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. 2024 Dec 18;3(12):e0000691. doi: 10.1371/journal.pdig.0000691

EXAM: Ex-vivo allograft monitoring dashboard for the analysis of hypothermic machine perfusion data in deceased-donor kidney transplantation

Simon Schwab 1,2,*, Hélène Steck 1, Isabelle Binet 3, Andreas Elmer 1, Wolfgang Ender 3, Nicola Franscini 1, Fadi Haidar 4, Christian Kuhn 3, Daniel Sidler 5, Federico Storni 6, Nathalie Krügel 1, Franz Immer 1
Editor: Miguel Ángel Armengol de la Hoz7
PMCID: PMC11654979  PMID: 39693311

Abstract

Deceased-donor kidney allografts are exposed to ischemic injury during ex vivo transport due to the lack of blood oxygen supply. Hypothermic machine perfusion (HMP) effectively reduces the risk of delayed graft function in kidney transplant recipients compared to standard cold storage. However, no free software implementation is available to analyze HMP data for state-of-the-art visualization and quality control. We developed the tool EXAM (ex-vivo allograft monitoring) as an interactive analytics dashboard. We wrote functions in the R programming language to read, process, and analyze HMP data from the LifePort kidney transporter (Organ Recovery Systems, USA). Time series for pressure, flow rate, organ resistance, and temperature are visualized, and relevant statistical indicators have been developed. We explain how data were processed, and indicators were calculated, and we present summary statistics for N = 255 kidney allografts receiving machine perfusion in Switzerland between 2020 and 2023. Median (interdecile range, IDR) of the main indicators were as follows: perfusion duration 5.18 hours (2.29−11.2), flow rate 110 ml/min (52.9−167), ice temperature 1.97°C (1.53−3.07), and perfusate temperature 6.68°C (5.58−8.36). We implemented the dashboard to identify issues, such as atypical perfusion parameters, high ice, or high perfusate temperature to inform transplant centers for quality assurance. In conclusion, EXAM is a free tool that statisticians and data scientists can quickly deploy to enable quality control at transplant organizations that use LifePort kidney transporters. An online viewer is available at https://data.swisstransplant.org/exam/.

Author summary

In deceased-donor kidney transplantation, a widely used treatment is to perfuse the kidney with a cold preservation solution during transport (hypothermic machine perfusion). High-quality evidence shows that this intervention reduces the risk of delayed graft function in the recipient after transplantation. There are only a few devices available, among them the LifePort kidney transporter, which records the time series of the perfusion and temperature parameters (vascular resistance, flow rate, ice, and perfusate temperature). Currently, no software exists to read, process, visualize, and perform statistics with LifePort data. We created EXAM (ex-vivo allograft monitoring), a free tool that provides an online state-of-the-art analytics dashboard. Our work will enable transplant organizations to inspect their data, perform statistics and quality checks to help identify potential problems and optimize the intervention to the benefit of kidney recipients.

Introduction

Hypothermic machine perfusion (HMP) during transport partially preserves deceased-donor kidney allografts from ischemia-related damage due to the lack of blood oxygen. After retrieval, the kidney is connected to the machine and constantly perfused at a hypothermic temperature between 4°C and 10°C [1,2]. Commonly used kidney perfusion machines are LifePort (Organ Recovery systems; Itasca, IL, USA), KidneyAssist (XVIVO; Groningen, Netherlands), and Waters RM3 (Rochester, MI, USA). High-level evidence from a Cochrane systematic review suggests a 23% risk reduction for delayed graft function (DGF) after transplantation compared to static cold storage (risk ratio 0.77; 95% CI from 0.67 to 0.90) [3]. Furthermore, one-year graft survival was also superior, as shown in an international randomized controlled trial [4]; more recent studies add further evidence of the efficacy of machine perfusion [5,6].

In Switzerland, 291 deceased-donor kidneys were transplanted in 2023 [7], and 159 were subjected to machine perfusion. Kidneys that are at a higher risk receive HMP treatment. The criteria for kidney HMP are donation after circulatory death (DCD) or donation after brain death (DBD) with either donor age ≥ 70 years or a minimum of two out of three donor criteria: arterial hypertension, cerebrovascular cause of death, or last measured serum creatinine > 130 μmol/L; but these criteria are currently being revised. Each of the six transplant centers in Switzerland is equipped with two LifePort machines.

The devices are equipped with several sensors to monitor the progression of the perfusion characteristics. During HMP, the LifePort device records the following parameters every 10 seconds: systolic and diastolic pressure, flow rate, vascular resistance, and ice and perfusate temperature. Data are stored on the device and can be retrieved using a USB memory stick. LifePort’s manufacturer provides software that can create a PDF case report, including plots with the time series of the parameters. First, the data visualization is limited: the time series plots are static and do not show data outside a predefined range. Second, there are no summary statistics provided for statistical analysis and quality control. Therefore, this work aimed to provide an advanced statistical analysis tool to read, process, and analyze LifePort HMP data to evaluate the quality of individual machine perfusion interventions given perfusion and temperature parameters.

We created EXAM—ex-vivo allograft monitoring—a tool to inspect perfusion data interactively. It includes state-of-the-art methodology: an analytics dashboard with interactive plots, visual aids, and relevant statistical indicators for an overall assessment of successful HMP, enabling better quality control, incidence reporting, and novel research. The tool is available in the R programming language and can be easily deployed by data scientists or statisticians working in transplant organizations.

Materials and methods

Data collection

Coded data from deceased kidney donors were collected at the six kidney transplant centers in Switzerland (Inselspital, Bern University Hospital; University Hospital of Geneva; Cantonal Hospital St. Gallen; University Hospital Basel; Lausanne University Hospital; University Hospital Zurich). Each center was equipped with two LifePort perfusion machines (Organ Recovery Systems; ORS).

Ethics approval and consent to participate

The project was submitted to the Ethics Committee of the Canton of Bern, which granted an exemption from requiring ethics approval (KEK Bern; 2023–00557). Legal regulation in Switzerland follows an internationally applied distinction between research subject to approval and quality assurance that is not subject to approval.

For HMP data from deceased-donor kidneys, consent was impossible to obtain as these persons were ventilated and in intensive care with acute circulatory failure or with a diagnosis of brain death. The data were from deceased-donor kidneys transported to and transplanted at the six Swiss transplant centers: Inselspital, Bern University Hospital; University Hospital of Geneva; Cantonal Hospital St. Gallen; University Hospital Basel; Lausanne University Hospital; University Hospital Zurich.

Development and implementation

EXAM has been fully developed in the R programming language. The source code is available on GitHub (https://github.com/Swisstransplant/EXAM). An online version can be accessed at https://data.swisstransplant.org/exam/. EXAM was licensed under the GNU Affero General Public License (AGPL v3).

EXAM can be used in both a simple and an advanced configuration. The simple configuration is using the online tool to upload the raw data files to assess an individual case. No data is stored permanently except for the two example cases.

The advanced configuration is to install EXAM on a local computer and load all the available data into the dashboard. This has the advantage that a quality manager can access and review all the cases and data sets simultaneously. This procedure is shown in Fig 1 and consists of two steps: first, the data preparation (reading, processing, and the calculation of statistical indicators for all cases), and second, loading and viewing the data with the dashboard. The data preparation reads all individual LifePort data files and saves them into a single.RData file. The data preparation is required every time a new data set is added so that the latest cases are also shown in the dashboard. After data preparation, the dashboard can be loaded to import the.RData file. This two-step approach is simple, fast, and has the advantage that no server or database is required.

Fig 1. Schematic overview of EXAM when used in a local computer installation to access all the data available (advanced configuration).

Fig 1

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In the advanced configuration, the tool can be installed on a local computer by researchers, data scientists, or quality managers. The requirements are an installation of R, RStudio, and Quarto, which are all free software. Importantly, EXAM requires the Swisstransplant R package swt (https://github.com/Swisstransplant/swt), which provides the core functionality (Table 1).

Table 1. Summary of main functions and procedures for processing LifePort HMP data. The data is processed in three steps: data import, data processing, and calculation of the statistical indicators.

Source Implementation Responsibility
R package swt Data import
swt::lifeport_read()		 Reads binary or ASCII raw data files obtained from LifePort machines. A list of three objects is returned containing device information (serial number, unit ID, start time, run time, etc.), organ information (organ ID, kidney side, blood type, etc.), and time series data (systolic pressure, diastolic pressure, flow rate, vascular resistance, ice temperature, and perfusate temperature).
Data processing
swt::lifeport_process()		 Adds actual clock time given the device start time. Pressure, flow, and vascular resistance time series are smoothed using a moving average with a window size of 15 samples (150 seconds). Temperature time series are not smoothed.
Statistical indicators
swt::lifeport_sumstats()		 Calculates various statistical indicators based on pressure, flow rate, vascular resistance, and temperature time series data.
EXAM Data preparation
Any custom R script or Quarto document		 Data preparation pipeline that applies the above swt functions sequentially (simple for loop) to import, process, and calculate statistical indicators for every dataset. The aggregated data is stored in a single RData container.
Quarto dashboard
EXAM-app.qmd		 Loads the data container and visualizes all the cases in an interactive analytics dashboard based on Quarto dashboard, Plotly, and Shiny.
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Data import

The LifePort raw data is available in two file types. One is a binary raw data file stored directly on the LifePort machine. This file is the best option as no additional software and manual conversion is required. The second type is a plain text ASCII file converted from the binary raw data using the manufacturer’s software. Note that both file types contain the same information and a temporal resolution of 1/10 Hz. In other words, they are equivalent from a data analysis perspective.

Most of the data we collected was in the original binary raw data format; however, we sometimes receive ASCII plain text files. We implemented a function lifeport_read() as part of the swt package that can handle both file types. For this aim, the structure of the binary raw data file had to be recovered, i.e., what parts of the binary code contained what information (time series, organ ID, kidney side, serial number, unit ID, start time, etc.). With the swt package, it is now possible to interact directly with LifePort HMP raw data from within R/RStudio.

Data processing

Data processing with lifeport_process() handles the actual time of the machine perfusion so that the time series in the plots do not start from time zero but at the actual clock time of the start of the HMP; this may be helpful for quality control and incidence reporting to see events against the actual clock time. Furthermore, the time series of pressure, flow rate, and vascular resistance were smoothed (Fig 2); for temperature data, this step was not necessary.

Fig 2.

Fig 2

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Time series for A systolic pressure (diastolic pressure not shown), B flow rate, and C vascular resistance were smoothed (pink line) using a rolling average with a window size of 150 seconds (15 samples).

Statistical indicators

During visual inspection of many different HMP cases, we developed statistical indicators that seem relevant for quality control. The function lifeport_sumstats() calculates these; they are explained in Table 2.

Table 2. Statistical indicators for quality control.

Class Indicator Meaning Unit Condition
Perfusion md sys median systolic pressure mmHg pressure must be > 0.
indicators m dia mean diastolic pressure mmHg pressure must be > 0.
m flow mean flow rate ml/min first 30 min. excluded; flow must be > 5.
m res mean vascular resistance mmHg/ml/min first 30 min. excluded; resistance must be > 0.
SD res std. dev of vascular resistance mmHg/ml/min first 30 min. excluded; resistance must be > 0.
Temperature m ice mean ice temperature °C –
indicators SD ice std. dev. of ice temperature °C –
Δt y>2.5 time duration ice above 2.5°C MM:HH:SS –
y start perfusate temperature at start °C window of 5 min. after discarding first 2 min; flow must be > 5.
m perf mean perfusate temperature °C first 2 min. are excluded and flow must be > 5.
SD perf std. dev. of perfusate temperature °C first 2 min. are excluded and flow must be > 25.
Δt y>10 time duration perfusate temperature above 10°C MM:HH:SS first 2 min. are excluded and flow must be > 25.
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Some conditions must be met to calculate the indications to reflect an accurate summary. For example, when the machine stops the perfusion data is still recorded and the pressure will be 0. Thus, we only calculate the mean pressure across the period where pressure was larger than 0, i.e., the period the kidney was perfused. The same principle affects the mean flow; however, here, we use a cutoff of 5 as flow is never precisely 0, and there is always some residual signal. Another example is the mean perfusate temperature, which is only calculated for time segments with flow larger than 25. Without flow, the perfusate temperature sensor will record a high temperature, but perfusate temperature can be disregarded when the kidney is not perfused.

For the three indicators based on flow rate and vascular resistance, the first 30 min. are excluded as this is the period where the flow rate builds up (and the resistance decreases) until the kidney reaches a steady state. Thus, these indicators are not calculated if the overall perfusion duration is below 30 min.

Dashboard, data visualization, and reporting

The dashboard has been implemented with Quarto, Shiny, and Plotly [8,9]; see Fig 3. This allows the user to interact with the data, for example, zooming in and out, data value display on hoover, panning, autoscale, reset axes, etc. This highly facilitates data inspection and quality control.

Fig 3. EXAM dashboard.

Fig 3

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Organ and device information is on the top; below interactive plots with time series data and statistical indicators. On the left is a dropdown menu to select cases and a button to upload a LifePort data file.

Visual aids were added to the plots that facilitate interpretation, such as horizontal dashed lines for optimal ranges or critical thresholds, for example, 10°C as the upper limit of HMP or 5°C for ice temperature, which is the threshold to produce an alarm by the perfusion machine.

Validation

We validated the data displayed by the EXAM dashboard (time series) by comparing our figures with the case reports created by the manufacturer’s software (ORS Data Station). However, we directly read the time series data from the raw data files with minimal processing (smoothing). The indicators are based on medians, means, or standard deviations and do not require validation.

Results

Descriptive statistics

We collected 266 LifePort datasets from 1. January 2020 to 31. December 2023. Eleven cases (4.1%) were excluded due to missing data for calculating the statistical indicators. These were caused by a perfusion time of below 30 minutes, and in such cases, flow and vascular resistance indicators were not calculated. The analysis data set had a sample size of N = 255 kidneys with machine perfusion. These kidneys were from 184 deceased donors, of which 131 (71.2%) were organ donations after circulatory death (DCD), and 53 (28.8%) were donations after brain death (DBD). The median age of the donors was 58 years (range 11–86 years). Summary statistics of the HMP indicators are shown in Table 3; the distributions are shown in Fig 4.

Table 3. Descriptive statistics (median and interdecile range) for the perfusion duration and the statistical indicators from N = 255 kidney allografts receiving HMP.

Median across kidneys (N = 255) Interdecile range (10th–90th percentile)
Perfusion duration (hours) 5.18 2.29–11.2
Perfusion indicators
 Median systolic pressure (mmHg) 29.4 21.7–29.6
 Mean diastolic pressure (mmHg) 18.9 15.1–22.4
 Mean flow rate (ml/min) 110 52.9–167
 Mean vascular resistance (mmHg/ml/min) 0.21 0.11–0.43
 SD vascular resistance (mmHg/ml/min) 0.02 0.01–0.09
Temperature indicators
 Mean ice temperature (°C) 1.97 1.53–3.07
 SD ice temperature (°C) 0.20 0.09–0.75
 Duration ice temperature > 2.5°C (min) 0.83 0–379
 Mean perfusate temperature (°C) 6.68 5.58–8.36
 SD perfusate temperature (°C) 0.94 0.46–1.49
 Duration perfusate temperature > 10°C (min) 0.17 0–30.8
 Start perfusate temperature (°C) 9.15 7.16–12.0
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Fig 4.

Fig 4

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Data distributions of A perfusion duration and perfusion indicators, and B temperature indicators.

The capability of EXAM and the developed statistical indicators is now highlighted in a short example.

Example: Comparing perfusate and ice temperature

We compare the temperature profiles of two cases. In Fig 5A, the start perfusate temperature was 8.2°C, and the mean was 5.7°C. In Fig 5B, the initial state of the perfusate was 14.9°C, and the mean was 9.3°C. The perfusate temperature was above 10°C for 1 hour and 4 minutes. The difference is also highlighted by an almost 3-fold increase in the variation (0.96°C versus 2.8°C). A smaller variation often reflects a more horizontal, stable curve, while a larger variation indicates a more steeply falling (or rising) curve or any other type of fluctuation. Regarding the ice temperature, both means were below average (1.2°C and 1.8°C) and stable (a slight variation of 0.07°C and 0.11°C across time).

Fig 5.

Fig 5

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Two HMP temperature plots with A average and B elevated perfusate temperature. Visual aids help interpret the data: horizontal lines at 10°C (the upper limit for hypothermic perfusion) and 5°C (LifePort device will start an alarm when ice temperature is above 5°C). The pink line highlights the perfusate temperature above the hypothermic threshold of 10°C. The indicators are shown below the plots.

Discussion

EXAM is a free tool in the R programming language (with Quarto dashboard and R Shiny) for the visual inspection and quality control of HMP data from the LifePort kidney transporter. Its main features are an analytics dashboard with interactive plots and calculation of statistical indicators for quality control. Furthermore, we provide an example for interpreting the statistical indicators concerning temperature.

There is ample evidence that HMP effectively lowers DGF and prolongs graft survival [3,4]. However, this requires optimal conditions in terms of perfusion and temperature during the treatment of the kidney allograft. One criticism of clinical trials is that they are artificial and do not provide real-life evidence. Therefore, quality assurance of the intervention in clinical practice is crucial to achieving the treatment benefit.

EXAM was developed for retrospective analysis of HMP data for quality control purposes. It helps to identify problems and can improve and optimize kidney HMP. Kidney allografts, mainly marginal kidneys, should receive the best treatment in the critical period between procurement and transplantation. This may enhance patient outcomes and help reduce additional costs, for example, when fewer kidney recipients experience DGF after transplantation. EXAM is not a tool for decision-making at the time of transplantation; e.g., HMP data alone should never be used to discard a kidney allograft. Further research may investigate the potential role of anomalies, such as those observed in vascular resistance, in clinical decision-making [10].

A limitation of the study may be the risk in case the manufacturer ORS changes the structure of the data file. Then, EXAM would not work anymore with new data. However, the risk is largely mitigated as the tool is under an open-source license. This would enable everyone, not only Swisstransplant, to modify and share a new version of the tool as long as it is released under the same conditions, i.e., everyone can again modify and share the code, as stated in the AGPLv3 license.

In the future, the dashboard could be expanded to include additional information on error events, such as exceeding the maximum ice temperature, not reaching the required pressure, or check filter. Another idea is to use the Mahalanobis distance to calculate a quality measure based on the various perfusion and temperature indicators. This would identify cases with large distances from the norm (across the indicators’ multiple dimensions) and monitor quality over a longer period of time.

Most importantly, the open development of EXAM on GitHub allows data scientists and statisticians to get involved and transplant organizations to use the tool free of charge.

Conclusions

EXAM is a dashboard based on R and Quarto to visualize HMP data from the LifePort kidney transporter. Quality control with EXAM can identify problems during HMP and support guidelines, checklists, and training for healthcare providers. This assures optimal therapy, which has been demonstrated to be effective in preventing DGF and dialysis after transplantation.

Data Availability

The source code and anonymized example perfusion datasets are available online: https://github.com/Swisstransplant/EXAM. An online version of the tool is available at https://data.swisstransplant.org/exam/.

Funding Statement

The author(s) received no specific funding for this work.

References

  • 1.Jochmans I, Akhtar MZ, Nasralla D, Kocabayoglu P, Boffa C, Kaisar M, et al. Past, Present, and Future of Dynamic Kidney and Liver Preservation and Resuscitation. Am J Transplant. 2016;16: 2545–2555. doi: 10.1111/ajt.13778 [DOI] [PubMed] [Google Scholar]
  • 2.Zulpaite R, Miknevicius P, Leber B, Strupas K, Stiegler P, Schemmer P. Ex-vivo Kidney Machine Perfusion: Therapeutic Potential. Front Med. 2021;8: 808719. doi: 10.3389/fmed.2021.808719 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Tingle SJ, Figueiredo RS, Moir JA, Goodfellow M, Talbot D, Wilson CH. Machine perfusion preservation versus static cold storage for deceased donor kidney transplantation. Cochrane Database Syst Rev. 2019;3: CD011671. doi: 10.1002/14651858.CD011671.pub2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Moers C, Smits JM, Maathuis M-HJ, Treckmann J, van Gelder F, Napieralski BP, et al. Machine perfusion or cold storage in deceased-donor kidney transplantation. N Engl J Med. 2009;360: 7–19. doi: 10.1056/NEJMoa0802289 [DOI] [PubMed] [Google Scholar]
  • 5.Malinoski D, Saunders C, Swain S, Groat T, Wood PR, Reese J, et al. Hypothermia or Machine Perfusion in Kidney Donors. N Engl J Med. 2023;388: 418–426. doi: 10.1056/NEJMoa2118265 [DOI] [PubMed] [Google Scholar]
  • 6.Brat A, de Vries KM, van Heurn EWE, Huurman VAL, de Jongh W, Leuvenink HGD, et al. Hypothermic Machine Perfusion as a National Standard Preservation Method for Deceased Donor Kidneys. Transplantation. 2022;106: 1043–1050. doi: 10.1097/TP.0000000000003845 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Annual Report 2023. Swisstransplant; 2024. Available: https://www.swisstransplant.org/jahresbericht
  • 8.Sievert C. Interactive Web-Based Data Visualization with R, plotly, and shiny. CRC Press; 2020. Available: https://play.google.com/store/books/details?id=7zPNDwAAQBAJ [Google Scholar]
  • 9.Chang W, Cheng J, Allaire JJ, Sievert C, Schloerke B, Xie Y, et al. shiny: Web Application Framework for R. 2022. Available: https://shiny.rstudio.com/ [Google Scholar]
  • 10.Weberskirch S, Katou S, Reuter S, Kneifel F, Morgul MH, Becker F, et al. Dynamic Parameters of Hypothermic Machine Perfusion-An Image of Initial Graft Function in Adult Kidney Transplantation? J Clin Med Res. 2022;11. doi: 10.3390/jcm11195698 [DOI] [PMC free article] [PubMed] [Google Scholar]
PLOS Digit Health. doi: 10.1371/journal.pdig.0000691.r001

Decision Letter 0

Miguel Ángel Armengol de la Hoz

17 May 2024

PDIG-D-24-00069

EXAM: Ex vivo allograft monitoring dashboard for the analysis of hypothermic machine perfusion data in deceased-donor kidney transplantation

PLOS Digital Health

Dear Dr. Schwab,

Thank you for submitting your manuscript to PLOS Digital Health. After careful consideration, we feel that it has merit but does not fully meet PLOS Digital Health's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1. Write a separate section for literature review/ Related work.

2. Research requires mathematical & statistical analysis. But no

mathematical equations found in the study.

3. Draw a work flow diagram to implement the methodology.

4. Write an algorithm to support the methodology.

5.Need a comparative study with results with this study and related similar types

already published articles.(In tabular form)

6. Need few more references from recent publications.

Reviewer #2: The topic of the paper is interesting but it needs major improvements as below:

The abstracts should be rewritten to show more results and what was achieved from the created dashboard. The present abstract is very brief and only highlights the background and methods with minimal findings.

Remove R programming language from the keywords.

In the method section, it is not clear whether the data collected were anonymous. Was there a framework for data collection?

What was done about missing data?

How were the findings validated?

The parameters collected seem to be incomprehensive. For example patients' clinical characteristics such as comorbidities, risk factors etc... should be included.

In the results, it is not clear how these indicators helped the patients.

The discussion is rather a conclusion that is under-referenced. The discussion section is too brief and does not justify the findings in terms of the literature. It should be rewritten to highlight the implications of the findings on the literature and on practice.

The conclusion should be more summative and informative

Reviewer #3: Abstract and Author Summary

What is not clear from the abstract and author summary is whether EXAM is a new tool that the authors have developed. The authors say there is currently no free software. Then immediately talk about R which is an open source software. Please clarify this.

Introduction

There same issue with abstract and author summary. EXAM is being introduced without clear information whether this is an existing platform, is it an R package? Did the authors develop it from scratch? Please clarify.

There is a hanging statement in paragraph three… There are two significant ??

Development and Implementation

Based on the links – This looks like a Shiny Dashboard. Is this what the authors call EXAM? If so, then EXAM is not a newly created software, it is a visualization platform which reads data from the LifePort machine.

If the above paragraph is correct, then the innovation here is the use of shiny dashboards to visualize the data that has traditionally not been visualized that way.

Then, it is not correct to say there is no free software because what the authors have done is to utilize an existing software (R), which is free, to visualize data.

Data import

Write ASCII in full. Ideally, an acronym needs to be written in full if using it for the first time in the document.

Statistical indicators

I think it is not correct to say statistical indicators. In other words, are there are other indicators that are not “statistical”? I would suggest, just have the topic to read “Indicators” or “Indicators of interest” or “Outcomes”

Results

Table 3: The title talks about median, but the results includes some indicators summarized using mean. What is unclear is, for the indicators summarized using mean, why do we use the 10-90th percentile range? Mean is usually reported alongside standard deviation. Please clarify what is happening here.

Discussion

This is too brief. I find the discussion to be lacking in depth and width. It is a major shortcoming of this manuscript.

I expected the discussion to focus on;

a) Atleat a paragraph on summary of the key findings

b) A few paragraphs to discuss what new knowledge or evidence your research has added in the context of other findings in literature. I don’t see any citations in the discussion. I know of papers that have been published that have used RShiny. This is very important. This should be a major part of the discussion.

c) A paragraph to discuss any strengths for your research

d) A paragraph on the limitations

e) Conclusions

Conclusion:

What is DGF in full? I may have missed it, but as I noted earlier, any acronymn should be written in full if appearing for the first time.

I think the conclusion should focus on what is new, and how can it be scaled up? What policy implications can it have?

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For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Dr. Sumanta Kuila

Reviewer #2: No

Reviewer #3: No

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PLOS Digit Health. doi: 10.1371/journal.pdig.0000691.r003

Decision Letter 1

Miguel Ángel Armengol de la Hoz

7 Nov 2024

EXAM: Ex vivo allograft monitoring dashboard for the analysis of hypothermic machine perfusion data in deceased-donor kidney transplantation

PDIG-D-24-00069R1

Dear Dr Schwab,

We are pleased to inform you that your manuscript 'EXAM: Ex vivo allograft monitoring dashboard for the analysis of hypothermic machine perfusion data in deceased-donor kidney transplantation' has been provisionally accepted for publication in PLOS Digital Health.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team. 

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they'll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact digitalhealth@plos.org.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Digital Health.

Best regards,

Miguel Ángel Armengol de la Hoz, Ph.D.

Section Editor

PLOS Digital Health

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Additional Editor Comments (if provided):

Reviewer Comments (if any, and for reference):

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Does this manuscript meet PLOS Digital Health’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: N/A

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Digital Health does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: No further comments

Reviewer #3: I am happy with the edits the authors have made. However, I disagree with their response on the discussion. If there is "no new knowledge" (using the author's words), then why publish? Scientific publications are meant to extend knowledge, add new knowledge, filling a knowledge gap.

I still feel the authors need to discuss what their innovation does and what implications it may have both for practice and future research. This is so important to contextualize the paper in the broader body of knowledge.

I still feel the discussion is lacking in depth. This needs to be improved.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

Do you want your identity to be public for this peer review? If you choose “no”, your identity will remain anonymous but your review may still be made public.

For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Attachment

    Submitted filename: EXAM-Plos DH-response-final.docx

    pdig.0000691.s001.docx (67.7KB, docx)

    Data Availability Statement

    The source code and anonymized example perfusion datasets are available online: https://github.com/Swisstransplant/EXAM. An online version of the tool is available at https://data.swisstransplant.org/exam/.

    Articles from PLOS Digital Health are provided here courtesy of PLOS

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