Using patient data for patients’ benefit

Banerjee, Amitava; Mathew, David; Rouane, Katherine

doi:10.1136/bmj.j4413

Cited by 4 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The possibility of collecting and analyzing large amounts of data has allowed the development of predictive models for new diagnostic techniques. 4,26 These are already being applied in several fields, such as imaging, 27,28 nuclear medicine, 29 and pathology. 30 Digital tools can also improve monitoring, predict outcome and course, and assist in the treatment of disease.…”

Section: Discussionmentioning

confidence: 99%

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

et al. 2021

View full text Add to dashboard Cite

We present a noninvasive Web-based app to help exclude or diagnose myelodysplastic syndrome (MDS), a bone marrow (BM) disorder with cytopenias and leukemic risk, diagnosed by BM examination. A sample of 502 MDS patients from the European MDS (EUMDS) registry (n > 2600) was combined with 502 controls (all BM proven). Gradient-boosted models (GBMs) were used to predict/exclude MDS using demographic, clinical, and laboratory variables. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to evaluate the models, and performance was validated using 100 times fivefold cross-validation. Model stability was assessed by repeating its fit using different randomly chosen groups of 502 EUMDS cases. AUC was 0.96 (95% confidence interval, 0.95-0.97). MDS is predicted/excluded accurately in 86% of patients with unexplained anemia. A GBM score (range, 0-1) of less than 0.68 (GBM < 0.68) resulted in a negative predictive value of 0.94, that is, MDS was excluded. GBM ≥ 0.82 provided a positive predictive value of 0.88, that is, MDS. The diagnosis of the remaining patients (0.68 ≤ GBM < 0.82) is indeterminate. The discriminating variables: age, sex, hemoglobin, white blood cells, platelets, mean corpuscular volume, neutrophils, monocytes, glucose, and creatinine. A Web-based app was developed; physicians could use it to exclude or predict MDS noninvasively in most patients without a BM examination. Future work will add peripheral blood cytogenetics/genetics, EUMDS-based prospective validation, and prognostication.

show abstract

Section: Discussionmentioning

confidence: 99%

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In the case of PRO data, the above biases could undermine the quality of data in medical registries and, more specifically [23] , the role of PRO measures in clinical research [21] , in exercises of health technology assessment [11] (e.g., those which address the question whether maintaining an expensive PRO collection program is positively associated with quality of care), and in the development of data-driven decision support systems [5] (like those based on machine learning techniques). For this reason, it is important to assess the extent these biases impact PRO biomedical data.…”

Section: Background and Objectivesmentioning

confidence: 99%

PROs in the wild: Assessing the validity of patient reported outcomes in an electronic registry

Cabitza

Dui

Banfi

2019

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

Background and objectives: Collecting Patient-Reported Outcomes (PROs) is an important way to get firsthand information by patients on the outcome of treatments and surgical procedure they have undergone, and hence about the quality of the care provided. However, the quality of PRO data cannot be given for granted and cannot be traced back to the dimensions of timeliness and completeness only. While the reliability of these data can be guaranteed by adopting standard and validated questionnaires that are used across different health care facilities all over the world, these facilities must take responsibility to assess, monitor and ensure the validity of PROs that are collected from their patients. Validity is affected by biases that are hidden in the data collected. This contribution is then aimed at measuring bias in PRO data, for the impact that these data can have on clinical research and post-marketing surveillance. Methods: We considered the main biases that can affect PRO validity: Response bias, in terms of Acquiescence bias and Fatigue bias; and Non-Response bias. To assess Acquiescence bias, phone interviews and online surveys were compared, adjusted by age. To assess Fatigue bias, we proposed a specific item about session length and compared PROs scores stratifying according to the responses to this item. We also calculated the intra-patient agreement by conceiving an intra-interview test-retest. To assess Non-Response bias, we considered patients who participated after the saturation of the response-rate curve as proxy of potential non respondents and compared the outcomes in these two strata. All methods encompassed common statistical techniques and are cost-effective at any facility collecting PRO data. Results: Acquiescence bias resulted in significantly different scores between patients reached by either phone or email. In regard to Fatigue bias, stratification by perceived fatigue resulted in contrasting results. A relevant difference was found in intra-patient agreement and an increasing difference in average scores as a function of interview length (or completion time). In regard to Non-Response bias, we found nonsignificant differences both in scores and variance. Conclusions: In this paper, we present a set of cost-effective techniques to assess the validity of retrospective PROs data and share some lessons learnt from their application at a large teaching hospital specialized in musculoskeletal disorders that collects PRO data in the follow-up phase of surgery performed therein. The main finding suggests that response bias can affect the PRO validity. Further research on the effectiveness of sim ple and cost-effective solutions is necessary to mitigate these biases and improve the validity of PRO data.

show abstract

Digital Futures Past — The Long Arc of Big Data in Medicine

Greene

Lea

2019

N Engl J Med

View full text Add to dashboard Cite

Using patient data for patients’ benefit

Cited by 4 publications

References 5 publications

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

A predictive algorithm using clinical and laboratory parameters may assist in ruling out and in diagnosing MDS

PROs in the wild: Assessing the validity of patient reported outcomes in an electronic registry

Digital Futures Past — The Long Arc of Big Data in Medicine

Contact Info

Product

Resources

About