Publications by authors named "Sajan Khosla"

13 Publications

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The Alignment of Real-World Evidence and Digital Health: Realising the Opportunity.

Ther Innov Regul Sci 2021 Apr 29. Epub 2021 Apr 29.

XNK Therapeutics, Stockholm, Sweden.

In the new era of healthcare digitalization, there is a golden opportunity in the overlap between digital health and Real-World Evidence (RWE). In this commentary, we define RWE and digital health and investigate their intersection. We describe the stages in the RWE value chain critical to the evidence generation process, how these stages change with new digital technologies and the opportunities and challenges that arise from how these stages evolve-including their application for stakeholders such as patients, physicians and regulators. We also discuss the current published guidelines and frameworks regarding digital health. We categorise these publications in terms of their clarity as "Extensive", "Intermediate" or "Basic" and according to whether they encompass all levels of digital health or are more focussed in their guidance. Finally, we provide recommendations to increase synergy between RWE and digital health.
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http://dx.doi.org/10.1007/s43441-021-00288-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082742PMC
April 2021

The Alignment of Real-World Evidence and Digital Health: Realising the Opportunity.

Ther Innov Regul Sci 2021 Apr 29. Epub 2021 Apr 29.

XNK Therapeutics, Stockholm, Sweden.

In the new era of healthcare digitalization, there is a golden opportunity in the overlap between digital health and Real-World Evidence (RWE). In this commentary, we define RWE and digital health and investigate their intersection. We describe the stages in the RWE value chain critical to the evidence generation process, how these stages change with new digital technologies and the opportunities and challenges that arise from how these stages evolve-including their application for stakeholders such as patients, physicians and regulators. We also discuss the current published guidelines and frameworks regarding digital health. We categorise these publications in terms of their clarity as "Extensive", "Intermediate" or "Basic" and according to whether they encompass all levels of digital health or are more focussed in their guidance. Finally, we provide recommendations to increase synergy between RWE and digital health.
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http://dx.doi.org/10.1007/s43441-021-00288-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082742PMC
April 2021

Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study.

Lancet Rheumatol 2020 Nov 21;2(11):e698-e711. Epub 2020 Aug 21.

Janssen Research and Development, Titusville, NJ, USA.

Background: Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis.

Methods: In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the value was less than 0·4.

Findings: The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Self-controlled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12-2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22-3·95]), chest pain or angina (1·15 [1·05-1·26]), and heart failure (1·22 [1·02-1·45]).

Interpretation: Hydroxychloroquine treatment appears to have no increased risk in the short term among patients with rheumatoid arthritis, but in the long term it appears to be associated with excess cardiovascular mortality. The addition of azithromycin increases the risk of heart failure and cardiovascular mortality even in the short term. We call for careful consideration of the benefit-risk trade-off when counselling those on hydroxychloroquine treatment.

Funding: National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Senior Research Fellowship programme, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research and Development, IQVIA, Korea Health Industry Development Institute through the Ministry of Health and Welfare Republic of Korea, Versus Arthritis, UK Medical Research Council Doctoral Training Partnership, Foundation Alfonso Martin Escudero, Innovation Fund Denmark, Novo Nordisk Foundation, Singapore Ministry of Health's National Medical Research Council Open Fund Large Collaborative Grant, VINCI, Innovative Medicines Initiative 2 Joint Undertaking, EU's Horizon 2020 research and innovation programme, and European Federation of Pharmaceutical Industries and Associations.
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http://dx.doi.org/10.1016/S2665-9913(20)30276-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442425PMC
November 2020

Bridging the Gap Between RCTs and RWE Through Endpoint Selection.

Ther Innov Regul Sci 2021 01 6;55(1):90-96. Epub 2020 Jul 6.

Real-World Evidence Center of Excellence, AstraZeneca, Cambridge, UK.

This commentary is authored by several industry real-world evidence (RWE) experts, with support from IQVIA, as part of the 'RWE Leadership Forum': a group of Industry Leaders who have come together as non-competitive partners to understand and respond to RWD/E challenges and opportunities with a single expert voice. Here, the forum discusses the value in bridging the industry disconnect between RTCs and RWE, with a view to promoting the use of RWE in the RCT environment. RCT endpoints are explored along several axes including their clinical relevance and their measure of direct patient benefit, and then compared with their real-world counterparts to identify suitable paths, or gaps, for assimilating RWE endpoints into the RCT environment.
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http://dx.doi.org/10.1007/s43441-020-00193-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785541PMC
January 2021

Strengthening pharma's contract with society: the value of trusted partnerships between pharma and healthcare facilitated by real-world data.

J Comp Eff Res 2020 02 22;9(3):155-159. Epub 2020 Jan 22.

Lilly, Indianapolis, IN, USA.

This White Paper is authored by 11 industry real-world evidence (RWE) experts, with support from IQVIA, as part of the 'RWE Leadership Forum': a group of industry leaders who come together as noncompetitive partners to understand and respond to internal or external RWD/E challenges and opportunities with a single expert voice. Herein we aim to clarify the rules of engagement between pharma and healthcare in order to establish trust-based partnerships, which will unlock unique value for society, including the medical community and the ultimate beneficiary, the patient.
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http://dx.doi.org/10.2217/cer-2019-0183DOI Listing
February 2020

Durability of glycaemic control in patients with type 2 diabetes after metformin failure: Prognostic model derivation and validation using the DISCOVER study.

Diabetes Obes Metab 2020 05 21;22(5):828-837. Epub 2020 Feb 21.

Leicester Real World Evidence Unit, Leicester Diabetes Centre, University of Leicester, Leicester, UK.

Aim: To develop and internally validate prognostic models on the long-term durability of glycaemic control in patients with type 2 diabetes after metformin failure.

Materials And Methods: DISCOVER is a 3-year, prospective observational study across six continents investigating second-line glucose-lowering therapies. In this analysis from 35 countries, we included patients on metformin initiating second-line glucose-lowering medication(s) because of physician-defined lack of efficacy. The outcome was durability of glycaemic control, defined as three consecutive levels of HbA1c at 6-, 12- and 24-month follow-up at target (HbA1c equal to or lower than the level when the physician initiated the second-line therapy in patients with baseline HbA1c ≤7% [53 mmol/mol]; and equal to or lower than 7% in those with baseline HbA1c >7%). We developed and internally validated two prognostic models: a base model, which included age, sex, ethnicity, country income group, baseline HbA1c and second-line therapy, and an advanced model, established through statistical variable selections from a model including base variables and 13 additional predictors selected from a literature review. We used logistic regression to develop and 500 bootstrapping samples to internally validate the models; discrimination and calibration were used to assess model performance.

Results: Overall, 896 out of 2995 participants (29.9%) had sustained glycaemic control. The base model performed well: Nagelkerke R was 0.13, C-index 0.70 (95% CI: 0.68, 0.71) and bias-corrected C-index 0.69 after internal validation. Diabetes duration, insurance type, estimated glomerular filtration rate and glucose self-monitoring were additionally selected in the advanced model, which had only a slightly better performance compared with the base model: Nagelkerke R 0.20, C-index 0.71 (95% CI: 0.69, 0.73) and bias-corrected C-index 0.70. Calibration plots showed good calibrations of both validated models.

Conclusion: These prognostic models, which include simple demographic and routinely collected clinical information, enabled the estimation of the probability of 2-year sustained glycaemic control in patients after metformin failure. The models have been implemented into a web-based tool to support healthcare professionals in their decisions.
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http://dx.doi.org/10.1111/dom.13966DOI Listing
May 2020

Real world evidence (RWE) - a disruptive innovation or the quiet evolution of medical evidence generation?

F1000Res 2018 25;7:111. Epub 2018 Jan 25.

AstraZeneca, Gaithersburg, MD, 20878, USA.

Stakeholders in healthcare are increasingly turning to real world evidence (RWE) to inform their decisions, alongside evidence from randomized controlled trials. RWE is generated by analysing data gathered from routine clinical practice, and can be used across the product lifecycle, providing insights into areas including disease epidemiology, treatment effectiveness and safety, and health economic value and impact. Recently, the US Food and Drug Administration and the European Medicines Agency have stated their ambition for greater use of RWE to support applications for new indications, and are now consulting with their stakeholders to formalize standards and expected methods for generating RWE. Pharmaceutical companies are responding to the increasing demands for RWE by developing standards and processes for each stage of the evidence generation pathway. Some conventions are already in place for assuring quality, whereas other processes are specific to the research question and data sources available. As evidence generation increasingly becomes a core role of medical affairs divisions in large pharmaceutical companies, standards of rigour will continue to evolve and improve. Senior pharmaceutical leaders can drive this change by making RWE a core element of their corporate strategy, providing top-level direction on how their respective companies should approach RWE for maximum quality. Here, we describe the current and future areas of RWE application within the pharmaceutical industry, necessary access to data to generate RWE, and the challenges in communicating RWE. Supporting and building on viewpoints from industry and publicly funded research, our perspective is that at each stage of RWE generation, quality will be critical to the impact that RWE has on healthcare decision-makers; not only where RWE is an established and evolving tool, but also in new areas that have the potential to disrupt and to improve drug development pathways.
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http://dx.doi.org/10.12688/f1000research.13585.2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6039945PMC
August 2019

Malignancy-related mortality following kidney transplantation is common.

Kidney Int 2014 Jun 20;85(6):1395-403. Epub 2013 Nov 20.

Department of Nephrology and Transplantation, Renal Institute of Birmingham, Queen Elizabeth Hospital, Birmingham, UK.

There is a paucity of studies describing malignancy-related mortality after kidney transplantation. To help quantify this, we extracted data for all kidney-alone transplant procedures performed in England between April 2001 and March 2012. Data linkage analysis was performed between Hospital Episode Statistics and the Office for National Statistics to identify all deaths occurring in this cohort. Among 19,103 kidney transplant procedures analyzed (median follow-up 4.4 years), 2085 deaths occurred, of which 376 (18.0%) were due to malignancy (crude mortality rate 361 malignancy-related deaths per 100,000 person-years). Common sites of malignancy-related death were lymphoma (18.4%), followed by lung (17.6%) and renal (9.8%), with 14.1% unspecified. The risk of malignancy-related death increased with age: under 50 (0.8%), 50-59 (2.5%), 60-69 (4.8%), 70-79 (6.5%) and over 80 years (9.1%). Age- and gender-stratified malignancy-related mortality risk difference was higher in the transplant compared with the general population. Cox proportional hazard models identified increased age, pretransplant history of malignancy and deceased-donor kidney transplantation to be independently associated with risk for post-transplant death from malignancy. Thus, malignancy as a cause of post-kidney transplantation death is common and requires heightened surveillance.
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http://dx.doi.org/10.1038/ki.2013.458DOI Listing
June 2014

Death within the first year after kidney transplantation--an observational cohort study.

Transpl Int 2014 Mar 14;27(3):262-70. Epub 2013 Nov 14.

Department of Nephrology and Transplantation, Renal Institute of Medicine, Queen Elizabeth Hospital, Birmingham, UK.

The risk of death within the first year postkidney transplantation is not well described in the contemporary era. We extracted data on all kidney transplant procedures performed in England between April 2001 and March 2012. Data linkage analysis was performed between Hospital Episode Statistics and the Office for National Statistics to identify all deaths. Cox proportional hazard models were performed to identify factors associated with 1-year mortality. 566 deaths (3.0%) occurred within the first year post-transplant (from 19,103 kidney transplant procedures analysed). Infection, cardiovascular events and malignancy were classified in 21.6%, 18.3% and 7.4% of death certificates, respectively. Among recipients with prior myocardial infarct history who died within the first year, 38.8% of deaths were attributed to a cardiac-related event. Malignancy-related death was responsible for 61.5% of 1-year mortality for allograft recipients with pretransplant cancer history. 22.1% of deaths included kidney failure as a contributory factor on the death certificate (3.3% specifically stated allograft failure). Variables associated with 1-year mortality included deceased-donor kidney, increasing age, residence in socioeconomically deprived area and history of select medical comorbidities pre-operatively. We conclude 1-year mortality postkidney transplantation is low, but in select allograft recipients, the risk of death increases considerably.
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http://dx.doi.org/10.1111/tri.12218DOI Listing
March 2014

Mortality after pediatric kidney transplantation in England--a population-based cohort study.

Pediatr Transplant 2014 Feb 18;18(1):16-22. Epub 2013 Oct 18.

Department of Nephrology and Transplantation, Renal Institute of Birmingham, Queen Elizabeth Hospital, Birmingham, UK.

The aim of this study was to explore mortality after pediatric kidney transplantation in England over the last decade. We used data from HES to select all kidney transplant procedures performed in England between April 2001 and March 2012. Data linkage analysis was performed with the ONS to identify all deaths occurring among this study cohort. Data for 1189 pediatric recipients were compared to 17 914 adult recipients (number of deaths, 33 vs. 2052, respectively, p < 0.001), with median follow-up 4.4 yr (interquartile range 2.2-7.3 yr). There was no difference in mortality within the pediatric cohort; age 0-1 (n = 25, patient survival 100.0%), age 2-5 (n = 198, patient survival 96.0%), age 6-12 (n = 359, patient survival 97.5%), and age 13-18 (n = 607, patient survival 97.4%), respectively (p = 0.567). The most common causes of death were renal (n = 8, 24.2%), infection (n = 6, 18.2%), and malignancy (n = 5, 15.2%). All deaths from malignancy were secondary to PTLD. In a fully adjusted Cox regression model, only white ethnicity was significantly associated with risk of pediatric mortality post-kidney transplantation (hazard ratio 2.7, 95% confidence interval [1.0-7.3], p = 0.047). To conclude, this population-based cohort study confirms low mortality after pediatric kidney transplantation with short follow-up.
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http://dx.doi.org/10.1111/petr.12173DOI Listing
February 2014

Socioeconomic deprivation is independently associated with mortality post kidney transplantation.

Kidney Int 2013 Oct 29;84(4):803-9. Epub 2013 May 29.

Department of Medical Informatics, Queen Elizabeth Hospital, Birmingham, UK.

The association between area socioeconomic deprivation and mortality post kidney transplantation is unclear. To clarify this, we obtained data from 19,103 kidney transplant procedures performed in England from April 2001 to March 2012. Patient demographics included age, gender, donor type (living or deceased), ethnicity, transplant year, allograft failure, medical comorbidities, and area socioeconomic deprivation (Index of Multiple Deprivation (2010)). Primary and secondary outcome measures were 1- and 5-year mortality with Cox proportional hazard models performed to identify independent factors associated with mortality. Data were broken down into quintiles of patients by area socioeconomic deprivation 1 to 5 (most to least deprived, respectively). At 1 year post transplant, 566 deaths were recorded, with infection being the most common cause of death. Compared with the most deprived individuals (reference point), the least deprived recipients had significantly decreased risk of death at 1 and 5 years post kidney transplant (hazard ratio 0.66, 95% CI (0.57-0.76) and hazard ratio 0.65, 95% CI (0.54-0.77), respectively). Thus, socioeconomic deprivation is independently associated with increased mortality post kidney transplantation.
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http://dx.doi.org/10.1038/ki.2013.176DOI Listing
October 2013

Can we update the Summary Hospital Mortality Index (SHMI) to make a useful measure of the quality of hospital care? An observational study.

BMJ Open 2013 Jan 30;3(1). Epub 2013 Jan 30.

Department of Primary Care and Population Health, PRIMENT Clinical Trials Unit, UCL Medical School, London, UK.

Objective: To advance methods for the estimation of hospital performance based upon mortality ratios.

Design: Observational study estimating trust performance in a year derived according to comparative standards from a 3-year period, accounting for patient-level case-mix and overdispersion (unexplained variability).

Participants: 23 363 630 admissions to the English National Health Service (NHS) by NHS Trust.

Main Outcome Measures: Number of SDs (QUality and Outcomes Research Unit Measure, QUORUM banding) and comparative odds of hospital mortality difference from mean performance by trust compared for 2010/2011, 2008/2009 and 2009/2010, accounting for patient-level case-mix.

Results: The model was highly predictive of mortality (C statistic=0.93), and well calibrated by risk stratum. There was substantial overdispersion. No trusts were more than 3 SDs above the mean, and only one trust was more than 2 SDs above the mean for 2010/2011.

Conclusions: QUORUM is highly predictive of patient mortality in hospital or up to 30 days after admission. However, like the Summary Hospital Mortality Indicator (SHMI), QUORUM is subjected to considerable remaining legitimate but unexplained variation. It is unlikely that measures like QUORUM and SHMI will be useful beyond identifying a very small number of trusts as potential outliers.
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http://dx.doi.org/10.1136/bmjopen-2012-002018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3563139PMC
January 2013