Bibliographic record
Abstract
Periodic colorectal cancer (CRC) screening in the general population receives one of the highest recommendations from the US Preventative Health Care Services Task Force and many other practice guideline organizations based on consistent strong evidence from clinical trials. However, the evidence is for subjects at average risk with average life expectancies. In this journal, Wong et al. examine the net benefit and cost-effectiveness of CRC screening in dialysis patients not on the wait list, dialysis patients wait-listed for a kidney transplant and kidney transplant recipients. The benefits of screening were estimated to be 2.6 added days of life for each dialysis patient, 6.9 days for wait-listed patients and 12 days for those transplanted. The benefits accruing to kidney transplant patients were much less than their earlier study showing a benefit of 24 days, probably because the model assumed the transplanted graft never failed. Not surprisingly the cost-effective ratios are high in the former two groups but reasonable in the transplanted population. Although there is a lack of direct evidence, these and other modeling studies suggest the benefits of CRC screening in patients with functioning kidney transplants may well be equivalent to the benefit in the general population. Given the potential harm of screening and the short life expectancies of dialysis patients, the benefits of CRC screening will be small and uncertain. Not screening many of our older dialysis patients would be consistent with guideline recommendations that CRC screening only be undertaken in patients with life expectancies of >10 years. Cancer prevention is high on the healthcare wish lists of both provider and patient [1]. However, there is nothing more controversial than screening to prevent cancer. Whether there has been a recent catharsis or simply a swing in the pendulum, many are not happy with the trend that sees guidelines pulling back with reductions in the frequency of screening, recommendations that screening be delayed to older ages or ceased at a certain age and recognition that some patients are currently inappropriately screened [2]. It is ironic that some cancers such as prostate cancer are frequently screened for in men yet the evidence of benefit is weak, whereas participation rates are relatively low for colorectal cancer (CRC) screening where the evidence of benefit is strongest [3]. A great deal of thought and effort is expended in finding the right balance. Nonetheless, cancer interest groups will always argue that any evidence of benefit is reason enough, whereas task forces believe that there must be strong unbiased evidence (randomized trials of screening) that show significant benefits in hard endpoints (lives saved) and that these benefits must significantly outweigh any potential harms of screening. Like all population screening strategies, the benefits are for the few while requiring participation of many. It is also the many that are potentially subjected to the harms. There are the risks of false-positive screens that result in unnecessary psychological distress, intrusion of time and invasive procedures, finding indolent lesions that may never pose a risk to an individual patient (length bias), later risks associated with further invasive testing and treatment or simply finding lesions earlier with no impact on overall outcomes (lead bias). The erosion of length and quality of life from these harms is not always appreciated nor captured. These have only recently been enumerated in detail for breast and prostate cancer screening and the analyses have been sobering. A recent study on prostate cancer shows the net benefits may be eroded completely by diagnosis and treatment complications and their effects on quality of life [4]. Most importantly, the recommendations to screen are for average risk patients with average life expectancies or for screening in higher risk populations. However, there has been an undercurrent that all patients should have the right to screening, not to do so is an error of omission, and quality of care can be measured by examining screening rates [5]. In fact, the USRDS at one time tracked prostate and breast cancer screening in US dialysis patients as a measure of quality care [6]. The issues become complex in subpopulations with reduced life expectancies and even more so if these populations are at increased cancer risk. The chronic kidney disease (CKD) population is very much an example where the recommendations might blur. Most agencies do not recommend CRC screening in patients with life expectancies <5 years, since the benefits of detecting early lesions that become problematic would take 5 years to see a benefit and these patients are likely to die of something else in the interim from competing risks [7, 8]. In fact, the American College of Physicians suggests limiting screening to those with life expectancies of >10 years [8]. Patients with CKD, and especially those on dialysis, have markedly reduced life expectancies. In the USA, the average life expectancy for all dialysis patients aged 50–54 is 6.4 years and for patients aged 60+ is <5 years [9]. In effect, screening may well be inappropriate for most ESRD patients. However, there is evidence that colon cancer is increased in CKD populations, particularly in transplanted patients with greater life expectancies [10]. Although years of remaining life are greater in transplanted patients compared with dialysis patients, life expectancies remain about one-third less in transplanted patients compared with the general population [9]. Colon cancer is reported to be ∼1.5- to 2.5-fold higher in the kidney transplant population [10–12]. Here, the balance of increased risk in a population with a shorter life expectancy makes decision-making interesting. In the article by Wong et al. [13] in this journal, the benefits and cost-effectiveness of CRC screening were explored in Australian/New Zealand patients on dialysis, on the wait-list and with a transplant. Not surprisingly, the benefits were low and cost-effective ratios high in the former two groups but reasonable in the transplanted population. In regard to my review, there are some important points in this analysis that deserve comment. First, this is a very detailed and comprehensive analysis consistent with many similar efforts from this group that readers should be aware of including screening for colorectal, kidney, breast and cervical cancer [14–17]. Second, the probabilities are derived from the Australian/New Zealand population and may not exactly translate to other regions. For example, dialysis mortality rates in the USA are generally higher than in other countries, such that the net benefit of CRC screening will be less [18]. In an economic decision analysis model of US patients, periodic CRC screening was predicted to increase life by <1 day in US dialysis patients, 7 days in transplant recipients compared with nearly 20 days in the general population [19]. In this US analysis, the rates of CRC were assumed to be the same in all populations. If a 2.8-fold higher rate of cancer was modeled, then the net benefits of screening were about the same (20 days) in the kidney transplant patient compared with the general population. In an earlier modeling CRC screening study published by Wong et al. [14] in 2009 of AUS/NZ kidney transplant recipients who experienced increased CRC rates, enrollees were predicted to gain 24 days per person screened. Both of these modeling studies in kidney transplant recipients compare favorably with CRC screening studies in the general population that estimate the magnitude of the benefit to be ∼24 days [20]. In comparison, Wong et al. [13] reports in this journal the benefits of screening to be 2.6 days for dialysis patients not on the transplant list, 6.9 days for those on the list and 12 days for those transplanted. The benefits accruing to transplanted patients were much less than their earlier study showing a benefit of 24 days [14]. So why are the two studies so different? Some of the differences are hidden in the analysis. In the earlier quoted study, kidney transplant patients were assumed to always have a functioning transplant [14, 18]. In this recent analysis, patients with failed grafts continue to receive screening with much reduced benefits. Many of the patients on the wait-list will receive a transplant and enjoy a greater life expectancy but at a higher CRC risk. The implication is that the benefit accrues mostly to those with a transplant (better life expectancy and higher CRC risk), not those on dialysis. Thirdly, they examine average life expectancies in their end-stage renal disease (ESRD) population. I expect that their dialysis population not wait-listed was healthier than my own. In a recent study of incident patients deemed to have contraindications to transplantation from our center, those
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.052 | 0.047 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".