SMATH® is suitable for automatic multidisciplinary treatment on patients with non-specific sub-acute and chronic LBP.Low back pain (LBP) is a major health problem in modern society, with 70-85% of the population experiencing LBP at some time in their lives. Each year, 5-10% of the workforce misses work due to LBP, most for less than 7 days. Almost 10% of all patients are at risk of developing chronic pain and disability. Little clinical evidence is available for the majority of treatments used in LBP therapy. However, moderate evidence exists for interdisciplinary rehabilitation, exercise, acupuncture, spinal manipulation, and cognitive behavioral therapy for subacute and chronic LBP. The SMATH® versus sham therapy  determined by evaluating self perceived physical function with Roland Morris Disability Questionnaire (RMDQ) scores after 4 weeks of treatment (end of treatment). Major secondary outcome will be effectiveness of SMATH® determined by evaluating self perceived physical function comparing RMDQ scores between end of treatment and baseline. The trial part of the study will take 7 months while observational follow-up will take 11 months. The sample size will be 72 participants (36 for each arm). The project has been approved by the Ethical Committee of Cremona Hospital, Italy on 29 November 2010.This paper describes the protocol for a double-blinded, sham-controlled, randomized, single-center short term clinical trial in patients with non-specific sub-acute and chronic LBP aged 18 to 70 years. The primary outcome will be the effectiveness of SMATHCompared to other medical specialties, physical and rehabilitation medicine (PRM) has not yet received the deserved recognition from clinicians and researchers in the scientific community, especially for medical devices. The best way to change this disadvantage is through well-conducted clinical research in sham-controlled randomized trials. Sham treatment groups are essential for improving the level of evidence-based practice in PRM. The present trial will counter the general lack of evidence concerning medical devices used in LBP therapy.ISRCTN08714168ISRCTN:  Low back pain (LBP) is a major health problem in modern society; 70-85% of the population will experience LBP at some time in their lives . Each yeLBP has been difficult to treat solely using medical interventions due to the complex interplay of biological, psychological, and social factors in its onset and persistence. LBP is usually a benign and self-limiting condition, but many patients look for some type of therapy to relieve their symptoms and provide hope for a cure. For this reason, more than 50 potential therapies promise to relieve the pain ,9, lesse® is a new medical device that combines basic principles of mechanical massage, thermotherapy, acupressure, infrared therapy, and moxibustion. SMATH® is included in the "conservative treatments" category [® system should theoretically be capable of combining the single clinical benefits of massage, thermotherapy, infrared, and acupressure, removing the dependency on operator capability, though obviously missing some elements of human touch, and providing maximum automaticity and reproducibility and producing a real clinical added value for patients. The literature involving different reviews show that little clinical evidence exists on the use of these endorsed therapies on their own. In particular, there is little evidence for massage performed by hands or a mechanical device, or and for superficial heat release [® system was developed in an attempt to create an effectiveness advantage by combining different basic theoretical principles. The first clinical results, from the safety clinical study involving 46 patients inside the process to obtain the European Conformity (CE) mark for SMATH®, showed the following: RMDQ average score of 10.96  at baseline and 3.21  after 3 months, and the average quality adjusted life year (QALY) was 0.46  at baseline and 0.81  after 3 months. These data have not been published because they were not suitable for publication and represented the results obtained by one clinical study in which the primary outcome was the demonstration of clinical safety. The internal clinical report concerning this study has been delivered to the notified body in charge of the CE mark process. The SMATH® system obtained the CE mark on 20 December 2010. The current RCT was designed to investigate and test the SMATH® system for use in LBP therapy.SMATHcategory . This de release . No evid release , or for  release . The SMA® system may also be interesting and could play an important role in the Italian NHS strategy for LBP interventions. An HTA procedure has already been launched in Italy for this new therapy. Like all new technologies and interventions, the clinical effectiveness of the SMATH® system has to be proven by clinical studies in a PRM context. Compared to other medical specialties, PRM has not yet received the recognition deserved from clinicians and researchers in the scientific community, especially for medical devices [® on patients with LBP. The SMATH® system has the CE mark as it has passed all tests concerning safety for the patient, operators, and environment. For this reason, no adverse events are expected during this clinical study. However, if adverse events occur, they will be reported to the regulatory authority and ethics committee in accordance with applicable regulations. Trial results will be used to inform HTA procedure already involving SMATH® therapy for health care policies regarding the treatment of LBP in Italy.The cost/effectiveness ratio for the SMATH devices . We thin® therapy and compared to 36 participants treated with sham therapy . All participants are patients with non-specific sub-acute and chronic LBP, aged 18 to 70 years. Exclusion criteria verification will be done at baseline (T1) with the identification of eligible participants. One week after T1 (T2), eligible participants who have signed informed consent and satisfy the inclusion criteria will be randomized into either the SMATH® or sham groups. Participants who are not randomized will be submitted to the most suitable therapy by the investigators and sent to TF1 to enter in the observational follow-up. Randomized participants will attend clinic visits at T2 (one week after T1), T3 (two weeks after T2) and T4 (two weeks after T3). The design of the clinical trial is depicted in Figure This is a double-blinded, sham-controlled, randomized, single-center short term clinical trial involving 72 participants. Thirty-six participants will be treated with SMATHSubjects with sub-acute and chronic non-specific LBP diagnosed according to generally accepted scientific criteria  were enrExclusion criteria were as follows: pregnancy  or breast-feeding; receiving physical therapy treatments in the 15 days preceding T1; treatment with cortisone in the month preceding T1, implanted with pacemaker or implantable cardioverter defibrillators (ICD) or, more generally, a user of active implantable devices; use of medullar stimulators and infusion pumps; recent or ongoing deep venous thrombosis (DVT); use of spine stabilization devices; serious osteoporosis associated with bone fracture risk, including soft or acutely infected bone tissues; acute cardiovascular disease; neoplastic disease; systemic rheumatic disease; or traumatic spinal episodes in the 3 months preceding T1. The rationale for the exclusion criteria is provided in Table A special area inside the Head Investigator's office in the Hospital will be dedicated to data collection and storage. Case report forms (CRFs) for the enrolled subjects will be kept strictly inside this area. Treatment plan sheets on which the treatment plan for each randomized participant will be recorded will be delivered externally by the technician in charge of executing the treatment. Before randomization, randomization envelopes will be held in this area by the Head Investigator and remain sealed, even after delivery, until used by the technician in charge of administering the treatment. Access to the CRFs by the investigators and monitor will be authorized by the Head Investigator. The technicians in charge of administering treatment will be responsible for storing the envelopes and treatment plan after randomization and treatment plan; they will have to practice maximum confidentiality about their contents as investigators will not have access to this information.A simple randomization process has been conceived through three different steps according to the Consolidated Standards of Reporting Trials (CONSORT) statement: sequence generation, allocation concealment, and implementation.Sequence generation has been achieved through a "congruential multiplier" statistical algorithm application, using IBM Statistical Package for Social Sciences (SPSS) Version 19 [rsion 19 ,22 and wallocation concealment, the randomization list was produced in three numbered copies. The first copy was delivered to the Ethics Committee inside an envelope sealed with warm sealing wax. A second copy was delivered to the Head Investigator in a similar sealed envelope. The Head Investigator will save his copy in a locked box in his office. He can open the envelope in the case an emergency arises. A third copy has been kept by the monitor of the study who managed the sequence generation. The monitor also prepared 72 opaque red envelopes containing randomization sheets. On each envelope is a number (from 1 to 72) and space for the patient's name and date of birth. The randomization sheet (opaque red) reports the progressive randomization number and assigned treatment (A or B) based on the randomization list. All of the envelopes have been closed and sealed with warm sealing wax. The 72 randomization envelopes were delivered to the Head Investigator, who will keep them in a locked box in his office. At the time of randomization, the investigator who decides to randomize the participant will take the first of the envelopes from the box in order of sequence and write the participant's name and date of birth on the envelope . Immediately after, the investigator will deliver the closed envelope and intervention plan to the technician in charge of administering the treatment. The technician will open the envelope to start the intervention, saving the envelope and randomization sheet in a locked box. The technicians involved in this phase of the study have been trained and educated to maintain maximum confidentiality concerning allocation concealment. This method will provide double-blinded conditions. Clinical investigators who enroll and evaluate participants will not know which treatment has been assigned to them.For Implementation of the randomization process will conform with the study requirements and steps described above. The process involves participant eligibility analysis, obtaining patient information, enrollment, treatment assignment, and data analysis to avoid any kind of bias. The implementation process is depicted in Figure ® therapy group (A arm) will have 14 treatment sessions of 45 minutes over 4 weeks starting at T2. The treatment module is associated with a dedicated educational booklet for each patient that provides general and behavioral indications for preventing LBP. In this clinical study, the educational booklet will be delivered to all enrolled participants to help them correct their lifestyle during and after the intervention period. In this dedicated booklet, different sections explain to the participants spine anatomy, LBP, LBP prevention, advantages of physical activity, suggestions for positions for walking, sleeping, and to keep weight under control, and exercises to prevent LBP.Participants assigned to the SMATH® system will be set-up with treatment variables  conforming to the scheduled treatment data sheet delivered to the technician after randomization. Sham therapy will be implemented using a dedicated SMATH® device. This medical device, which was conceived and manufactured exclusively for this clinical study, has the same external look and design of the SMATH® device. The active principles (thermomechanic and infrared energy release) have been fully blocked in the sham device, but the participants' perceptions will be more or less the same . The engineering department has made important efforts to create a device with the appearance of the active machine, inducing feelings similar to those of the real device [® device with 90% less thermal power (just to induce the feeling of heat). Infrared emission has been removed, as well as the active rollers that release mechanical energy to the back and legs, replaced by an inert, flat wood plate. The sham device will be set-up with the same treatment variables as the SMATH® device. The noise and vibration generated by the sham device will be the same as that of the SMATH® system because the devices have the same mechanical configuration, including the same motor and transmission equipment, operating in the same mode. Despite this similarity, a therapeutic placebo effect could be introduced by the sham device. This risk has been considered and accepted as a natural risk of the RCT with a sham control.During the first and second week (from T2 to T3), 4 sessions will be done per week, whereas 3 sessions per week will be planned for the third and fourth week (from T3 to T4). The SMATHl device . This ef® arm (14 sessions in four weeks from T2 to T4). The participants in the sham arm will also receive the educational booklet. The SMATH® and sham devices are located inside two different therapy boxes and treatment will be given at separate times to prevent contact between participants in the different treatment arms. The operators managing treatments will be the same for the SMATH® and sham devices, and they have been trained to answer the participants' questions about the procedures. The same operators will also be trained and educated in keeping participants in the study, avoiding an increased drop-out rate. The main features of the SMATH® system and sham version are presented in Figures Participants assigned to the sham therapy arm will receive the same treatment module scheduled for the active SMATHThis clinical study was designed to include the following general outcome measures: pain, overall improvement, back-specific functional status, well being , and disability. The questionnaires that will be used to collect data from the participants are: visual analog scale (VAS) for pain , Roland ® system versus sham therapy after 4 weeks of treatment in patients with sub-acute and chronic non-specific LBP, determined by evaluating self perceived physical function with RMDQ scores. The status of physical function in participants treated with SMATH® interventions will be compared with the status achieved in participants in the sham control arm at the end of treatment (T4) [The primary outcome of the trial is represented by the clinical effectiveness of the SMATHent (T4) .® determined by evaluating self perceived physical function comparing RMDQ scores between end of treatment and baseline. 2) Pain perception change from baseline (T2) to end-treatment (T4), evaluated by VAS scores. 3) Quality of life at different steps of the study (T2 and T4), evaluated by EQ-5D scores (QALY). 4) Verified general feasibility of the medical device compared to sham therapy in terms of methodology, sample size, and drop-out rate, among other measures, to constitute a real pilot study and act as a possible reference for future generations of RCTs involving the medical device, conforming to the evidence-based medicine (EBM) criteria for LBP therapy [The secondary outcomes of the trial are: 1) Effectiveness of SMATH therapy  accordin therapy .Observational follow-up will start at TF1 (T4) and cover 11 months. During this period, all of the participants will come to the treatment centre to attend clinic visits one month after TF1 TF2), 3 months after TF2 (TF3), 3 months after TF3 (TF4), and 4 months after TF4 (TF5). Figure , 3 month® versus sham will be determined by evaluating self-perceived physical function with RMDQ scores at T4. The secondary outcome of SMATH® therapy effectiveness will be determined by evaluating improvements in patients' motion functionality between T2 and T4 using RMDQ scores. The evolution of pain perception during the study will be measured by VAS scores submitted by the participants at T2, T3, and T4. Quality of life for participants will be estimated by the comparing the EQ-5D questionnaires submitted at the beginning and end of treatment (T2 and T4). During the observational follow-up period all participants will be examined at TF1, TF2, TF3, TF4, and TF5 for general patient status: anamnesis, blood pressure, heart rate in beats per minute, and morphological variables . The RMDQ and VAS questionnaires will be given on the participants at TF1, TF2, TF3, TF4, and TF5, and EQ-5D will be given at TF1, TF2, and TF5. All of the data collected during the study and follow-up will be reported to the CRF.Four steps of participant examination are planned in this RCT ; during which, general clinical data will be collected about the persistence of LBP symptoms, previous treatment, morphological data, patient activity, blood chemistry tests, anamnesis, and eventual clinically important imaging investigations that were recently done. The effectiveness of SMATH® active principles to obtain a sham device. The therapeutic effect induced by the sham device has been estimated to be 10% of the effect induced by the SMATH® system. Consequently, the probability of SMATH® therapy success has been fixed at 50% (P1) [2) has been fixed for success with sham therapy. Thus, the success difference between the two study arms (P1 - P2) is 40%. This important delta has been fixed to be statistically significant also in the case of unexpected placebo effect of sham. The false-positive risk factor α (type I error rate) has been fixed at 0.05 (5%). The probability of discovering a difference between the two study arms has been fixed at 90% (study's power). Based on the study's power, (1 - β) = 0.9, the false-negative risk factor rate β is 0.1 (10%). The value of the function f  was calculated by Geigy's tables [2 [2 = 1.3841, and the sample size after correcting for drop-out will be:n# = nμ = 36. The sample of each group will be 36 participants for a total study group of 72 subjects.Due to the difficulty of finding solid references in the literature regarding the probability of success for different arms of RCTs comparing therapy with medical devices versus sham therapy , some ba50% (P1) , whereass tables . Thus, ts tables . The samables [2 -26, wher® versus sham) will be estimated using Kaplan-Meier curves, which will be compared using the log-rank test. Multivariable analysis will be performed using the Cox model.The accuracy and completeness of the collected and registered data will be checked daily by clinical investigators to provide a suitable level of quality for the study. The external monitor will be responsible for arranging random periodic tests of quality throughout the study. In particular, controls will involve the quality and completeness of the CRFs, the treatment execution modalities, the drop-out rate, and correct respect of enrollment criteria application. Data analysis will be conducted using IBMSPSS 19 ,22. The ® treatment module.Correct involvement and informed participant processes are key issues for the success and credibility of the study. Clinical investigators will be responsible for informing participants and involving them in the most appropriate way. This goal will be pursued by discussing the study objectives, risks, benefits, and modalities with participants at T1. Each participant will make a decision as to whether they will participate in the study, which will have to be clearly checked by investigators and documented. In this study, clinical investigators will submit and explain to each participant the following documents: (1) signed informed consent, (2) information module, (3) authorization to use personal sensitive data based on privacy law, (4) information letter that will be delivered to the family physician, (5) the dedicated educational booklet. This booklet must be considered a part of the SMATHlisted in additional file Adverse events will be managed to conform with good clinical practice and actual normative rules, Eventual unplanned and important changes to the study protocol involving, for instance, eligibility criteria, interventions, examination data collection, method of analysis, and outcomes, will be submitted to the ethics committee as amendments for approval. In the case of approval and application, they will be reported at the end of the study. All deviations from the protocol during its execution will have to be justified and documented in CRFs for the participants.In this manuscript, the following abbreviations have been used:®: System for automatic thermomechanic massage in health; VAS: Visual analog scale.BMI: Body Mass Index; CRF: Case report form; CE: Conformité Européenne (European Conformity); CONSORT: Consolidated Standards of Reporting Trials; DVT: Deep venous thrombosis; EBM: Evidence-based medicine; EQ-5D: Euro Qol 5D questionnaire; HTA: Health technology assessment; ICD: Implantable cardioverter defibrillator; ICER: Incremental Cost Effectiveness Ratio; LBP: Low back pain; MD: Medical device; NHS: National Health System; PRM: Physical and rehabilitation medicine; QALY: Quality adjusted life year; RCT: Randomized controlled trial; RMDQ: Roland Morris Disability Questionnaire; SMATHThe authors declare that they have no competing interests.Trials.PB acts as guarantor of the data, he has designed the interventions and supervises research staff. PB and SM designed the study, which is funded by internal resources. GB, RB, PF, and ELS will be responsible for data collection and analysis, and they contributed to drafting the paper. PB, with SM, reviewed the manuscript critically. RM and FZ will manage the treatment of randomized participants after randomization. All authors reviewed and edited the draft version of the manuscript and approved the final version. All authors have approved the submission of the present paper to List of the Italian and international laws and norms for clinical studies. This additional file contains a list of the italian and international laws and norms which have been respected for this randomized double-blinded controlled trial. This clinical study has been designed and will be managed to conform with the following Italian and international laws and norms listed in additional file Click here for file


Leigh Johnson and colleagues estimate the life expectancies of HIV positive South African adults who are taking antiretroviral therapy by using information from 6 programmes between 2001 and 2010. Few estimates exist of the life expectancy of HIV-positive adults receiving antiretroviral treatment (ART) in low- and middle-income countries. We aimed to estimate the life expectancy of patients starting ART in South Africa and compare it with that of HIV-negative adults.Data were collected from six South African ART cohorts. Analysis was restricted to 37,740 HIV-positive adults starting ART for the first time. Estimates of mortality were obtained by linking patient records to the national population register. Relative survival models were used to estimate the excess mortality attributable to HIV by age, for different baseline CD4 categories and different durations. Non-HIV mortality was estimated using a South African demographic model. The average life expectancy of men starting ART varied between 27.6 y (95% CI: 25.2–30.2) at age 20 y and 10.1 y (95% CI: 9.3–10.8) at age 60 y, while estimates for women at the same ages were substantially higher, at 36.8 y (95% CI: 34.0–39.7) and 14.4 y (95% CI: 13.3–15.3), respectively. The life expectancy of a 20-y-old woman was 43.1 y (95% CI: 40.1–46.0) if her baseline CD4 count was ≥200 cells/µl, compared to 29.5 y (95% CI: 26.2–33.0) if her baseline CD4 count was <50 cells/µl. Life expectancies of patients with baseline CD4 counts ≥200 cells/µl were between 70% and 86% of those in HIV-negative adults of the same age and sex, and life expectancies were increased by 15%–20% in patients who had survived 2 y after starting ART. However, the analysis was limited by a lack of mortality data at longer durations.South African HIV-positive adults can have a near-normal life expectancy, provided that they start ART before their CD4 count drops below 200 cells/µl. These findings demonstrate that the near-normal life expectancies of HIV-positive individuals receiving ART in high-income countries can apply to low- and middle-income countries as well.Please see later in the article for the Editors' Summary According to the latest figures, more than 34 million people worldwide currently live with HIV/AIDS. In 2011, an estimated 2.5 million people were newly infected with HIV, and in the same year 1.7 million people died from AIDS. Since the beginning of the epidemic in the 1980s, more than 60 million people have contracted HIV and nearly 30 million have died of HIV-related causes. Despite the stark statistics, the life expectancy for people infected with the AIDS virus has dramatically improved over the past decade since the introduction of an effective combination of antiretroviral drugs. In high-income countries, people who are HIV-positive can expect a near-normal life expectancy if they take these drugs  throughout their life.Recent studies investigating the life expectancy of people living with HIV have mostly focused on the situation in high-income settings. The situation in low- and middle-income countries is vastly different. People who are diagnosed with HIV are often late in starting treatment, treatments regimes are sometimes interrupted, and a large proportion of patients are lost to follow-up. It is important to gain a realistic estimate of life expectancy in low- and middle-income countries so patients can be given the best information. So in this study the researchers used a model to estimate the life expectancy of patients starting ART in South Africa, using data from several ART programs.The researchers used data collected from six programs in South Africa based in Western Cape, Gauteng, and KwaZulu-Natal between 2001 and 2010. The researchers calculated the observation time from the time of ART initiation to the date of death or to the end of the study. Then the researchers used a relative survival approach to model the excess mortality attributable to HIV, relative to non-HIV mortality rates in South Africa, over different periods from ART initiation.Using these methods, the researchers found that over the time period, 37,740 adults started ART and 2,066 deaths were recorded in patient record systems. Of the 16,250 patients who were lost to follow-up, the researchers identified 2,947 further deaths in the population register. When they inputted these figures into their model, the researchers estimated that the mortality rate was 83.2 per 1,000 person-years of observation (PYO), and was higher in males  than in females . The researchers also found that the most significant factor determining the life expectancy of treated patients was their age at ART initiation: the average life expectancy of men starting ART varied between 27.6 years at age 20 and 10.1 years at age 60, while corresponding estimates in women were 36.8 and 14.4, respectively. Life expectancies were also significantly influenced by baseline CD4 counts; life expectancies in patients with baseline CD4 counts ≥200 cells/µl were between 70% and 86% of those of HIV-negative adults of the same age and sex, while patients starting ART with CD4 counts of <50 cells/µl had life expectancies that were between 48% and 61% of those of HIV-negative adults. Importantly, the researchers found that life expectancies were also 15%–20% higher in patients who survived their first 24 months after starting ART than in patients of the same age who had just started therapy.These findings suggest that in South Africa, patients starting ART have life expectancies around 80% of normal life expectancy, provided that they start treatment before their CD4 count drops below 200 cells/µl. Although these results are encouraging, this study highlights that health services must overcome major challenges, such as dealing with late diagnosis, low uptake of CD4 testing, loss from pre-ART care, and delayed ART initiation, if near-normal life expectancies are to be achieved for the majority of HIV-positive South Africans. With the anticipated increase in the fraction of patients starting ART at higher CD4 counts in the future, long-term survival can be expected to increase even further. It is therefore critical that appropriate funding systems and innovative ways to reduce costs are put in place, to ensure the long-term sustainability of ART delivery in low- and middle-income countries.http://dx.doi.org/10.1371/journal.pmed.1001418.Please access these websites via the online version of this summary at International Epidemiologic Databases to Evaluate AIDS has more statistical information from world regionsThe HIV research, treatment, prevention, and education strategiesamfAR, the Foundation for AIDS Research, works with health care workers and AIDS organizations in developing countries to create and implement effective  Estimates of life expectancies of HIV-infected individuals are important in providing information to patients about their long-term prognosis, in projecting the future costs of HIV-related care, and in forecasting the likely future demographic and socioeconomic impact of HIV/AIDS In South Africa there exists a unique opportunity to obtain accurate estimates of ART mortality in a middle-income country experiencing a generalised HIV epidemic. With around 90% of adult deaths recorded through the country's vital registration system The International Epidemiologic Databases to Evaluate AIDS Southern Africa (IeDEA-SA) is a collaboration of ART programmes in southern Africa This analysis is limited to six programmes providing ART to adults in South Africa We calculated observation time as starting at the time of ART initiation and ending at the date of death or the date of analysis closure, whichever occurred first. Patients were considered to be LTFU if there was no record of their attendance at the clinic for at least 182 d prior to database closure and their patient records did not indicate that they had died. The date of database closure differed for each of the participating programmes, and was calculated as the last recorded visit date in each programme. The date of analysis closure was defined for each programme to be the date 182 d prior to the date of database closure, in order to allow sufficient time to determine the LTFU status of each patient.In the case of patients who were LTFU, the approach to calculating person-years of observation (PYO) differed depending on whether the programme had recorded the patient's civil identity document (ID) number and used this information to check the vital status of the patient against the national population register. In the case of LTFU patients with ID information, we censored observation at the date of analysis closure or the date of death recorded on the population register. Inverse probability weighting was used to ensure that LTFU patients with IDs were weighted up to represent the LTFU patients without IDs; this meant assigning zero weight to the LTFU patients who had no ID (so that the censoring dates for these patients were irrelevant) and assigning weights equal to the inverse of the probability of having ID information, in those patients who had IDs. This approach has been used previously in estimating mortality rates in South African patients receiving ART In one of the cohorts, problems with the recording of date of last clinic attendance prevented us from applying the LTFU definition and using inverse probability weighting. In this cohort, analysis was restricted to those patients with IDs, and observation time was censored at the date of death or date of analysis closure . In a sensitivity analysis we considered the effect of applying the same method to all of the other cohorts.We used a relative survival approach to model the excess mortality attributable to HIV, relative to non-HIV mortality rates in South Africa, over different durations from ART initiation We obtained estimates of non-HIV mortality, by age and sex, from an independent demographic model of the South African population We fitted the relative survival model to the data using a maximum likelihood approach in STATA 11.0 (StataCorp), assuming that the number of deaths in each age, sex, cohort, CD4, and duration category was Poisson-distributed. Parametric bootstrapping was used to generate 1,000 alternative parameter estimates To assess the sensitivity of the results to the high early mortality after ART initiation, we estimated life expectancy for patients who had survived 24 mo since ART initiation. We also limited the analysis to patients with IDs, to assess the effect of longer follow-up time (with later administrative censoring), and also to assess the effect of not applying inverse probability weighting in LTFU patients. To assess sensitivity to non-HIV mortality rates, the relative survival model was refitted after increasing the assumed non-HIV mortality rates by 50%. In addition, we refitted the model using a negative binomial model in place of the Poisson model, to assess possible bias due to over-dispersion Because the relative survival approach differs substantially from the more widely used abridged life table method, we also estimated life expectancy using the abridged life table approach Analysis was based on 37,740 adults who started ART between March 2001 and February 2010. Life expectancies differed substantially between cohorts. For men starting ART at age 35 y, life expectancy varied between 16.6 y (95% CI: 14.4–19.2) and 28.6 y (95% CI: 26.2–30.3). Life expectancy in women starting ART at age 35 y varied between 23.6 y (95% CI: 21.4–25.9) and 35.5 y (95% CI: 33.3–36.9). The average life expectancies in n = 30,287), total PYO increased  and average follow-up time increased (2.75 y) because of the later analysis closure date. In these patients, 1,451 deaths were recorded through patient record systems, and a further 3,760 were identified through the national population register, yielding a crude mortality rate of 62.6 per 1,000 PYO. Modelled mortality rates were lower in patients starting ART after 2006 than in patients starting ART in 2006 or earlier, and life expectancies were therefore calculated separately for the two enrolment periods. Women with recorded ID who started ART in 2006 or earlier had similar life expectancies to those in the main analysis, but men with recorded ID who started ART in 2006 or earlier had life expectancies about 12% higher than those in the main analysis  that are recorded neither in the patient record system nor in the vital registration system. Although this figure is lower than the proportion of deaths missing from the vital registration system because of the added information from patient record systems, it may nevertheless lead to life expectancies of ART patients being slightly exaggerated.Predicting future mortality for HIV-positive patients on ART is challenging. In addition to the historical bias towards enrolment of symptomatic patients, mentioned previously, our mortality estimates do not take into account potential future reductions in mortality that may occur as new drugs and salvage regimens A limitation of this analysis is the short average duration of follow-up (1.84 y), which we have addressed by controlling for differences in mortality by duration. Our method of controlling for differences by duration is based on the assumption that mortality rates are piecewise constant over 1-y duration intervals. To the extent that there is residual variation in mortality by duration, for which we have not fully controlled, our method will be biased towards overestimating mortality when the average follow-up time is short, because the observation time in each duration interval will be weighted toward the lower end of the interval, where mortality rates are likely to be highest. This bias is evident when comparing the results from the main analysis with the results from the analysis that is limited to patients with IDs; the longer average follow-up time (2.75 y) leads to slightly lower mortality rates over each duration interval (results not shown) and hence increased life expectancies. Due to the small number of patients on ART at long durations, mortality data have been aggregated for all durations greater than 36 mo. This approach may be reasonable, as average CD4 counts tend to stabilise after the first 36 mo of treatment The principle advantage of the relative survival model, compared with the abridged life table method that is more commonly used in the estimation of life expectancy, is that it adjusts for differences in mortality by duration, which are particularly marked in resource-limited settings Studies of life expectancies of ART patients in high-income countries that have excluded high-risk groups (people who inject drugs and patients starting ART in advanced disease) have generally estimated life expectancies that are 73%–99% of those in the general population Only one other study in Africa has estimated the life expectancy of patients starting ART The generalisability of our findings—even within South Africa—is open to question. The South African cohorts participating in the IeDEA-SA Collaboration are relatively well-resourced programmes with substantial research support, mostly in urban centres. Life expectancies differed between cohorts because of differences in patients' socioeconomic characteristics as well as differences in models of ART delivery. The average results that we have presented are likely to be typical of public sector programmes in urban areas, but mortality rates may differ in rural treatment programmes and in private sector programmes. The assumption that the average non-HIV mortality rates in South Africa apply in all cohorts could also be problematic, although estimates of life expectancy did not change substantially when the model was refitted with 50% higher non-HIV mortality rates. These findings might not be typical of programmes in most other African countries, as South Africa is an upper middle-income country with rates of non-HIV mortality lower than in most other African countries These results have important implications for the pricing models used by life insurance companies, as well as the demographic and epidemiological models that are used to forecast the impact and cost of ART programmes in low- and middle-income countries. These models have typically assumed that life expectancy after ART initiation is around 10 y Figure S1Cumulative survival after ART initiation, compared to age-standardised survival rates in the HIV-negative population. HIV-positive survival curves are calculated by grouping all ages and cohorts together and applying Kaplan-Meier methods to calculate proportions surviving, after including data from the national population register and applying inverse probability weighting. HIV-negative survival curves are calculated for a hypothetical cohort with the same initial age distribution as the HIV-positive cohort.(PDF)Click here for additional data file.Figure S2Annual mortality rates stratified by age, sex, and time since ART initiation. Dots represent observed mortality rates, standardised to the CD4 distribution in (PDF)Click here for additional data file.Text S1Technical appendix.(PDF)Click here for additional data file.


There is currently much interest in pharmacogenetics: determining variation in genes that regulate drug effects, with a particular emphasis on improving drug safety and efficacy. The ability to determine such variation motivates the application of personalized drug therapies that utilize a patient's genetic makeup to determine a safe and effective drug at the correct dose. To ascertain whether a genotype-guided drug therapy improves patient care, a personalized medicine intervention may be evaluated within the framework of a randomized controlled trial. The statistical design of this type of personalized medicine intervention requires special considerations: the distribution of relevant allelic variants in the study population; and whether the pharmacogenetic intervention is equally effective across subpopulations defined by allelic variants.CYP2C9 and VKORC1; and clinical-guided dosing, the initiation of warfarin therapy based on algorithms using only clinical information.The statistical design of the Clarification of Optimal Anticoagulation through Genetics (COAG) trial serves as an illustrative example of a personalized medicine intervention that uses each subject's genotype information. The COAG trial is a multicenter, double blind, randomized clinical trial that will compare two approaches to initiation of warfarin therapy: genotype-guided dosing, the initiation of warfarin therapy based on algorithms using clinical information and genotypes for polymorphisms in CYP2C9 or VKORC1 and an assumed 15% relative effectiveness of genotype-guided warfarin initiation for those with zero or multiple genetic variants. Thus we calculate a sample size of 1238 to achieve a power level of 80% for the primary outcome. We show that reasonable departures from these assumptions may decrease statistical power to 65%.We determine an absolute minimum detectable difference of 5.49% based on an assumed 60% population prevalence of zero or multiple genetic variants in either In a personalized medicine intervention, the minimum detectable difference used in sample size calculations is not a known quantity, but rather an unknown quantity that depends on the genetic makeup of the subjects enrolled. Given the possible sensitivity of sample size and power calculations to these key assumptions, we recommend that they be monitored during the conduct of a personalized medicine intervention.clinicaltrials.gov: NCT00839657 The recent availability of lower-cost genetic testing has motivated medical researchers to determine whether patient care and safety is improved by using a patient's genetic information to initiate and manage drug therapy . To evalAlthough interventions that use a subject's clinical factors, gene expression profile, or perhaps other factors can also be considered as personalized medicine, we restrict our attention to interventions that use genotype. In addition, personalized medicine interventions may be evaluated using several different study designs. For example, in a targeted design, frequently used to evaluate genetic-based therapies for cancer, study eligibility may be restricted to a marker-positive subset of the population anticipated to benefit from therapy based on their genetic characteristics . We focuWarfarin sodium is the most common oral anticoagulant used for the prevention and treatment of thromboembolism, the formation of a clot in a blood vessel or cardiac chamber that may be carried by the blood stream and obstruct another vessel. Initiation of warfarin therapy is usually based on empiric dosing, which may put patients at an increased risk for either major bleeding complications due to over-anticoagulation or thromboembolic events due to under-anticoagulation. Therefore, initiation of warfarin therapy at an improper dose may be associated with increased costs and higher morbidity .CYP2C9) gene effects pharmacokinetics, i.e., the effects of the body on the drug; and the vitamin K epoxide reductase complex 1 (VKORC1) gene effects pharmacodynamics, i.e., the effects of the drug on the body. Thus, CYP2C9 variants alter S-warfarin metabolism [VKORC1 variants alter warfarin response [CYP2C9 and VKORC1 have proven useful in algorithms to predict the ultimate maintenance dose for optimal warfarin therapy [Many patient-specific clinical factors impact warfarin dose-response. In addition, two genes influence warfarin dose: the cytochrome P-450 family 2 subfamily C polypeptide 9 enzyme  is to conduct a multicenter, double blind, randomized clinical trial that compares two approaches to initiation of warfarin therapy:Genotype-guided dosing, the initiation of warfarin therapy based on algorithms using clinical information and genotypes for polymorphisms in two genes known to influence warfarin response (CYP2C9 and VKORC1); and• Clinical-guided dosing, the initiation of warfarin therapy based on algorithms using only clinical information.• Both approaches will include a baseline dose-initiation algorithm  and a doEligible subjects will be recruited from at least 12 clinical sites in the United States. Clinical and genotype data will be collected on all subjects. Subjects will be randomized to initiate warfarin therapy either using genotype-guided or clinical-guided dosing. All subjects will receive their warfarin on a standard-of-care schedule. Study investigators, clinicians, and subjects will be blinded to treatment assignment and warfarin dose for the first four weeks of the trial. After four weeks of therapy, subjects will be unblinded to dose and followed for up to an additional five months. The Institutional Review Board of all participating institutions approved the COAG trial. Written informed consent will be obtained from all patients who participate in the trial.The primary outcome of the COAG trial is the percentage of time that participants spend within a therapeutic range for anticoagulation (PTTR) during the first four weeks of therapy. The therapeutic range is defined using the International Normalized Ratio (INR), which reflects the ratio of a patient's prothrombin time to that for a control sample. An INR between 2.0 and 3.0, inclusive, is typically considered to be within the therapeutic range. To calculate the PTTR for each subject, we will use a standard interpolation method that assumes a linear change in INR from one measurement to the next . Figure Analysis of the primary outcome will be by intention-to-treat . It willVKORC1 (not CYP2C9) [VKORC1 and CYP2C9, so that the availability of genetic information by day two will allow the full use of the subject's genetic information to determine their dose for days two through five. We fully expect genotype information to be available on almost all subjects within 24 hours, and certainly by the time of the dose-revision calculations on days four and five.For those subjects assigned to the genotype-guided dosing group whose genetic information is not available prior to the initial dose, the initial dose will be determined using the clinical dose-initiation algorithm. Once genetic information becomes available, the dose for these subjects will be determined using the genetic dose-initiation and dose-revision algorithms. The genotype-guided dose-initiation algorithm on day one only uses information on  CYP2C9) . TherefoCYP2C9 and VKORC1 variants and is associated with the prevalence of other genetic variants that influence warfarin dose-response. Finally, some clinical sites may recruit a small number of African Americans due to the demographic makeup of their surrounding community.To provide balance in treatment assignment within sites, random assignment to either the genotype-guided or clinical-guided dosing group will be stratified by clinical site. Randomization will also be stratified by race  because race is associated with differential predictive ability of dosing algorithms, with lesser accuracy in African Americans , and theWe will use a block-randomized procedure to assign the treatment groups. Blocking ensures that there will be a balance in the number of patients in each treatment group within each clinical site. Thus, we will use permuted blocks with block sizes of four and six, randomly chosen, which will minimize any imbalances in treatment group assignment. The RANUNI function in SAS 9.2 will be used to generate the randomization numbers within each site for two strata .A critical element of the statistical design of a randomized clinical trial is to determine a sample size so that a statistical test has adequate power to detect a clinically relevant difference in the primary outcome between treatment groups. The parameters considered in the estimation of sample size include: a minimum detectable difference in the primary outcome between groups; an assumed level of significance for the statistical test of the primary outcome; a measure of variability for the primary outcome in the study population; and the percentage of subjects, if any, expected to drop out of the trial. The sample size parameters for a personalized medicine intervention require additional considerations: the distribution of relevant allelic variants in the study population; and whether the intervention is equally effective across subpopulations defined by allelic variants . Due to uncertainly in the distribution of allelic variants and uncertainty in the effectiveness of the intervention across subpopulations, careful attention is required in the design of a personalized medicine to ensure that the study will have adequate power to detect a clinically relevant minimum detectable difference.CYP2C9 or VKORC1. We viewed the primary outcome of PTTR in each treatment group as a weighted average of PTTR and the corresponding treatment effect (Δ) across subpopulations defined by 1 versus 0, > 1 variants, in which the weights (w) are determined by the populations prevalences that sum to 1:In the statistical design of the COAG trial, we focused on the difference in the relative effectiveness of genotype-guided across two genetic subpopulations: those with a single genetic variant versus zero or multiple genetic variants in either C and PTTRG denote the PTTR in the clinical-guided and genotype-guided dosing groups, respectively. It is straightforward to generalize this approach to more than two subpopulations of interest. Adding additional terms into the weighted average, given the population prevalence and the anticipated treatment effect in each subpopulation, could accommodate more than two subpopulations. Indeed, this approach is generalizeable to any setting in which treatment effects are expected to differ across any number of subpopulations. Specific assumptions are discussed in the following section.where PTTRCYP2C9 and VKORC1 variants and whether genotype-guided dosing of warfarin is equally effective across groups defined by CYP2C9 and VKORC1 variants. Current evidence suggests that there will be a subgroup with certain genotypes that will not benefit from genotype-guided dosing [We considered the distribution of d dosing , most liwhere:CYP2C9 or VKORC1) and who possess zero or multiple variants is assumed to be 0.4 and 0.6, respectively;• The proportion in the population who possess a single genetic variant  would not benefit from clinical-guided dosing because previous data suggest that the genotype-guided algorithm will predict essentially the same dose as the clinical-guided algorithm. These subjects are expected to attain the same PTTR regardless of their treatment assignment and thus attenuate the mean difference in PTTR between the two groups. To wit, the assumed 15% relative difference in PTTR between the genotype-guided and clinical-guided dosing groups is attenuated to an absolute difference of 5.49% (PTTRG - PTTRC). Therefore, we assumed an overall minimum detectable difference of 5.49% between groups in the full cohort for sample size calculations. If we had ignored the fact that the intervention is not equally effective across subpopulations defined by genetic variants and assumed a minimum detectable difference of 15%, then the trial would have chosen an inadequately small sample size to achieve adequate power.We assumed that subjects who possess a single genetic variant  of the overall level of significance is used to test the comparison in the full cohort; the remaining portion (αS) is used to test the comparison in a pre-defined primary subgroup. The alpha-allocation approach facilitates a traditional primary analysis to assess a statistically significant difference between the treatment groups, as well as a predefined primary subgroup analysis that is not relegated to a secondary analysis, as in a traditional analysis.To determine the level of significance (α) for the statistical test of PTTR between the genotype-guided and clinical-guided dosing groups, we considered an alpha-allocation approach -19. In tWe defined the primary subgroup based on subjects whose predicted initial dose employing the genetic and clinical dose-initiation algorithms differs by ≥ 1.0 mg, a factor known at the time of randomization and therefore not a post-randomization selection. We posited that the subgroup of participants with a larger difference between the predicted initial doses should have a larger separation in PTTR between the two groups. If the improvement in PTTR is related to the magnitude of difference in dosing between the genotype-guided and clinical-guided dosing groups, then the primary subgroup comparison should reflect a larger absolute difference than the 5.49% assumed for the full cohort analysis. We assumed that a clinically relevant absolute difference to detect in the primary subgroup is 9.15%, from a PTTR of 61% to 70.15% in Equation (4), reflecting a 15% relative difference.A = 0.04 for the full cohort analysis and αS = 0.01 for the primary subgroup analysis, for an overall type-I error rate of α = 0.05. However, allocating alpha so that sum of αA and αS is equal to α is a conservative Bonferroni-type correction, which may be unnecessarily conservative if there is a positive correlation between the tests in the full cohort and in the primary subgroup [S > α - αA given that αA is fixed, so that the overall type-I error rate is controlled at α.We selected αsubgroup ,21. The Other assumptions in the computation of sample size were the standard deviation of the PTTR in the study population and the percentage of subjects expected to drop out before reaching the primary endpoint. The within-study variability of PTTR in the literature varied across study designs and populations under study. However, there was a reasonable consistency of variability for the genetic-guided and clinical-guided dosing groups in the studies reviewed. We assumed a standard deviation of 25% based on a study of dose-refinement algorithms in which the standard deviation averaged 23% . We alsoa priori by allelic variation (zero versus a single versus multiple CYP2C9 or VKORC1 variants) and by race (African American versus not).The null hypothesis for the primary outcome is that the percent of time that subjects spend within the therapeutic INR range (PTTR) during the first four weeks of therapy is equal between the genotype-guided and clinical-guided dosing groups. We will estimate the difference in mean PTTR between the genotype-guided and clinical-guided dosing groups using a linear regression model, both for the full cohort and for the primary subgroup whose predicted initial dose employing the genetic and clinical dose-initiation algorithms differs by ≥ 1 mg. Inference will be based on a Wald test with a level of significance of 0.05 allocated between the full cohort analysis and the primary subgroup analysis. Because randomization will be stratified by site and race, these variables will be included in the linear regression model. We will perform additional analyses in subgroups defined CYP2C9 and VKORC1 genotypes may not be the only genetic variants that determine optimal warfarin dosing, it is possible that more variants will be identified during the trial. To adjust for additional genetic factors in secondary analyses, we will include them as covariates in a linear regression model if their prevalence differs between the clinical and genetic groups, and also consider possible interactions with CYP2C9 and/or VKORC1.Additional genetic factors may be considered in secondary analyses. Specifically, because t-test with αA = 0.04 (two-sided), assuming various proportions with a single genetic variant , estimates for the standard deviation of PTTR , and power levels (80% and 90%), and drop-out rate (10%). A sample size of 1140 would provide 90% power to detect an absolute difference of 5.49% in the full cohort, given that the proportion with a single genetic variant is 0.4 and the standard deviation is 25%. We selected a sample size of 1238 to protect against departures from the assumed proportion with a single genetic variant, study drop-out rate, and standard deviation of PTTR. For example, if the proportion with a single genetic variant is 0.5 and the standard deviation is 25%, then there is 80% power to detect an absolute difference in PTTR of 4.58%. If the proportion with a single genetic variant is 0.4 and standard deviation is 30%, then there is 80% power to detect the assumed 5.49% absolute difference.Table t-test with αS = 0.01 (two-sided). Recall that the size of the primary subgroup is determined by the percentage of subjects whose predicted initial dose employing the genetic and clinical dose-initiation algorithms differs by ≥ 1 mg. If the relative size of the primary subgroup is 50% and the standard deviation of PTTR is 25%, then there is 93.6% power to detect a 9.15% absolute difference. In addition, if the relative size of the primary subgroup is 60% and the standard deviation is 30%, then there is 87.8% power. In fact, the power will be higher because αS will be increased according to the correlation between the tests in the full cohort and in the primary subgroup.A sample size of 1238 provides sufficient power for the primary subgroup analysis using a two-sample In the statistical design of the COAG trial, there was a concern that the genotype-guided and clinical-guided dosing algorithms may not produce sufficiently differentiable doses between the treatment groups, which may lead to an underestimation of the minimum detectable difference in PTTR between groups. We assumed that any difference between the two groups would arise from the subgroup of patients with either zero or multiple genetic variants.  For subjects in this allelic subgroup, if the difference between the two algorithm predictions is negligible or clinically irrelevant, then it is reasonable to expect no difference in PTTR. In this case the PTTR for the genotype-guided dosing group can be expressed as:d is the proportion of subjects with zero or multiple genetic variants in whom there is a clinically meaningful difference between the predicted dose determined by the genotype-guided and clinical-guided dosing algorithms. Hence the expected 15% difference would be diluted by a factor d and it would be more difficult to detect a clinically relevant difference between groups.where d = 0.91. With this dilution of the treatment effect, in order to detect an overall effect size of 5.49% in PTTR, the relative effect size in the  group would need to be 16.5%.To explore the impact of dilution of the treatment effect, we examined the distribution of the differences between the predicted doses among groups defined by allelic variation in the IWPC cohort  and calcd, the proportion of subjects with zero or multiple genetic variants in whom there is a difference between the predicted doses. There is sufficient power when d > 0.9. We are not highly confident in our estimate of how frequently the predicted dose will differ between the two algorithms and therefore have not taken this potential dilution effect into account in our calculations for sample size and power. However, given the potential impact of the dilution effect on the sample size requirements of the study seen in Table Table In this manuscript we provided practical guidance on the statistical design of a personalized medicine intervention that uses each subject's genotype information in an untargeted design. We used power and sample size calculations to illustrate the primary statistical challenge of designing this type of personalized therapy intervention: to accommodate the potential differential effectiveness of genotype-guided therapy across subpopulations defined by allelic variation. To determine a minimum detectable difference in PTTR between groups, we assumed that 40% of enrolled subjects would have a single genetic variant and would therefore not benefit from genotype-guided warfarin therapy. Hence, the minimum detectable difference used in sample size calculations is not a known quantity, but rather an unknown quantity that depends on the genetic makeup of the subjects enrolled. In addition, the sample size for the primary subgroup analysis depends on the proportion of subjects whose predicted initial dose employing the genetic and the clinical dose-initiation algorithms differs by ≥ 1.0 mg. Due to the importance of these parameters for adequate sample size and statistical power to detect a clinically meaningful difference, they will be monitored during the course of the trial.As shown in Table In our sensitivity analysis, we examined the dilution of the treatment effect due to a clinically irrelevant difference between the predicted doses  for subjects with zero or multiple genetic variants. However, we did not consider the impact of a clinically relevant difference between the predicted doses for subjects with a single variant. In this situation the PTTR for the genotype-guided dosing group can be expressed as:d' is the proportion of subjects with a single genetic variant in whom there is a meaningful difference between the predicted doses and d is defined in Equation (5). For example, in the IWPC cohort, approximately 26% of subjects with a single genetic variant would have received a 'different' initial dose, i.e., d' = 0.26. For these subjects, we expect that there would be a difference in PTTR, which would increase the power of the full cohort analysis. Because we were not highly confident in this estimate, we did not examine the increase in power associated with this allelic subgroup. Therefore, our sensitivity analysis is conservative.where CYP2C9 and VKORC1 variants. As we have shown with the statistical design of the COAG trial, by including potentially unresponsive subjects, a larger sample size may be required. Cost-benefit considerations regarding the cost of genetic screening for eligibility versus the cost of enrolling potentially unresponsive subjects may be useful to determine which design is more practical in specific applications.An individual's genetic information could be used prior to randomization to identify subjects who are potentially unresponsive to either drug therapy or the pharmacologic intervention, motivating researchers to decide whether to include or exclude those subjects from the trial . For exaCYP2C9 [For the COAG trial, we favored including all participants, regardless of their genetic variants. First, the assumption that those who possess a single genetic variant will not benefit from genotype-guided dosing, while suggested by the Couma-Gen trial , was notCYP2C9 . TherefoCYP2C9 and VKORC1 variants and whether genotype-guided dosing of warfarin therapy would provide an equal improvement in efficacy across populations defined by genetic variants. We assumed that subjects with a single genetic variant would not benefit from genotype-guided dosing, thus attenuating the postulated 15% relative difference between the two treatment groups to a 5.49% absolute difference. In our sample size calculations, if we ignored the fact that the genotype-guided dosing is not equally effective across subpopulations defined by genetic variants and assumed a minimum detectable difference of 15%, then the COAG trial would likely have chosen an inadequately small sample size to achieve adequate power. We also recommend that key assumptions regarding sample size and statistical power be monitored during the conduct of the trial, to inform any requisite increase in the sample size needed to detect a clinically relevant difference in the primary outcome between treatment groups. Further research is required to determine whether an interim sample size adjustment based on the observed proportion of allelic variants increases the type-I error rate.We recommend that the statistical design of a personalized medicine intervention that uses each subject's genotype information, within the framework of a randomized clinical trial, consider the distribution of relevant allelic variants in the study population and whether the intervention is equally effective across subpopulations defined by allelic variants. In the statistical design of the COAG trial, we considered the distribution of In summary, we found that sample size and power calculations may be sensitive to key assumptions required in the design of a personalized medicine intervention: the distribution of relevant allelic variants in the study population; and whether the pharmacogenetic intervention is equally effective across subpopulations defined by allelic variants. Given the novelty of pharmacogenetic research, we recommend that these assumptions be monitored during the conduct of a personalized medicine intervention.The authors declare that they have no competing interests.All authors made substantial contributions to conception and design. BF and JJ drafted the manuscript. All authors revised the manuscript critically for important intellectual content and approved the final manuscript.


Treatment of acute myocardial infarction with stem cell transplantation has achieved beneficial effects in many clinical trials. The bone marrow microenvironment of ST-elevation myocardial infarction (STEMI) patients has never been studied even though myocardial infarction is known to cause an imbalance in the acid-base status of these patients. The aim of this study was to assess if the blood gas levels in the bone marrow of STEMI patients affect the characteristics of the bone marrow cells (BMCs) and, furthermore, do they influence the change in cardiac function after autologous BMC transplantation. The arterial, venous and bone marrow blood gas concentrations were also compared.Blood gas analysis of the bone marrow aspirate and peripheral blood was performed for 27 STEMI patients receiving autologous stem cell therapy after percutaneous coronary intervention. Cells from the bone marrow aspirate were further cultured and the bone marrow mesenchymal stem cell (MSC) proliferation rate was determined by MTT assay and the MSC osteogenic differentiation capacity by alkaline phosphatase (ALP) activity assay. All the patients underwent a 2D-echocardiography at baseline and 4 months after STEMI.2, pCO2, base excess and HCO3 were similar in venous blood and bone marrow. Surprisingly, bone marrow showed significantly lower pH and Na+ and elevated K+ levels compared to arterial and venous blood. There was a positive correlation between the bone marrow pCO2 and HCO3 levels and MSC osteogenic differentiation capacity. In contrast, bone marrow pCO2 and HCO3 levels displayed a negative correlation with the proliferation rate of MSCs. Patients with the HCO3 level below the median value exhibited a more marked change in LVEF after BMC treatment than patients with HCO3 level above the median .As expected, the levels of pO2 and HCO3 levels may represent the optimal environment for BMCs in terms of their efficacy in autologous stem cell therapy in STEMI patients.Low bone marrow pCO Acute myocardial infarction (AMI) is the major cause of congestive heart failure and subsequent mortality in the developed countries. Despite the major advances in treatment methods, myocardial infarction usually causes irreversible damage to heart muscle. Cell therapy based on autologous stem cell transplantation and potential myocardial regeneration has been the focus of many clinical trials for more than a decade. These trials have yielded contradictory results which have been proposed to be attributable to the heterogeneity of the study designs . A recenhttp://www.clinicaltrials.gov with registration number NCT00363324.We examined a consecutive series of STEMI patients who were admitted to the University Hospital of Oulu, Finland, or to the central hospitals of Kajaani, Rovaniemi or Kemi, Finland, between April 2008 and August 2009. Inclusion criteria were age 18-79 years, acute STEMI and depressed left ventricular function (LVEF < 55%) measured by echocardiography 2-5 days after STEMI. Exclusion criteria were unwillingness or inability to provide informed consent, NYHA class IV, psychological or physiological unsuitability for participation in the study, inaccessibility for follow-up due to geographical or other reasons, severe leukopenia or thrombocytopenia, hepatic or renal dysfunction or evidence for malignant disease. Patient characteristics are shown in Table 7, n = 6) or to the group receiving the high cell dose . The patients and investigators performing the cell injection procedures and analysis of data were unaware of the randomization throughout the study. Bone marrow aspiration, collection and preparation of cells were performed in the morning preceding the intracoronary injection of BMCs, which was performed 4-14 days post-STEMI.The day of acute STEMI was defined as day 0. On days 1-5, patients were randomly assigned in a double-blinded fashion, either to the group receiving the small cell dose . 10 ml or 160 ml of bone marrow was aspirated and placed into tubes containing heparinized phosphate buffered saline . The bone marrow cells were then transported to a culture laboratory dedicated to aseptic cell manipulation for autologous stem cell transplantation. The mononuclear cell fraction containing stem and progenitor cells was isolated by Ficoll density gradient centrifugation, washed twice with PBS and resuspended with 50% autologous serum in heparinized saline or with heparinized saline alone . The cells were kept at +4°C until use. The BMC separation procedure took approximately 3 hours and the cells were administered within 24 hours to the target coronary artery. One aliquot of the cell sample was subjected to quality-control procedures, i.e. microbial culture for sterility and flow cytometer analysis for CD34+ cell counting and determination of cell viability in the accredited laboratory of the Oulu University Hospital, which is subjected to both outside and inside quality control. The validity of the cell preparation system was assessed as described previously [2 tissue culture flask and cultured in 5 ml medium containing alpha MEM (Gibco) buffered with 20 mM HEPES (Gibco) and containing 10% heat-inactivated fetal calf serum , 100 U/ml penicillin, 0.1 mg/ml streptomycin, 2 mM L-glutamine (Gibco). Cells were cultured at +37°C in 5% CO2 and 95% air. After one day, the medium was changed and non-attached cells were washed away. The attached cells  were cultured in a flask and the medium was replaced two times per week until near-confluence. The cells were passed two times before the analyses. In the cell counting analyses, the cells were washed with PBS and adherent cells detached using trypsin-EDTA solution (Gibco).BMCs were collected in the morning of the cell administration day under local anesthesia and sterile conditions. 0.5-1 ml sample was aspirated from one-two iliac crest into a RAPIDLyteFlow cytometric analysis of cell surface antigens was performed as previously described . The min2 and 95% air. After incubation, the MTT solution was removed and 100 μl/well of dimethyl sulfoxide  was added. The absorbance of the reduced form of MTT was measured at 550 nm and 650 nm (background) in a plate reader .MSCs were cultured in 96-well plates in six replicates. 500 cells/well were plated and half of the medium was changed twice a week. Cell proliferation was measured by MTT -2,5-diphenyltetrazolium bromide) assay after 1, 4, 7, and 14 days culture. The medium was removed and MTT reagent   was added to the cells and incubated for 2 h at +37°C in 5% COHuman MSCs were seeded at 10000 cells/well into 24-well plates in four replicate wells and were cultured with the medium described above and another four replicates in a medium containing also 100 mM dexamethasone , 10 mM β-glycerol (Sigma) and 0.05 mM ascorbic acid (Sigma). After 3 weeks, cultured cells were assayed in the following way: the assay buffer containing 0.1% Triton X-100, pH 7.6, was added to each well, and the plates were frozen. After thawing, alkaline phosphatase (ALP) activity was determined using 0.1 mM 4-p-nitrophenylphosphate (Sigma-Aldrich) as the substrate and absorbance was read at 405 nm in a plate reader . Each sample was measured in duplicate. The protein content of the wells was determined by the BIO-RAD Protein Assay . The enzyme activities were expressed as units/mg protein.2, pCO2, HCO3, BE, Na+ and K+ levels were measured in the central laboratory of Oulu University Hospital.All the patients underwent venous and arterial blood sampling on the day of cell transplantation. pH, pOA 2-dimensional echocardiogram was performed 2-5 days after STEMI and at 4 months after STEMI. The LVEF was measured by an experienced investigator in the core laboratory, unaware of the patient's treatment assignment, using the technique described previously .t-test was used to determine the statistical significance of the difference in laboratory values between bone marrow and arterial/venous blood. Correlation analyses between bone marrow and arterial/venous blood laboratory values were conducted using paired samples correlation. In other analyses, Pearson's correlation or Spearman's correlation with skewed data was used. Analysis-of-variance (ANOVA) was used in the between-group comparisons. The variance analyses of the LVEF change in HCO3, BE and potassium subgroups (above/below median) were corrected with the cell dose group (low/high dose). All P values are two-tailed and statistical significance was set at P < 0.05. Analyses were performed with SPSS software, version 14.0.Variables are expressed as means ± SD, or medians with interquartile range with skewed data. Paired samples 2, pCO2), serum bicarbonate (HCO3), base excess (BE), pH and electrolytes sodium (Na+) and potassium (K+) were measured from venous, arterial and bone marrow blood samples  proliferation rate was determined by MTT assay (absorbance/time in days) Figure . The abs3 level as well as the BE level and a positive correlation between LVEF change and potassium level, but these associations did not reach statistical significance  . In addition, the patients using diuretics experienced a smaller improvement in the LVEF than patients without diuretic medication . No significant associations were found between any other patient characteristics and the bone marrow blood gases .In an attempt to clarify the individual differences in the levels of bone marrow blood gases, the associations of several other parameters  with the bone marrow blood gas values were tested. The bone marrow BE and HCO2, pCO2, BE and HCO3 were similar in venous blood and bone marrow. In a further blood gas analysis, the proliferation rate of bone marrow-derived MSCs was associated with the pCO2 and HCO3 levels in the bone marrow. The MSC proliferation was more efficient when bone marrow pCO2 and HCO3 levels were low. It was also shown that a low HCO3 level in bone marrow was associated with better functional recovery of the heart after myocardial infarction treated with intracoronary injection of autologous BM cells. Patients with an HCO3 level below the median value experienced a more marked improvement of LVEF than patients with the HCO3 level above the median value. In contrast, the osteogenic differentiation potential of the BM-derived MSCs was shown to be better when bone marrow pCO2 and HCO3 levels were high.The results of the present study demonstrate that there is a significant difference in blood gas and electrolyte levels between bone marrow and arterial blood in patients with STEMI. In addition, a significant difference was found between bone marrow aspirate and venous blood in pH, sodium and potassium levels. In contrast, the levels of pO2, HCO3, BE and sodium concentration, but no predictable relationship was observed for pO2 and potassium concentration. In the present study, pCO2, BE and HCO3 also correlated between bone marrow and venous blood as well as bone marrow and arterial blood. However, pH, sodium and potassium levels differed significantly between venous blood and bone marrow. In the study of Grisham et al. the actual differences between bone marrow and venous blood were not assessed. However, they also noticed that the potassium levels differed significantly between bone marrow and venous blood. They considered that this could be due to the traumatic nature of specimen collection and the high negative pressure generated in aspirating marrow evoking hemolysis [One objective of this study was to compare arterial, venous and bone marrow blood gas and electrolyte concentrations in STEMI patients. As far as we are aware, only one previous study has assessed the levels of blood gases and electrolytes in bone marrow and venous blood in humans . Grishamemolysis . A porti2 in the body is converted into carbonic acid (H2CO3), which rapidly forms bicarbonate (HCO3-). The pH in the body depends on the pCO2 and the level of HCO3-. In this study MSC proliferation was found to be more efficient when the bone marrow aspirate pCO2 and HCO3 levels were low. The pH in the bone marrow was not directly associated to the proliferation rate but this may be due to other compensatory buffering mechanisms, such as hemoglobin, which is known to stabilize the pH value. It is also known that active inflammation creates an acidic environment [2 and HCO3 displayed a nonsignificant negative correlation with bone marrow interleukin (IL)-6 levels (data not shown). Thus, the levels of pCO2 and HCO3 may have declined in an attempt to buffer the acidosis caused by inflammatory processes. We have previously shown that inflammation  enhances the proliferation of MSCs and activates many immunosuppressive pathways in vitro [2 and HCO3, it can be postulated to have an influence on the resident MSCs and enhance also their in vitro proliferation. On the contrary, the osteogenic differentiation potential of the BM cells was found to be better when bone marrow pCO2 and HCO3 levels were high. This result is in line with previous studies which have shown that acidic environment impairs osteoblast function,[Bicarbonate is alkaline, and it is one of the components that maintain acid-base homeostasis in the body. Most of the COironment . In the in vitro . Thus, ifunction,,12 reducfunction,,14 and ifunction,.2 and HCO3 levels. In addition, the observation that low bone marrow pCO2 and HCO3 is beneficial for BM-derived MSC proliferation but not for the osteoblast differentiation suggests that maintaining the stemness of MSCs or some other cell lineage is predominant in this kind of environment. It has been previously shown that hMSCs cultured in hypoxic conditions (2% oxygen) display significantly improved expansion characteristics while maintaining their multi-lineage potential [2 value and reduced HCO3 levels which support the stemness of BM cells making them more efficient for cell therapy use and also more capable of undergoing intrinsic repair mechanisms. However, the results of this study are still preliminary findings and the importance of bone marrow microenvironment on MSC functionality after two MSC passages in culture media with 5% CO2 and 95% air, which are completely different conditions compared to the bone marrow of STEMI patients, remain to be elucidated. Further tests in cell cultures are currently ongoing and will later provide us more insights about the effect of different environmental factors of the bone marrow on the functionality of the BM cells.Several previous trials have shown that the number of bone marrow CD34+ cells does not correlate with the change of LVEF after cell transplantation -19. In totential . On the otential . Obviousotential ,23. BaseAs far as we are aware, this is the first study to assess the effects of the metabolic state of bone marrow on the functional capacity of the bone marrow-derived stem cells in STEMI patients. It has been previously shown that age, risk factors for coronary artery disease, diabetes and heart failure diminish the capacity of the blood-derived circulating cells to contribute to the functional repair compared to healthy controls -26. In a3 level below the median value experienced a more marked improvement of LVEF after stem cell treatment than their counterparts with an HCO3 level above the median value. This clinical trial was designed to be a dose-response study but no difference in the LVEF recovery could be observed between the groups receiving low or high dose of BMCs in this pilot phase. Thus, it is not likely that the differences in the cell doses would have affected the EF results of the present substudy. However, because of the low patient number, the results of this study have to be interpreted as descriptive rather than confirmatory when considering the importance of the blood gas values of the bone marrow on the clinical outcome of BMC treated STEMI patients. More studies with higher patient number are needed to confirm these preliminary findings.The LVEF change between baseline and follow-up visit has been the most widely used endpoint to evaluate the effectiveness of stem cell transplantation after myocardial infarction . In the 3 levels were found to be significantly higher in patients using diuretic drugs at the time of hospital discharge than in patients with no diuretic medication. The patients using diuretics also experienced less extensive improvement in the LVEF than the patients with no diuretic medication. The use of diuretic drugs is usually associated with post-STEMI hypertension or to venous or pulmonary congestion, and it is these conditions that most likely are affecting the blood gas values rather than the medication itself. Based on the results of the present study, we have created a hypothetical model describing the associations between heart, pulmonary vasculature, peripheral circulation and bone marrow after an ischemic event (Figure 2 and HCO3 concentrations in the peripheral circulation as well as in the bone marrow. The acidic bone marrow microenvironment also impairs the function of the resident stromal cells which leads to impaired cardiac regeneration after myocardial infarction. The course of events creates a vicious cycle causing even more impairment of cardiac function. This idea is supported by a previous experimental work which showed that donor myocardial infarction impaired the therapeutic potential of bone marrow cells in mice [The bone marrow BE and HCOt Figure . In this2 and HCO3 levels are low. In contrast, the osteogenic differentiation potential of the BM-derived MSCs is better when bone marrow pCO2 and HCO3 levels are high. The STEMI patients with lower HCO3 levels experience a more marked change in LVEF. Moreover, patients using diuretic drugs at the time of hospital discharge tended to have higher bone marrow pCO2 and HCO3 levels and these patients experienced a poorer improvement in the LVEF after stem cell therapy, most probably because of the underlying cardiac or venous congestion affecting also the bone marrow metabolic state. In this respect, low bone marrow pCO2 and HCO3 levels represent the optimal environment for BM cells in terms of their efficacy in autologous stem cell therapy in STEMI patients.There is a significant difference in blood gas and electrolyte levels between bone marrow and arterial blood in patients with STEMI. There are also significant differences in the pH, sodium and potassium levels between bone marrow aspirate and venous blood. Even though bone marrow acid-base status and blood gas levels resemble more closely venous blood than its arterial counterpart, the bone marrow seems to form a unique compartment for maintenance and storage of stromal cells, similarly to that reported in the context of the hematopoietic stem cell niche. The proliferation rate of bone marrow-derived MSCs is more efficient when bone marrow pCOThe authors declare that they have no competing interests.JM participated in the design of the study, made laboratory analyses and cell culturing, acquired the data, analysed and interpreted the data, performed statistical analyses and drafted the manuscript.RS made laboratory analyses and cell culturing, acquired the data, analysed and interpreted the data and helped to draft the manuscript. KY conceived and designed the study, chose and examined the patients, performed 2D-echocardiography and other medical interventions for the study patients, injected the cells, acquired the data and analysed and interpreted the data. MN and KK chose and examined the patients, performed 2D-echocardiography and other medical interventions for the study patients, injected the cells, acquired the data and analysed and interpreted the data. MS and PK conceived and designed the study, performed cell aspirations from the bone marrow, performed laboratory analyses, examined the patients, acquired the data, analysed and interpreted the data and helped to draft the manuscript. E-R.S conceived and designed the study, acquired the data, handled funding and supervision and helped to draft the manuscript. TM conceived and designed the study, chose and examined the patients, performed medical interventions for the study patients, acquired the data and handled supervision. HH conceived and designed the study, chose and examined the patients, performed medical interventions for the study patients, acquired the data, analyzed and interpreted the data, handled funding and supervision and helped to draft the manuscript. PL conceived and designed the study, acquired the data, analyzed and interpreted the data, handled funding and supervision and helped to draft the manuscript. All authors read and approved the final manuscript.


Pathway-based analysis has been recently used in joint tests of association between disease and a group of common genetic variants. Here we explore this idea for the joint effects analysis of rare genetic variants and their association with quantitative traits and disease. We accumulate multiple rare minor alleles in a genetic risk score for each individual in a given pathway; this score is then used to assess association with quantitative phenotypes and disease. We demonstrate that this approach may be better than studying single rare variants or a gene risk score for identifying individuals with significantly greater risk. In the past few years, genome-wide association studies have been widely used to identify genetic risk factors for complex diseases. This analysis paradigm has made significant progress in many genetic studies. For example, so far, many variants have been discovered that are associated with common diseases, such as type 2 diabetes . To dateWe use replicate 1 with 697 unrelated individuals from the Genetic Analysis Workshop 17 (GAW17) data. There are 24,487 autosomal SNPs. All SNPs and samples have a genotype call rate of 100%. We define rare SNPs as those with a minor allele frequency (MAF) less than 1%. For the phenotype data, there are three quantitative risk factors , which are simulated as normally distributed phenotypes, and one disease risk factor, for which there are 209 case subjects and 488 control subjects. Genes influencing Q1 and the disease are primarily from the vascular endothelial growth factor (VEGF) pathway, whereas those influencing Q2 are primarily associated with cardiovascular disease risk and inflammation. There are no causal genes or pathways related to Q4 .P-value for each rare SNP.For each rare SNP, we performed linear regression analysis for association between genotypes and Q1, Q2, and Q4 and logistic regression analysis for association between genotypes and disease status. We adjusted for sex, age, smoking, and population stratification by generating two dummy covariates for the three populations: Asian, African, and European. Genotypes were coded additively. We obtained the corresponding adjusted odds ratio (OR) and We analyzed the joint effects of rare genetic variants at both the gene and the pathway level. The objective was to test for association of an aggregation of rare minor alleles with quantitative phenotypes and disease by combing genetic information across multiple variants within a given gene or pathway, respectively. To do this, we first mapped rare SNPs to genes and pathways as follows.http://www.broadinstitute.org/gsea/msigdb/). This database includes 888 gene sets collected from 186 pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/), 430 pathways from Reactome, 217 pathways from BioCarta among others. We kept only the pathways with at least five genes in our data set, which left 472 pathways for follow-up analysis. We note that each of these three databases includes the VEGF causal pathway. In the next step, we defined a genetic risk score for each individual as the count of minor alleles of all rare variants in a given gene or pathway [In step 1, we mapped rare SNPs to genes. We obtained the nearest gene name for each rare SNP from the snp_info file provided by GAW17. In step 2, we mapped the genes from step 1 to the c2 curated canonical pathways (version 3) from the Broad Institute . We did find three rare variants that were significantly associated with Q1  from the 23,173 SNPs that passed quality control. Overall, we did not find any rare variants with significant association for Q2, Q4, and disease at the Bonferroni-corrected significance level of 0.05 . We found four genes that were significantly associated with Q1 (Table FLT1 and KDR) were causal genes and two of which (RGPD8 and EPHB1) were not causal genes.The 18,094 rare variants mapped to 2,439 genes. Of these 2,439 genes, 911 had 1 rare SNP in each gene, 655 had 2–5 rare SNPs, and 873 had more than 5 rare SNPs. The joint effects analysis of rare genetic variants at the gene level did not identify significant association for Q2, Q4, or disease at the Bonferroni-corrected significance level of 0.05 . The most significant pathway for Q2 was Reactome Phase II Conjugation (P = 6.5 × 10−4), which did not include causal genes.The 2,439 genes that include at least one rare variant were mapped to 472 canonical pathways. We did not find significant association of pathways with either Q2 or Q4 at a Bonferroni-corrected significance level of 0.05  includes the VEGF causal pathway for Q1 and disease. We report the association results of this causal pathway in Table Because the VEGF pathway is significantly associated with disease, as shown in Table To evaluate whether the significance of the causal pathways in different databases is driven by only one rare SNP out of each gene in a given pathway, we did a leave-one-out cross-validation (LOOCV). To do this test, for each pathway we first removed one rare SNP from each gene and then recalculated the genetic risk score for each individual as the accumulation of minor alleles for all rare variants in a given pathway by excluding the rare SNP. Finally, we performed the regression analysis described earlier. We repeated these steps for all the rare SNPs in a given pathway. Table P-value column in Table Because our analysis has narrowed the associations to the VEGF pathway in the three databases, we further evaluated the associations between SNPs in the pathway with Q1 and disease using a smaller Bonferroni correction based on the number of SNPs in the pathway in each of the three databases  and liability (disease). However, this assumption may not hold in real data, resulting in decreased power to detect causal genes. We have also observed that, although most of the causal genes for Q1 are not the causal genes for disease, those causal genes for Q1 actually play a key role in disease (such as the five genes of the VEGF pathway in the Reactome database). Our results show that a simple but efficient pathway-based analysis of rare genetic variants can identify potential genetic risk factors that were missed in the SNP- and gene-level analysis.Our study does have some limitations. One is that our analysis was focused on only one simulated data set. A better strategy would be to analyze all simulated data sets. Another limitation is that we assumed that there was a linear relationship between the traits  and the covariates used in the study. We observed that some of the covariates had a nonlinear relationship with the traits. Therefore nonlinear regression models may be more suitable. We will explore these models in detail in the future.The authors declare that there are no competing interests.PH designed the study, performed the data analysis and drafted the manuscript. WX and ADP participated in designing the study. ADP supervised the study. All authors participated in the statistical analysis and helped to draft the manuscript. All authors read and approved the final manuscript.