiting list control participants?

Q1c. Are TOA participants more often treatment-responders than SOA and waiting list control participants at t1 and t2?

Q1d. Do SOA participants reduce their alcohol consumption to a larger extent from t0 to t1 and t2 than waiting list control participants?

Q1e. Do SOA participants improve on secondary outcome measurement scores from t0 to t1 and t2 to a larger extent than waiting list control participants?

Q1f. Are SOA participants more often treatment-responders than waiting list control participants at t1 and t2?

Q2. What are the comparative costs per quality adjusted life year (QALY) for SOA and TOA?

Q3a. How do participants' demographic characteristics relate to demographics of participants in conventional face-to-face CBT programs?

Q3b. Do scores of working alliance between TOA participants and counsellor predict treatment-response at t1 and t2?

Hypotheses

We hypothesize TOA participants will have reduced their alcohol consumption significantly more at the three month follow-up (t1) compared to SOA and waiting list control participants (Q1a). Between t0 and t1, we also expect TOA participants to improve on secondary outcome measurements (quality of life and working abilities) significantly more than SOA and waiting list control participants (Q1b). We hypothesize that a larger percentage of TOA participants will meet the criteria for treatmentresponders compared to SOA and waiting list control participants (Q1c). We hypothesize that SOA participants will have reduced their t0 alcohol consumption significantly more at t1 than waiting list control participants (Q1d). Between t0 and t1, we also expect SOA participants to improve on secondary outcome measurements significantly more than waiting list control participants (Q1e). Finally, we hypothesize that a larger percentage of SOA participants will meet the criteria for treatment-responders compared to waiting list control participants (Q1f).

Between t1 and t2, we expect TOA participants to be better able than SOA participants to maintain their achieved changes in alcohol consumption, resulting in a significant time (t1-t2) by group (SOA-TOA) interaction. Alcohol consumption in SOA will be significantly higher at t2 compared to t1, whereas alcohol consumption in TOA will not change significantly during this period.

For question 2, we hypothesize that from t0 to t1 both the costs and the gains in QALY for SOA to be lower than the For question 3, we hypothesize our participants to be higher educated, a larger percentage to be working and female, compared to conventional outpatient SATC clients (Q3a). We also hypothesize that higher scores on working alliance will predict a larger proportion of treatment-response at t1 for TOA participants (Q3b).

Measurement instruments

Several measurement instruments are included in the t0, t1 and t2 questionnaire, as presented in Table 2. Alcohol consumption will be measured using substance use subscales of the 'European Addiction Severity Index' (Euro-pASI) [15] and Timeline-Follow-Back for alcohol consumption [16]. This Timeline-Follow-Back method has good capacity to retrieve drinking history in the past week [17]. As a third scale to measure alcohol consumption burden the AUDIT is included [18].

Quality of life will be measured using two instruments:

The EQ-5D [19][20][21] and the QOL [22]. The EQ-5D is a highly accepted, widely used and easy to administer instrument for measuring quality of life for alcohol and drug dependents [23,24]. As the scoring algorithm of the EQ-5D generates an index weighted by a preference measure, EQ-5D scores can be used in cost-effectiveness analysis to estimate QALYs [25]. However, it is recommended that the EQ-5D is used in combination with other quality of life instruments. This should be done to minimize its potential limitation in sensitivity until further results confirm EQ-5D validity in alcohol dependent individuals [26]. Therefore, the 'Quality Of Life' scale QOL [22] is included. According to content validity analysis, this is a valid instrument for measuring quality of life across patient groups and cultures. It is conceptually distinct from health status or other causal indicators of quality of life [27].

As an earlier evaluation study of SOA has shown that a majority of its participants manage to hold jobs [5,6], their problematic drinking habit could negatively influence their working ability. To be able to measure this effect, we included two working ability measurement scales. The SF-HLQ, a subscale of the Trimbos/iMTA questionnaire for Costs associated with Psychiatric Illness (TiC-P) [28], is well able to measure the work related productivity of applicants who held a job in the past two weeks. A more general measure of working ability, which is also eks held a job. A more general applicable for the applicants who are not currently employed, is provided by the 'Working Ability Index' (WAI-work) [29]. This questionnaire measures self-reported ability to work, as opposed to the SF-HLQ which measures the actual working behaviour.

Key demographical characteristics will be measured using subscales of the EuropASI [15]. Baseline psychopathology will be measured using the Brief Symptom Inventory (BSI) [30,31]. To be able to evaluate the reliability of the self-reported questionnaire data, a 13-item version of the Marlowe Crowne Social Desirability Scale (MCSDS) [32] is included.

Primary and secondary outcome measures

Primary outcome measures are the differences in alcohol consumption, and the number of participants showing 'treatment response', as defined below. Although de facto a composite score of primary and secondary outcomes, 'treatment response' is considered primary outcome as well. Secondary outcome measurements are AUDIT-score; quality of life as measured by QOL, and EQ-5D, psychopathological symptoms as measured by BSI and ability to work as measured by SF-HLQ and WAI-work.
EuropASI (subscales) x x x Timeline-follow-back x x x AUDIT x x x QOL x x x EQ-5D x x x WAI-work x x x SF-HLQ x x x BSI x x x MCSDS
x Dutch mental health thermometer

Definition of treatment-response

Adding treatment-response criteria has an important advantage over the comparison of group mean-scores on primary and secondary outcome measures only. In our opinion, it has more external validity as it is helpful in making a clear statement on who actually improved to a clinical relevant extent. Based on binary treatmentresponse outcome data, we can calculate relative risk reduction, number needed to treat, and odds ratios. The British Medical Association published guidelines on sensible drinking which suggested that the boundaries for sensible alcohol consumption should be set at a maximum of 21 standard alcohol units per week for men, and 14 standard alcohol units per week for women [33]. To calculate treatment-response outcome, alcohol consumption as measured using Timeline-Follow-Back will be used [16]. Deteriation percentages relate to changes within participants on the secondary outcome measurement scores between t0 and t1. Treatment-response is defined as: 'alcohol consumption within British Medical Association boundaries, and less than 10% deteriation on any of the secondary outcome measures'.

Power calculation

Appropriate sample size is estimated using Gpower v.3.0.5 software [34]. This calculation is based on the primary outcome measure (alcohol consumption), in a oneway ANOVA test design. Based on [10] we expect an effectsize of d = 0.40 when comparing waiting list control and treatment groups. To be able to detect even smaller differential effects (d = 0.25) between SOA en TOA with = .05 and = .20, we aim to include 150 participants. The power calculation is summarized in Table 3.

Randomization

After the t0 measurement is completed, participants are allocated to one of the three trial arms. Because this randomized clinical trial is internet-based, we will organize the allocation procedures online as well. As the number of participants we aim to include in each trial arm (n = 50) is not very large, random variation in baseline characteristics could reduce group equivalence. Online, server-based performance of randomization procedures comes with the advantage of being able to implement stratified allocation or allocation through adaptive randomization relatively easy.

To prevent between-group variability on relevant baseline characteristics, we use an adaptive or biased-coin randomization algorithm for this trial. Adaptive randomization aims to ensure trial arms are balanced with respect to a priori defined participant factors as well as for the number of participants in each group. Simulation studies show that adaptive randomization provides better balanced trial arms when compared with 'normal' randomization [35] and that it is more efficient in balancing multiple poli-level factors than permutated block stratification [36]. Although not widely used due to organizational complexity, adaptive randomization is a highly effective allocation method, and its wider adaptation in the conduct of randomized clinical trials is recommended [37]. The use of adaptive randomization is supported in the CONSORT statement [38]. We will implement the Pocock & Simon Range Method of adaptive randomization [39], with D = 'range', G is the sum of D (no factor weighting), and P = 0.60.

Procedures

For the purpose of this trial we will develop a website to inform potential applicants about the contents of the online treatment, and the procedures associated with this trial. Upon visiting the website, new participants can read all relevant information and ask additional questions through a mail form. For included participants, a secured login system to enter the online interventions is developed and available on the website. Visitors willing to participate fill out a web-based questionnaire to check if they meet the inclusion criteria. If they do not, they are informed so, and an email is sent to this applicant. This email contains additional information about alternative treatment options. Applicants meeting inclusion criteria proceed to the subscription screen. Next, they are once again informed on the conditions of participation in this trial. After reading all information, they decide whether they are willing to participate. If they would like to participate, they are asked for necessary personal information. A confirmation email is sent to them, with the informed consent as an attachment. This confirmation email also contains their personal username and password. With this unique username/password combination, applicants can log in to their private area. Upon logging in for the first time, applicants are shown a screen which requests them to confirm they read, understood and agreed with the informed consent sent to them. Applicants can decide to agree with this informed consent at any time, and can deliberate on whether or not they consent with the partic- ipant information for as long as they want. Only after their confirmation they are introduced to the baseline measurement procedure. After completion of the baseline measurement the randomization procedure takes place. Depending on the outcome, participants are allocated to one of three trial arms. Figure 1 provides an overview of the trial flow. If a participant is allocated to trial arm 1, he or she is directly introduced to TOA through a welcome text. A notification will now be sent to the TOA coordinator to make a first appointment for a chat session with the participant. From now on, TOA starts for participants in trial arm 1. Three months after t0 measurement, participants will receive an invitation for the t1 measurement by email and upon visiting the trial website. It is possible that some participants

Trial flow

have not yet finished the entire TOA process after this period. However, the decision has been made to let follow-up timing depend solely on the time interval since t0, not on the current status of their treatment proceedings. This is in our opinion the best possible confirmation to the 'intention to treat' principle [40]. Three months after the t1 measurement participants are invited for the t2 measurement. After completion of t2 measurements, data collection is completed. Participants in trial arm 2 are introduced to SOA after randomization. Up until t1, participants can use the SOA program every time they log in on the trial website. t1 and t2 measurements are introduced by email and upon visiting the trial website. Participants in this trial arm are offered the possibility to proceed with TOA after completion of t2 measurements, would they be interested in more help after completion of SOA. Participants in trial arm 3 are allocated to the wait- Figure 1 Trial flowchart. This figure provides an overview of the participant flow for this trial.

Trial flowchart
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ing list after randomization. After three months, they too will receive an invitation for the t1 measurement. After t1, participants in trial arm 3 are introduced to TOA. For ethical reasons, the decision is made to offer them TOA after three months (t1), and not after t2 (six months).

Maximize response rates

Drop-out is a potential threat in every clinical trial. Since attrition rates for online trials tend to be larger than in regular ones [52], we will make extra efforts to maximize response rates. Following suggestions for successful strategies to maximize response rates [41,42], we made followup questionnaires as short as possible and give participants feedback on the actual length and their proceedings while filling out the questionnaire. We motivate participants to fill out the questionnaires by sending them a reminder if they do not respond within five days after the first emailed invitation. To optimize a feeling of worthiness and to reward participants for their efforts and time, participants will receive gift coupons (€15,-) after filling out a follow-up questionnaire. Participants will be aware of this incentive upon reading the informed consent form. However, participants who do not fill out the questionnaires after a reminder is sent will be contacted by a call centre. Since 2005, this call centre has been collecting regular outcome data of outpatient addiction treatment for SATCs nationwide in the Netherlands. It is experienced in maximizing response rates in hard to reach populations [43]. For this research project, they will collect outcome data by phone from trial participants who do not respond to emailed invitations. All participants are aware of these procedures, provide phone numbers, and consent with receiving phone-calls for follow-up measurements.

Data analysis

Multiple imputation missing data handling procedures will be implemented using MICE [45] a package for the R Statistical Software Environment. As the quantity of consumed alcoholic drinks is the primary outcome variable in this trial, analysis techniques are chosen that suit the expected data distribution. Baseline measurements of the three trial arms will be compared using t-tests and χ 2 -tests. Trial arm differences on primary and secondary outcome variables at three months are tested using repeated measures ANOVA. Trial arm differences for SOA and TOA between t1 and t2 are tested with a 2 (group) × 2 (time) repeated measures ANCOVA with between and within contrasts. Effect-sizes will be calculated using Cohen's d [46]. Trial arm differences on treatment-response will be tested using multiple logistic regression analysis. Costs from TOA and SOA will be derived from cost records of the collaborating SATC. Costs will be assessed in correspondence to the recommendations by the U.S. Panel on Cost-effectiveness in Health and Medicine [25], following a macro-costing approach. QALYs will be calculated from the EQ-5D questionnaire, using algorithm by Dolan [47], retrieved from the Open Health Measures website [48]. Based on these estimations, costs-per-QALY and an incremental cost-effectiveness ratio can be calculated. The U.S. Panel on Cost-effectiveness has recommended that costeffectiveness studies should conduct sensitivity analysis, which for this cost-effectiveness analysis will be implemented through the bootstrap method [49][50][51].

Ethical approval

This trial will be executed in compliance to the Helsinki Declaration and is approved by the Medical Ethical Commission of the Academic Medical Center in Amsterdam, the Netherlands.

Discussion

To our best knowledge, this randomized clinical trial is the first to test the effectiveness of online therapy (TOA) against both a derived self-help program (SOA) and a waiting-list control group. Both SOA and TOA are based on an evidence-based CBT face-to-face treatment programme [13], implemented by several SATCs in the Netherlands. The counsellors involved in TOA are all working for a well respected, research minded SATC with many years of experience in the counselling of problem drinkers. The AIAR (Amsterdam Institute for Addiction Research) group has over 15 years of experience in both clinical and experimental addiction research. This solid foundation makes a sound opportunity to design and execute a randomized clinical trial to test effectiveness, while on the other hand extending knowledge on how internet technology can be used in healthcare research.

A potential threat for this trial is the so-called 'law of attrition', defined by Eysenbach: 'the phenomenon of participants stopping usage and/or being lost to follow-up, as one of the fundamental characteristics and methodological challenges in the evaluation of eHealth applications' [52]. We intent to implement four measures to reduce the impact of this attrition law. First, data-collection over the telephone, performed by an experienced call centre as a backup method for the follow-up measurements gives us a greater possibility to actively pursuit higher response rates. Second, all participants in this trial have to make a major time investment before being randomized and included in this trial -by filling out a 45 minutes baseline measurement. As an extra incentive for participation to the follow-up measurements, all participants receive gift coupons for their filled-out questionnaires. As a last resolve, all missing data values in the final data set will be multiple imputed according to methodology suggested by Schafer and Graham (2002) [44].

A second potential risk in this trial would concern the inclusion of participants. The inclusion of enough partic-ipants is not our main sorrow. As SOA in the regular context and without promotional efforts is able to attract over 150 new participants each month [6], we think we will manage to include 150 participants in a one year inclusion period. However, the resemblance of the regular online clientele is our main focus of attention. Due to the high accessibility of online help in the normal context and the relatively high constraints posed on participants in this trial, there is a potential risk that the trial participants do not resemble the regular e-clientele exactly. Whether this actually occurs or not can only be answered afterwards, by comparing data on characteristics of trial participants and regular online clientele data.

A third possible limitation concerns integrity of our collected data. As our t0 and t1/t2 data is self reported without a researcher nearby to oversee data-collection, we rely heavily on the correctness of our participants' answers. Various authors evaluate the self-reported data in alcohol research as reliable [53][54][55]. Tourangeau and colleagues even concluded that self-reported data is more reliable in the absence of a research confederate [56]. However, as an extra means to check data integrity, we included a social desirability scale, to measure whether these tendencies occur in our research population.

Another point of concern on data integrity in this trial could be made about our suggested statistical procedures. Multivariate statistics relies considerably on data distribution normality [57]. As the distribution of alcohol consumption count data is usually zero-elevated and highly skewed, particularly in subjects with alcohol dependence [58], multivariate statistics and most statistical tests should be used with caution. To prevent possible in-or deflated α's, we will use bootstrap methods and permutation tests as suggested by Hesterberg and colleagues [59], to minimize the influence of data distributions on our testing results, if data turn out to be non-normally distributed.

During the planning stage of this trial we have thought of implementing a conventional face-to-face treatment comparison condition as well. However, it is disputable whether in clinical practice, the face-to-face treatment and the online treatment populations are comparable. In previous pilot studies, we found that less than 10% of the internet treatment population was willing to receive conventional treatment [6]. On the other hand, all face-toface participants who consider themselves as computerilliterate, or do not have a computer with internet connection at home, would be unsuitable for online treatment. These considerations illustrate not only the methodological difficulties of a hybrid online -face-to-face trial, but also the restricted external validity of it's results. This was for us the main reason not to chose for a conventionalinternet therapy comparison.

Trial registration

This trial is registered in the Dutch Trialregister (Cochrane Collaboration) and traceable as NTR-TC1155.

Table 1 :
1Selection criteria for inclusioncosts and the gains in QALY for TOA. As this means that both the numerator and the denominator of the costeffectiveness equation is larger for TOA compared to SOA, we have no preconceptions of whether the incremental cost-effectiveness calculation will favour either SOA or TOA (Q2).Inclusion criteria
Exclusion criteria

age 18 -64
prior therapy in addiction treatment centre
internet at home
history of alcohol delirium
AUDIT score > 8
history of drug overdose
alcohol consumption > 14 standard units per week
severe coronary problems
informed consent received
severe intestine diseases
diagnosed schizophrenic
history of stroke
epilepsy in last year
contemplated or attempted suicide in last year
cocaine and or amphetamine use > 4 days last month
cannabis use > 9 days last month
holiday/travelling intention > 2 weeks during trial

Table 2 :
2Measurement instrumentsInstrument
t0 measure
t1 measure
t2 measure

Table 3 :
3Power analysisF tests -ANOVA: Fixed effects, omnibus, one-way
Analysis:
A priori: Compute required sample size
Input:
Effect size f
0.25
α error probability
0.05
Power (1-β error probability)
0.80
Number of groups
3
Output:
Non-centrality parameter λ
9.938
Critical F
3.054
Numerator df
2
Denominator df
156
Total sample size
159
Actual power
0.805

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AcknowledgementsThis randomized clinical trial is funded by a grant from the ZonMW Addiction II Program (Risk behaviour and dependency). The SATC referred to in this text as 'collaborating' is Jellinek, a division of Arkin, the Amsterdam based mental health and addiction treatment centre. Jellinek/Arkin supports and facilitates the development of this trial. SOA and TOA have been developed by Jellinek Prevention, a subdivision of this SATC. None of the funders has had any role in the trial design, selection of measurement instruments or the preparation of this article. The authors would like to thank the mentioned call centre for their intentions to assist in data-collection.Competing interestsThe authors declare that they have no competing interests.Authors' contributionsAll authors have contributed substantially to this protocol. MB constructed the design of the study and drafted the manuscript. MK led on the overall methodological development and revised the manuscript. GMS is principal investigator, constructed the design of the study and drafted the manuscript. All authors have read and approved the final manuscript.Pre-publication historyThe pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/9/16/prepub
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Back to past leeches: repeated phlebotomies and cardiovascular risk

Melania Manco
Josè Manuel Fernandez-Real
Back to past leeches: repeated phlebotomies and cardiovascular risk
C O M M E N T A R Y Open Accessblood pressureinsulin sensitivityironmetabolic syndromephlebotomy
In patients with metabolic syndrome, body iron overload exacerbates insulin resistance, impairment of glucose metabolism, endothelium dysfunction and coronary artery responses. Conversely, iron depletion is effective to ameliorate glucose metabolism and dysfunctional endothelium. Most of its effectiveness seems to occur through the amelioration of systemic and hepatic insulin resistance. In a study published by BMC Medicine, Michalsen et al. demonstrated a dramatic improvement of blood pressure, serum glucose and lipids after removing 550 to 800 ml of blood in subjects with metabolic syndrome. This effect was apparently independent of changes in insulin resistance, in contrast to previous cross-sectional and cohort studies investigating the association between iron overload, insulin resistance and cardiovascular disease. Despite drawbacks in the study design, its findings may lead the way to investigations aimed at exploring irondependent regulatory mechanisms of vascular tone in healthy individuals and patients with metabolic disease, thus providing a rationale for novel preventive and therapeutic strategies to counteract hypertension. Please see related article: http://www.biomedcentral.com/1741-7015/10/54

Background

Metabolic syndrome (MetS) is a clustering of cardiovascular disease risk factors (for example, visceral, obesity dyslipidemia, hypertension, hyperglycemia, fatty liver disease). Both insulin resistance (IR) and low-grade inflammation underlie the syndrome [1]. The presence of MetS is predictive of type 2 diabetes mellitus (T2DM) and all-cause mortality [2]. In the United States, prevalence estimates of MetS are 33% for the general younger adult population (aged 20 to 59) and 59% in older individuals. In the future, metabolic syndrome may overtake smoking as the leading risk factor for heart disease [3]. Currently, it is possible to prevent or delay the occurrence of MetS, mainly with a healthy lifestyle, which is a lifelong commitment [1].

Other strategies are also possible. Michalsen et al. [4] observed an impressive reduction of blood pressure (about 18 mmHg in the treated group compared with 0.2 in the control group) in patients with MetS after removing 550 to 800 ml of blood. The effect was evident early after the first phlebotomy and persisted two weeks following the second venopuncture (performed at Day 28). The study was a randomized, controlled singleblinded trial of 64 hypertensive patients. Thirty-seven percent had T2DM. Most of them were on drug treatment. Changes in blood pressure and insulin resistance (as estimated by the homeostatic model assessment of insulin resistance, HOMA-IR) correlated with the reduction in ferritin levels.

The beneficial effects of phlebotomies have been repeatedly observed in the past and documented in 1867 by Brunton, who observed that "Small bleedings of three or four ounces, whether by cupping or venesection, were (...) beneficial", and recommended that a few ounces of blood be removed every few weeks from patients with angina. He attributed the relief of angina to the diminution of "arterial tension" [5]. In 1970, Parker et al. [6] observed that angina was relieved and ventricular function returned to normal after a phlebotomy averaging 276 ml. More recently, iron overload was observed to cluster with abnormalities of MetS, including overt T2DM [7][8][9]. Iron depletion by repeated phlebotomies, erythrocytapheresis or use of iron chelators ameliorated metabolic control, coronary artery responses and endothelial dysfunction [7][8][9] and the beneficial effects of these procedures on metabolism are thought to be driven primarily by the amelioration of insulin resistance [9,10].

Puzzlingly, the beneficial effect on blood pressure observed by Michalsen [4] was independent of any effect on IR; hence, suggesting an independent mechanism of action. The potential impact of the Michalsen's study is remarkable in terms of reduced health care costs. If effectiveness of repeated phlebotomies in reducing blood pressure in patients with MetS is confirmed, this could dramatically reduce the burden of the syndrome and its related costs. Indeed, phlebotomies as routine means for primary prevention or treatment of hypertension are easy to perform, low-in-cost and, hence, could be a good alternative to more expensive drugs. Moreover, by encouraging blood donation in patients with MetS it will be effective also to respond to the worldwide significant demand for red cells and blood components.

However, the results from this trial generate a number of questions regarding the lack of effect on IR and the early and consistent effects on blood pressure.

Iron overload, insulin resistance and risk of T2DM

A relationship between iron stores and IR has been reported, but with some inconsistencies. The risk of diabetes was significantly lower in frequent blood donors from the Health Professionals Follow-up Study when compared with non-donors after 10 years of follow-up [11]. However, the beneficial effect of frequent blood donation disappeared at 12 years [12]. In the same cohort [12] and in post menopausal women from the Iowa Women's Health Study [13], heme-iron intake from red meat sources was associated with increased risk of diabetes.

Serum ferritin, a surrogate marker of iron status as it accurately reflects body iron stores in healthy individuals [14], was associated with increased risk of T2DM [15]. Subjects with hyperferritinemia had a 2.4-fold higher risk of developing T2DM [16]. This association was further confirmed in other studies [17][18][19].

None of these aforementioned studies provided information on insulin resistance, which has been explored in cross-sectional investigations. Frequent blood donors had improved insulin sensitivity and decreased insulin secretion when compared with sporadic blood donors or nondonors [20]. In an unblinded and uncontrolled study of 31 subjects with glucose intolerance, serial phlebotomy individually adjusted to induce near iron deficiency was associated with improved insulin sensitivity in response to oral glucose loading and reduced levels of glycated haemoglobin [21]. In another unblinded, randomized study in 28 patients with high-ferritin T2DM, three serial 500-ml phlebotomies reduced mean serum ferritin from 460 to 232 ng/ml and simultaneously led to increased insulin sensitivity and reduced levels of glycated haemoglobin [8].

Concerning the relationship between iron stores, MetS and IR, a number of epidemiological studies showed a relationship [22][23][24]. Hence, the lack of a significant effect of phlebotomies on IR in the present series is somewhat unexpected. HOMA is a poor predictor of insulin sensitivity in patients with T2DM, but, additionally, more than two phlebotomies and steadily reduced ferritin levels might be needed to see a significant effect of iron depletion on IR in patients with long-term diabetes and on multi-drug treatment as those evaluated in the Michalsen's series [4]. Indeed, significant effects after one year of treatment in high-ferritin T2DM patients [7] and after two years in those carrying hereditary hemochromatosis [10] have been observed. Moreover, drug treatment might have confounded results and thus, it would be important to study in more depth the effects on HOMA-IR by taking into account the use of medication (that is, separating those taking metformin vs. the other groups). Alternatively, the effects of blood donation on IR may differ in subjects with normal versus abnormal glucose tolerance as observed when exploring the effect of iron depletion on brachial artery flow-mediated dilation [25] and in keeping with those of Hirai et al. [26], who demonstrated differential effects of vitamin C in subjects with normal versus abnormal glucose tolerance.

Behind the iron-heart hypothesis

Several epidemiological studies have investigated the effect of iron depletion on atherosclerosis, cardiovascular risk, mortality and morbidity [27]. Blood pressure is one of the main factors driving cardiovascular morbidity and mortality, but no evidence was provided of any reduction in blood pressure following phlebotomies.

By stratifying men from the Health Professionals Follow-up Study according to lifetime number of donations (0, 10 to 20 and ≥30), no difference was observed in the risk for hypertension [11]. The Kuopio Study was the study which reported significant differences in mean blood pressures between donors and non-donors, but such a difference might reflect different lifestyles [16].

Thus, one of the main questions is how two phlebotomies worked to reduce blood pressure. Improvement of IR is not a nominee, at least at a first glance. Michalsen et al. [4] argue that reduced low-grade inflammation and oxidative stress may play a role as iron-dependent hydroxyl radical formation can contribute to vascular dysfunction. In this regard, high-frequency blood donation was associated with reduced serum ferritin and increased brachial artery flow-mediated dilation compared with low-frequency blood donation [22]. Serum ferritin was significantly decreased and flow-mediated dilation was increased in high-frequency donors compared with low-frequency donors with no betweengroup difference in insulin sensitivity. The decrease in flow-mediated dilation during oral glucose tolerance testing did not differ between high-and low-frequency blood donors [22]. Alternatively, the authors [4] speculate that changes in blood viscosity caused vasodilatation and, in turn, reduced blood pressure.

On the other hand, as blood volume was not replaced following phlebotomies, patients on multi-drug therapy or with type 2 diabetes might also have a dysfunctional endothelium and sympathetic response to relative hypovolemia. They might be unable to compensate for hypovolemia as healthy donors do. Indeed, most previous evidence were from cohort and cross-sectional studies of healthy donors [20], and from high-ferritin T2DM patients and carriers of hereditary hemocromatosis in whom blood volume was restored to normal at each procedure [7,10].

Future directions and conclusions

The strength of Michalsen's study is that for the first time an effect of iron depletion on blood pressure is recognized. Like other evidence from clinical studies, the effectiveness of iron depletion on hypertension needs to be proved with further studies. The focus of the research should move toward two different fronts: i) investigation of the effect of reducing body iron stores through graded phlebotomy, preferably using solid measures of insulin sensitivity, vascular resistance, viscosity and oxidative damage; ii) meta-analysis of data from published cohorts or de novo analyses of much larger cohorts of healthy donors and patients with an appropriately long-term follow-up and tight monitoring of dietary iron intake.

In conclusion, findings from Michalsen's study give new perspectives for prevention and treatment of the metabolic syndrome demonstrating that repeated phlebotomies, a low-cost and minimally invasive technique, are effective in reducing blood pressure with a mechanism that is independent of insulin resistance. Routine phlebotomies in these patients may enormously reduce health care costs related to the epidemic metabolic syndrome and, importantly, also contribute to increase the rate of blood donations.

 Manco and Fernandez-Real BMC Medicine 2012, 10:53 http://www.biomedcentral.com/1741-7015/10/53 Page 2 of 4
AcknowledgementsWe are indebted to Francesco Equitani for the critical revision of the manuscript.Authors' contributionsBoth authors contributed equally to the drafting and revising of the manuscript, and both authors approved the final version to be published.Authors' information MM, MD Ph.D FACN, is responsible for the research unit for multifactorial diseases at the Scientific Directorate of the Bambino Gesù Children's Hospital, Rome. She has devoted her entire research activity to obesity and type 2 diabetes. JMFR, MD, PhD, is Chief of the Section of Diabetes. Department of Endocrinology. Hospital de Girona Dr Josep Trueta and CIBERobn. Avinguda de França s/n. 17007 Girona, Spain.Competing interestsThe authors have no conflict of interest in relation to this work.
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Diagnostic Pathology Atypical adenomatous hyperplasia (adenosis) of the prostate: a case report with review of the literature

Henry B Armah
Department of Pathology
University of Pittsburgh Medical Center
15213PittsburghPAUSA

Anil V Parwani
Department of Pathology
University of Pittsburgh Medical Center
15213PittsburghPAUSA

Diagnostic Pathology Atypical adenomatous hyperplasia (adenosis) of the prostate: a case report with review of the literature
10.1186/1746-1596-3-34BioMed Central Page 1 of 4 (page number not for citation purposes) Case Report * Corresponding author
A 62-year-old male presented with symptoms of urinary obstruction and elevated serum prostatespecific antigen level of 3.61 ng/mL. Prostate needle biopsies showed benign prostatic tissue with a focus of crowded glands with minimal cytological atypia, fairly well-circumscribed with infiltrative appearance of glands at the edges. This focus had both small and larger glands with similar histological features. This focus was strongly positive for alpha-methylacyl-coenzyme A-racemase (AMACR), but showed scattered patchy staining with basal cell markers (p63 and CK903/34βE12). Hence, the final histologic diagnosis was benign prostatic tissue with a focus of florid adenosis. Two subsequent follow-up prostate needle biopsies performed six and 12 months later both showed benign prostatic tissue with atrophic changes. This case highlights the utility of these three immunostains (AMACR, p63 and CK903/34βE12) in the accurate diagnosis of adenosis of the prostate on needle biopsy, and avoiding its misinterpretation as prostate adenocarcinoma.

Background

Atypical adenomatous hyperplasia (AAH) or adenosis of the prostate is a pseudoneoplastic lesion that can mimic prostate adenocarcinoma because of its cytologic and architectural features [1][2][3]. For many years, atypical epithelial lesions of the prostate have been known to occur, but much refining of this knowledge has evolved over the last two decades. Initially two lesions, prostatic intraepithelial neoplasia (PIN) and AAH, were assumed to be precursors of prostatic adenocarcinoma [1][2][3]. However, PIN now remains as the only well-proven preneoplastic condition with clinical significance. AAH is no longer considered a premalignant lesion but rather a benign small glandular process of the transition zone that simulates acinar adenocarcinoma [1][2][3]. Since AAH occurs predominantly in the transition zone, which is only rarely sampled in needle biopsy, it is uncommon to see examples of this lesion in biopsy specimens. However, as the sampling of the transition zone of the prostate has become more frequent recently with ultrasound-guided multiple segmental prostate biopsies [4] the practicing surgical pathologists must be aware of the histologic features of AAH of the prostate in needle biopsy specimens, in order to avoid misinterpretation of AAH of the prostate, a benign lesion, as prostate adenocarcinoma. Additionally, because some patients showing raised prostate specific antigen levels and negative peripheral zone biopsy may present with transition zone prostatic adenocarcinoma, sampling of the transition zone of the prostate by needle biopsy and the identification of AAH are likely to increase [4].

Case presentation

A 62-year-old male was referred to his urologist for symptoms of urinary obstruction and an elevated serum prostate-specific antigen (PSA) level of 3.61 ng/mL. Digital rectal examination and ultrasound revealed an ill-defined nodule in the prostate suggestive of malignancy. A needle biopsy was performed.

The histologic section of one needle biopsy was characterized at low-power examination by the replacement of normal prostatic tissue by a proliferation of haphazardly arranged glands, partially arranged in an ill-defined nodule. The lesions had an infiltrative aspect at their edge, but the glands were not admixed with normal prostatic acini. At medium power, the proliferating glands were often slitlike, variably sized and shaped, alternating small and rounded acini with elongated and branching ones ( Figure  1A &1B). Occasional solid nests and cords could be seen. The atypical acini were lined by epithelial secretory cells with clear eosinophilic cytoplasm ( Figure 1C). The nuclei were regular, rounded to oval, and slightly larger than those of the adjacent normal prostatic acini. There were inconspicuous nucleoli. There were a few focally prominent basally located cells with dense amphophilic cytoplasm. Eosinophilic crystalloids were present within some acinar lumina ( Figure 1C &1D).

Immunohistochemical (IHC) stains revealed strong reactivity for alpha-methylacyl-coenzyme A-racemase (AMACR) in both small and large glands (Figure 2A &2B), patchy reactivity for p63 ( Figure 2C) and patchy reactivity for high-molecular-weight cytokeratin CK903/34βE12 ( Figure 2D).

The final histologic diagnosis was benign prostatic tissue with a focus of florid AAH. Two subsequent follow-up prostate needle biopsies performed six and 12 months later both showed benign prostatic tissue with atrophic changes.

Discussion

AAH of the prostate is a microglandular lesion and a recognized mimicker of small acinar adenocarcinoma [1][2][3]. AAH is characterized by a proliferation of prostatic glands with abnormal architectural patterns, but without significant cytologic atypia. AAH of the prostate is usually an incidental finding in transurethral resections or simple prostatectomies performed in the clinical setting of benign prostatic hyperplasia. The prevalence of AAH in Histologic (hematoxylin-eosin) findings of atypical adenoma-tous hyperplasia of the prostate transurethral prostatectomy specimens without cancer ranges from 1.6% to 7.3% [5] compared to its prevalence of 0.8% in needle biopsy specimens [6]. Their rarity in needle biopsies of the prostate is attributable to the fact that sampling of the transition zone is not common in needle biopsies. Although, AAH can be diagnosed throughout the prostate, it is most often located in the transition zone of the prostate in intimate association with benign nodular hyperplasia [7]. Since the frequency of needle biopsies, including tissue from the transition zone, is likely to increase due to the introduction of ultrasound-guided multiple segmental prostate biopsy [4], knowledge of the main diagnostic histologic features of AAH would represent an important issue in genitourinary surgical pathology. The diagnosis of AAH in needle biopsies, as seen in the case herein presented, relies on both histologic features in hematoxylin and eosin-stained slides and immunohistochemical features.

Microscopically, AAH is a localized proliferative lesion consisting of small amounts of atypical epithelial cells arranged in irregular glandular patterns, often within or adjacent to typical hyperplastic nodules [8]. At low magnification, it is usually partially circumscribed with a pushing rather than infiltrating border, although the small acini may show a limited degree of infiltrative features at the margins. The individual glands are closely packed but separate and show no evidence of fusion. The glands show some variation in size and shape and are lined by cuboidal to low columnar cells with moderate to abundant clear or lightly eosinophilic cytoplasm [6,9]. The basal cells are usually recognized at least focally. The luminal borders are often irregular and somewhat serrated in contrast to the rigid borders that typify small acinar carcinoma. The lumens are often empty but may contain corpora amylacea and in some instances luminal eosinophilic crystalloids [5,9]. The nuclei are round to oval, slightly enlarged, and with uniform fine chromatin and inconspicuous or small nucleoli [9]. AAH can be difficult to distinguish from low-grade prostatic adenocarcinoma (Gleason pattern 1 or 2) because both are located in the transition zone and show small acinar proliferation and intraluminal crystalloids [9]. The two distinguishing features of AAH are the lack of significant cytological atypia and the presence of patchy basal cells, which can be demonstrated by patchy immunostaining for high molecular-weight cytokeratin (CK903/34βE12) or p63 [7][8][9]. In contrast, prostatic adenocarcinoma usually shows notable nuclear atypia, lacks basal cells, and rarely expresses high molecular weight cytokeratin [10]. Yang and colleagues [11] found that AMACR was focally expressed in 10% of cases and diffusely positive in only 7.5% of cases of AAH. The biological significance of AMACR expression in a small subset of AAH remains to be determined. AAH differs from sclerosing adenosis, another benign mimicker of prostate adenocarcinoma, since sclerosing adenosis displays myoepithelial features of the basal cells and an exuberant stroma of fibroblasts and loose ground substance [12].

While circumstantial evidence exists, there is lack of proof of a relationship between AAH and adenocarcinoma. It has been suggested that AAH is a precursor of some lowgrade transition zone carcinomas but the lack of an increased prevalence of AAH in prostate glands with transition zone carcinoma argues against this hypothesis. Clearly, there is less evidence linking AAH to carcinoma than there is for high-grade PIN and cancer. Therefore, the major importance of AAH is its potential for being misdiagnosed as adenocarcinoma. Biochemical and molecular analyses of AAH have generated inconclusive results. There is limited data that AAH has a proliferation rate higher than hyperplasia but lower than adenocarcinoma [1,7,8,13]. By the use of fluorescent in situ hybridization analysis, chromosomal anomalies were seen in only 9% of AAH cases, compared with 55% of prostatic adenocarcinoma cases [13]. Two independent studies showed that AAH contains genetic alterations commonly found in early prostatic carcinoma, with changes being reported in 47% or 12% of AAH cases, respectively [8,14]. Recent cytogenetic analyses have detected abnormalities of chromosome 8 in a very small proportion (4-7%) of AAH cases [7,8]. The recent finding of molecular alterations in AAH including immunoreactivity for AMACR, a marker linked to prostate adenocarcinoma, suggests that at least a subset of AAH cases might be related to prostate carcinoma of the transition zone [11,13,14].

The widespread use of PSA screening has led to an increase in prostate needle biopsies and, subsequently, an increase in earlier detection of prostate carcinoma. This trend has also led to an increase in the number of equivocal diagnoses on prostate biopsy specimens. Surgical pathologists must make critical decisions on an increasing number of prostate needle biopsy specimens with only small foci of atypical glands. In this setting, the mimics of prostate cancer must be distinguished from a small focus of adenocarcinoma. The distinction of benign small acinar proliferations (benign mimickers of cancer) from atypical acinar proliferations suspicious for cancer is crucial, since the subsequent clinical approach is different. Biopsies harboring a small focus of atypical glands frequently represent an under-sampled cancer and a subsequent biopsy will show cancer in up to 50% of cases [15]. In contrast, following a diagnosis of benign mimickers of cancer (such as atrophy or AAH), a re-biopsy is usually not indicated.

Conclusion

AAH is a transition zone lesion of the prostate that can simulate small acinar carcinoma. By itself positive immu-nostaining for AMACR is not diagnostic for carcinoma because the latter is also positive in high-grade PIN and some benign mimickers of adenocarcinoma. The case herein presented highlights the utility of AMACR, p63 and CK903/34βE12 immunostaining in the accurate diagnosis of adenosis of the prostate, a benign mimicker of prostate adenocarcinoma.

Figure 1
1Histologic (hematoxylin-eosin) findings of atypical adenomatous hyperplasia of the prostate. (A) Crowded haphazardly arranged variably sized glands with infiltrative appearance of glands at the edges. Original magnification × 200. (B) Predominantly small glands lined by epithelial secretory cells with clear eosinophilic cytoplasm and minimal cytological atypia. Original magnification × 400. (C) Predominantly large glands lined by epithelial secretory cells with clear eosinophilic cytoplasm and minimal cytological atypia. Original magnification × 400. (D) Predominantly large glands lined by epithelial secretory cells with clear eosinophilic cytoplasm, minimal cytological atypia with inconspicuous nucleoli, few focally prominent basally located cells with dense amphophilic cytoplasm, and luminal eosinophilic crystalloids. Original magnification × 600. Immunohistochemical findings of atypical adenomatous hyperplasia of the prostate Figure 2 Immunohistochemical findings of atypical adenomatous hyperplasia of the prostate. (A) Strong reactivity for alpha-methylacyl-coenzyme A-racemase in predominantly small glands. Original magnification × 200. (B) Strong reactivity for alpha-methylacyl-coenzyme A-racemase in predominantly large glands. Original magnification × 200. (C) Patchy reactivity for p63 in both small and large glands. Original magnification × 200. (D) Patchy reactivity for high-molecularweight cytokeratin CK903/34βE12 in both small and large glands. Original magnification × 200.
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AcknowledgementsConsent was received from the patient before publication.Competing interestsThe authors declare that they have no competing interests.Authors' contributionsHBA participated in the histopathological evaluation, performed the literature review, acquired photomicrographs and drafted the manuscript. AVP conceived and designed the study, gave the final histopathological diagnosis and revised the manuscript for important intellectual content. Both authors read and approved the final manuscript.
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