Using the Observational Medical Outcomes Partnership Common Data Model for a multi-registry intensive care unit benchmarking federated analysis: lessons learned

Aasiyah Rashan; Daniel P. Püttmann; Nicolette F. De Keizer; Dave A. Dongelmans; Ronald Cornet; Otavio Ranzani; Wangari Waweru-Siika; Matthew Smith; Steve Harris; Abi Beane; Ferishta Bakhshi-Raiez; Roya Afzali; Noorullah Ahmadzai; Mirwais Azizi; Nasibullah Barukzai; Maryam Barukzay; Naqibullah Danish; Maliha Farooq; Maryam Shamal Ghalib; Owais Urhman Ghalib; Rahim Mazloomyar; Shoaib Mirzada; Meher Negar; Bahar Nadim; Abdul Majid Rahimi; Muhammad Dawood Safi; Muhammad Hamid Rahimi Safi; Guldad Khan Saifi; Ahmad Zakariya Shinwary; Hiranmoy Dutta; Enshad Ekramullah; Aniruddha Ghose; Md Hassanuzzaman; Muna Islam; Mahabubul Alam Khondokar; Md Abdur Rahim; Md Harun Or Rashid; Md Abdus Sattar; Abdullah Abu Sayeed; Sarkar Shoman; Md Rezaul Hoque Tipu; Rabiul Alam Md Erfan Uddin; Mohammed Jashim Uddin; A. S.M. Zahed; Menbeu Sultan; John Amuasi; Joe Bonney; Moses Siaw Frimpong; Mohd Shahnaz Hasan; Mohd Basri Mat Nor; Mohd Zulfakar Mazlan; Isha Amatya; Diptesh Aryal; Basanta Gauli; Praveen Giri; Kishor Khanal; Sushil Khanal; Sabin Koirala; Sanjay Lakhey; Subekshya Luitel; Hem Raj Paneru; Sushila Paudel; Lalit Rajbanshi; Sangina Ranjit; Yam Roka; Pramesh Sundar Shrestha; Raju Shrestha; Pradeep Tiwari; Wangari Waweru-Siika; Madiha Hashmi; Eva Hanciles; Luigi Pisani; Dave Thomson, Martha Alupo; Adam Hewitt Smith; Dennis Kakaire; Herbert Kiwalya; Joseph Kiwanuka; Arthur Kwizera; Joshua Muhanguzi; Cornelius Sendagire; Udara Attanayake; Abi Beane; Sri Darshana; Arjen M. Dondorp; Layoni Dullewe; Nilmini P. Dullewe; Kaumali Gimhani; Judy Ann Gitahi; Rashan Haniffa; Pramodya Ishani; Chamira Kodippily; Issrah Jawad; Shiekh Mohiuddin; Himasha Muvindi; Upule Pabasara; Luigi Pisani; Dilanthi Priyadarshani; Disna Pujika; Aasiyah Rashan; Sumayyah Rashan; Thalha Rashan; Shoba Sathasivam; Timo Tolppa; Shara Udayanga

doi:10.1093/jamiaopen/ooaf052

Using the Observational Medical Outcomes Partnership Common Data Model for a multi-registry intensive care unit benchmarking federated analysis: lessons learned

Aasiyah Rashan, Daniel P. Püttmann, Nicolette F. De Keizer, Dave A. Dongelmans, Ronald Cornet, Otavio Ranzani, Wangari Waweru-Siika, Matthew Smith, Steve Harris, Abi Beane, Ferishta Bakhshi-Raiez, Roya Afzali, Noorullah Ahmadzai, Mirwais Azizi, Nasibullah Barukzai, Maryam Barukzay, Naqibullah Danish, Maliha Farooq, Maryam Shamal Ghalib, Owais Urhman GhalibRahim Mazloomyar, Shoaib Mirzada, Meher Negar, Bahar Nadim, Abdul Majid Rahimi, Muhammad Dawood Safi, Muhammad Hamid Rahimi Safi, Guldad Khan Saifi, Ahmad Zakariya Shinwary, Hiranmoy Dutta, Enshad Ekramullah, Aniruddha Ghose, Md Hassanuzzaman, Muna Islam, Mahabubul Alam Khondokar, Md Abdur Rahim, Md Harun Or Rashid, Md Abdus Sattar, Abdullah Abu Sayeed, Sarkar Shoman, Md Rezaul Hoque Tipu, Rabiul Alam Md Erfan Uddin, Mohammed Jashim Uddin, A. S.M. Zahed, Menbeu Sultan, John Amuasi, Joe Bonney, Moses Siaw Frimpong, Mohd Shahnaz Hasan, Mohd Basri Mat Nor, Mohd Zulfakar Mazlan, Isha Amatya, Diptesh Aryal, Basanta Gauli, Praveen Giri, Kishor Khanal, Sushil Khanal, Sabin Koirala, Sanjay Lakhey, Subekshya Luitel, Hem Raj Paneru, Sushila Paudel, Lalit Rajbanshi, Sangina Ranjit, Yam Roka, Pramesh Sundar Shrestha, Raju Shrestha, Pradeep Tiwari, Wangari Waweru-Siika, Madiha Hashmi, Eva Hanciles, Luigi Pisani, Dave Thomson, Martha Alupo, Adam Hewitt Smith, Dennis Kakaire, Herbert Kiwalya, Joseph Kiwanuka, Arthur Kwizera, Joshua Muhanguzi, Cornelius Sendagire, Udara Attanayake, Abi Beane, Sri Darshana, Arjen M. Dondorp, Layoni Dullewe, Nilmini P. Dullewe, Kaumali Gimhani, Judy Ann Gitahi, Rashan Haniffa, Pramodya Ishani, Chamira Kodippily, Issrah Jawad, Shiekh Mohiuddin, Himasha Muvindi, Upule Pabasara, Luigi Pisani, Dilanthi Priyadarshani, Disna Pujika, Aasiyah Rashan, Sumayyah Rashan, Thalha Rashan, Shoba Sathasivam, Timo Tolppa, Shara Udayanga

Anaesthesiology, East Africa

Research output: Contribution to journal › Article › peer-review

Abstract

Objective Federated analysis is a method that allows data analysis to be performed on similar datasets without exchanging any data, thus facilitating international research collaboration while adhering to strict privacy laws. This study aimed to evaluate the feasibility of using federated analysis to benchmark mortality in 2 critical care quality registry databases converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), describing challenges to and recommendations for performing federated analysis on data transformed to OMOP CDM. Materials and Methods To identify as many challenges as possible and to be able to complete the benchmarking phase, a 2-step approach was taken during implementation. The first step was a naive implementation to allow challenges to surface naturally; the second step was developing solutions for the encountered challenges. Expected patient mortality risk was calculated by applying the Acute Physiology and Chronic Health Evaluation II (APACHE II) model to data from OMOP CDM databases containing adult ICU encounters between July 1, 2019 and December 31, 2022. An analysis script was developed to calculate comparable, registry level standardized mortality ratios. Challenges were recorded and categorized into predefined categories: "data preparation,""data analysis plan,"and "data interpretation."Challenges specific to the OMOP CDM were further categorized using published steps from an existing generic harmonization process. Results A total of 7 challenges were identified, 4 of which were related to data preparation, 1 to data analysis, and 1 to data interpretation. Out of all 7 challenges, 4 stemmed from decisions made during the implementation of OMOP CDM. Several recommended solutions were distilled from the naive approach. Discussion Federated analysis facilitated by a CDM is a feasible option for critical care quality registries. However, future analysis is influenced by decisions made during the CDM implementation process. Thus, prior publication of data dictionaries and the use of metadata to communicate data handling and data source classification during CDM implementation will improve the efficiency and accuracy of subsequent analysis.

Original language	English (US)
Article number	ooaf052
Journal	JAMIA Open
Volume	8
Issue number	4
DOIs	https://doi.org/10.1093/jamiaopen/ooaf052
Publication status	Published - 1 Aug 2025

Keywords

APACHE II
OMOP CDM
critical care
data standardization
federated analysis
global health
registries

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10.1093/jamiaopen/ooaf052

Cite this

Rashan, A., Püttmann, D. P., De Keizer, N. F., Dongelmans, D. A., Cornet, R., Ranzani, O., Waweru-Siika, W., Smith, M., Harris, S., Beane, A., Bakhshi-Raiez, F., Afzali, R., Ahmadzai, N., Azizi, M., Barukzai, N., Barukzay, M., Danish, N., Farooq, M., Ghalib, M. S., ... Udayanga, S. (2025). Using the Observational Medical Outcomes Partnership Common Data Model for a multi-registry intensive care unit benchmarking federated analysis: lessons learned. JAMIA Open, 8(4), Article ooaf052. https://doi.org/10.1093/jamiaopen/ooaf052

@article{320058da829c4129b428ee5b4ab2c5e1,

title = "Using the Observational Medical Outcomes Partnership Common Data Model for a multi-registry intensive care unit benchmarking federated analysis: lessons learned",

abstract = "Objective Federated analysis is a method that allows data analysis to be performed on similar datasets without exchanging any data, thus facilitating international research collaboration while adhering to strict privacy laws. This study aimed to evaluate the feasibility of using federated analysis to benchmark mortality in 2 critical care quality registry databases converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), describing challenges to and recommendations for performing federated analysis on data transformed to OMOP CDM. Materials and Methods To identify as many challenges as possible and to be able to complete the benchmarking phase, a 2-step approach was taken during implementation. The first step was a naive implementation to allow challenges to surface naturally; the second step was developing solutions for the encountered challenges. Expected patient mortality risk was calculated by applying the Acute Physiology and Chronic Health Evaluation II (APACHE II) model to data from OMOP CDM databases containing adult ICU encounters between July 1, 2019 and December 31, 2022. An analysis script was developed to calculate comparable, registry level standardized mortality ratios. Challenges were recorded and categorized into predefined categories: {"}data preparation,{"}{"}data analysis plan,{"}and {"}data interpretation.{"}Challenges specific to the OMOP CDM were further categorized using published steps from an existing generic harmonization process. Results A total of 7 challenges were identified, 4 of which were related to data preparation, 1 to data analysis, and 1 to data interpretation. Out of all 7 challenges, 4 stemmed from decisions made during the implementation of OMOP CDM. Several recommended solutions were distilled from the naive approach. Discussion Federated analysis facilitated by a CDM is a feasible option for critical care quality registries. However, future analysis is influenced by decisions made during the CDM implementation process. Thus, prior publication of data dictionaries and the use of metadata to communicate data handling and data source classification during CDM implementation will improve the efficiency and accuracy of subsequent analysis.",

keywords = "APACHE II, OMOP CDM, critical care, data standardization, federated analysis, global health, registries",

author = "Aasiyah Rashan and P{\"u}ttmann, \{Daniel P.\} and \{De Keizer\}, \{Nicolette F.\} and Dongelmans, \{Dave A.\} and Ronald Cornet and Otavio Ranzani and Wangari Waweru-Siika and Matthew Smith and Steve Harris and Abi Beane and Ferishta Bakhshi-Raiez and Roya Afzali and Noorullah Ahmadzai and Mirwais Azizi and Nasibullah Barukzai and Maryam Barukzay and Naqibullah Danish and Maliha Farooq and Ghalib, \{Maryam Shamal\} and Ghalib, \{Owais Urhman\} and Rahim Mazloomyar and Shoaib Mirzada and Meher Negar and Bahar Nadim and Rahimi, \{Abdul Majid\} and Safi, \{Muhammad Dawood\} and Safi, \{Muhammad Hamid Rahimi\} and Saifi, \{Guldad Khan\} and Shinwary, \{Ahmad Zakariya\} and Hiranmoy Dutta and Enshad Ekramullah and Aniruddha Ghose and Md Hassanuzzaman and Muna Islam and Khondokar, \{Mahabubul Alam\} and Rahim, \{Md Abdur\} and \{Or Rashid\}, \{Md Harun\} and Sattar, \{Md Abdus\} and Sayeed, \{Abdullah Abu\} and Sarkar Shoman and Tipu, \{Md Rezaul Hoque\} and Uddin, \{Rabiul Alam Md Erfan\} and Uddin, \{Mohammed Jashim\} and Zahed, \{A. S.M.\} and Menbeu Sultan and John Amuasi and Joe Bonney and Frimpong, \{Moses Siaw\} and Hasan, \{Mohd Shahnaz\} and Nor, \{Mohd Basri Mat\} and Mazlan, \{Mohd Zulfakar\} and Isha Amatya and Diptesh Aryal and Basanta Gauli and Praveen Giri and Kishor Khanal and Sushil Khanal and Sabin Koirala and Sanjay Lakhey and Subekshya Luitel and Paneru, \{Hem Raj\} and Sushila Paudel and Lalit Rajbanshi and Sangina Ranjit and Yam Roka and Shrestha, \{Pramesh Sundar\} and Raju Shrestha and Pradeep Tiwari and Wangari Waweru-Siika and Madiha Hashmi and Eva Hanciles and Luigi Pisani and Alupo, \{Dave Thomson, Martha\} and Smith, \{Adam Hewitt\} and Dennis Kakaire and Herbert Kiwalya and Joseph Kiwanuka and Arthur Kwizera and Joshua Muhanguzi and Cornelius Sendagire and Udara Attanayake and Abi Beane and Sri Darshana and Dondorp, \{Arjen M.\} and Layoni Dullewe and Dullewe, \{Nilmini P.\} and Kaumali Gimhani and Gitahi, \{Judy Ann\} and Rashan Haniffa and Pramodya Ishani and Chamira Kodippily and Issrah Jawad and Shiekh Mohiuddin and Himasha Muvindi and Upule Pabasara and Luigi Pisani and Dilanthi Priyadarshani and Disna Pujika and Aasiyah Rashan and Sumayyah Rashan and Thalha Rashan and Shoba Sathasivam and Timo Tolppa and Shara Udayanga",

note = "Publisher Copyright: {\textcopyright} 2025 The Author(s).",

year = "2025",

month = aug,

day = "1",

doi = "10.1093/jamiaopen/ooaf052",

language = "English (US)",

volume = "8",

journal = "JAMIA Open",

issn = "2574-2531",

publisher = "Oxford University Press",

number = "4",

}

Rashan, A, Püttmann, DP, De Keizer, NF, Dongelmans, DA, Cornet, R, Ranzani, O, Waweru-Siika, W, Smith, M, Harris, S, Beane, A, Bakhshi-Raiez, F, Afzali, R, Ahmadzai, N, Azizi, M, Barukzai, N, Barukzay, M, Danish, N, Farooq, M, Ghalib, MS, Ghalib, OU, Mazloomyar, R, Mirzada, S, Negar, M, Nadim, B, Rahimi, AM, Safi, MD, Safi, MHR, Saifi, GK, Shinwary, AZ, Dutta, H, Ekramullah, E, Ghose, A, Hassanuzzaman, M, Islam, M, Khondokar, MA, Rahim, MA, Or Rashid, MH, Sattar, MA, Sayeed, AA, Shoman, S, Tipu, MRH, Uddin, RAME, Uddin, MJ, Zahed, ASM, Sultan, M, Amuasi, J, Bonney, J, Frimpong, MS, Hasan, MS, Nor, MBM, Mazlan, MZ, Amatya, I, Aryal, D, Gauli, B, Giri, P, Khanal, K, Khanal, S, Koirala, S, Lakhey, S, Luitel, S, Paneru, HR, Paudel, S, Rajbanshi, L, Ranjit, S, Roka, Y, Shrestha, PS, Shrestha, R, Tiwari, P, Waweru-Siika, W, Hashmi, M, Hanciles, E, Pisani, L, Alupo, DTM, Smith, AH, Kakaire, D, Kiwalya, H, Kiwanuka, J, Kwizera, A, Muhanguzi, J, Sendagire, C, Attanayake, U, Beane, A, Darshana, S, Dondorp, AM, Dullewe, L, Dullewe, NP, Gimhani, K, Gitahi, JA, Haniffa, R, Ishani, P, Kodippily, C, Jawad, I, Mohiuddin, S, Muvindi, H, Pabasara, U, Pisani, L, Priyadarshani, D, Pujika, D, Rashan, A, Rashan, S, Rashan, T, Sathasivam, S, Tolppa, T & Udayanga, S 2025, 'Using the Observational Medical Outcomes Partnership Common Data Model for a multi-registry intensive care unit benchmarking federated analysis: lessons learned', JAMIA Open, vol. 8, no. 4, ooaf052. https://doi.org/10.1093/jamiaopen/ooaf052

TY - JOUR

T1 - Using the Observational Medical Outcomes Partnership Common Data Model for a multi-registry intensive care unit benchmarking federated analysis

T2 - lessons learned

AU - Rashan, Aasiyah

AU - Püttmann, Daniel P.

AU - De Keizer, Nicolette F.

AU - Dongelmans, Dave A.

AU - Cornet, Ronald

AU - Ranzani, Otavio

AU - Waweru-Siika, Wangari

AU - Smith, Matthew

AU - Harris, Steve

AU - Beane, Abi

AU - Bakhshi-Raiez, Ferishta

AU - Afzali, Roya

AU - Ahmadzai, Noorullah

AU - Azizi, Mirwais

AU - Barukzai, Nasibullah

AU - Barukzay, Maryam

AU - Danish, Naqibullah

AU - Farooq, Maliha

AU - Ghalib, Maryam Shamal

AU - Ghalib, Owais Urhman

AU - Mazloomyar, Rahim

AU - Mirzada, Shoaib

AU - Negar, Meher

AU - Nadim, Bahar

AU - Rahimi, Abdul Majid

AU - Safi, Muhammad Dawood

AU - Safi, Muhammad Hamid Rahimi

AU - Saifi, Guldad Khan

AU - Shinwary, Ahmad Zakariya

AU - Dutta, Hiranmoy

AU - Ekramullah, Enshad

AU - Ghose, Aniruddha

AU - Hassanuzzaman, Md

AU - Islam, Muna

AU - Khondokar, Mahabubul Alam

AU - Rahim, Md Abdur

AU - Or Rashid, Md Harun

AU - Sattar, Md Abdus

AU - Sayeed, Abdullah Abu

AU - Shoman, Sarkar

AU - Tipu, Md Rezaul Hoque

AU - Uddin, Rabiul Alam Md Erfan

AU - Uddin, Mohammed Jashim

AU - Zahed, A. S.M.

AU - Sultan, Menbeu

AU - Amuasi, John

AU - Bonney, Joe

AU - Frimpong, Moses Siaw

AU - Hasan, Mohd Shahnaz

AU - Nor, Mohd Basri Mat

AU - Mazlan, Mohd Zulfakar

AU - Amatya, Isha

AU - Aryal, Diptesh

AU - Gauli, Basanta

AU - Giri, Praveen

AU - Khanal, Kishor

AU - Khanal, Sushil

AU - Koirala, Sabin

AU - Lakhey, Sanjay

AU - Luitel, Subekshya

AU - Paneru, Hem Raj

AU - Paudel, Sushila

AU - Rajbanshi, Lalit

AU - Ranjit, Sangina

AU - Roka, Yam

AU - Shrestha, Pramesh Sundar

AU - Shrestha, Raju

AU - Tiwari, Pradeep

AU - Waweru-Siika, Wangari

AU - Hashmi, Madiha

AU - Hanciles, Eva

AU - Pisani, Luigi

AU - Alupo, Dave Thomson, Martha

AU - Smith, Adam Hewitt

AU - Kakaire, Dennis

AU - Kiwalya, Herbert

AU - Kiwanuka, Joseph

AU - Kwizera, Arthur

AU - Muhanguzi, Joshua

AU - Sendagire, Cornelius

AU - Attanayake, Udara

AU - Beane, Abi

AU - Darshana, Sri

AU - Dondorp, Arjen M.

AU - Dullewe, Layoni

AU - Dullewe, Nilmini P.

AU - Gimhani, Kaumali

AU - Gitahi, Judy Ann

AU - Haniffa, Rashan

AU - Ishani, Pramodya

AU - Kodippily, Chamira

AU - Jawad, Issrah

AU - Mohiuddin, Shiekh

AU - Muvindi, Himasha

AU - Pabasara, Upule

AU - Pisani, Luigi

AU - Priyadarshani, Dilanthi

AU - Pujika, Disna

AU - Rashan, Aasiyah

AU - Rashan, Sumayyah

AU - Rashan, Thalha

AU - Sathasivam, Shoba

AU - Tolppa, Timo

AU - Udayanga, Shara

PY - 2025/8/1

Y1 - 2025/8/1

N2 - Objective Federated analysis is a method that allows data analysis to be performed on similar datasets without exchanging any data, thus facilitating international research collaboration while adhering to strict privacy laws. This study aimed to evaluate the feasibility of using federated analysis to benchmark mortality in 2 critical care quality registry databases converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), describing challenges to and recommendations for performing federated analysis on data transformed to OMOP CDM. Materials and Methods To identify as many challenges as possible and to be able to complete the benchmarking phase, a 2-step approach was taken during implementation. The first step was a naive implementation to allow challenges to surface naturally; the second step was developing solutions for the encountered challenges. Expected patient mortality risk was calculated by applying the Acute Physiology and Chronic Health Evaluation II (APACHE II) model to data from OMOP CDM databases containing adult ICU encounters between July 1, 2019 and December 31, 2022. An analysis script was developed to calculate comparable, registry level standardized mortality ratios. Challenges were recorded and categorized into predefined categories: "data preparation,""data analysis plan,"and "data interpretation."Challenges specific to the OMOP CDM were further categorized using published steps from an existing generic harmonization process. Results A total of 7 challenges were identified, 4 of which were related to data preparation, 1 to data analysis, and 1 to data interpretation. Out of all 7 challenges, 4 stemmed from decisions made during the implementation of OMOP CDM. Several recommended solutions were distilled from the naive approach. Discussion Federated analysis facilitated by a CDM is a feasible option for critical care quality registries. However, future analysis is influenced by decisions made during the CDM implementation process. Thus, prior publication of data dictionaries and the use of metadata to communicate data handling and data source classification during CDM implementation will improve the efficiency and accuracy of subsequent analysis.

AB - Objective Federated analysis is a method that allows data analysis to be performed on similar datasets without exchanging any data, thus facilitating international research collaboration while adhering to strict privacy laws. This study aimed to evaluate the feasibility of using federated analysis to benchmark mortality in 2 critical care quality registry databases converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), describing challenges to and recommendations for performing federated analysis on data transformed to OMOP CDM. Materials and Methods To identify as many challenges as possible and to be able to complete the benchmarking phase, a 2-step approach was taken during implementation. The first step was a naive implementation to allow challenges to surface naturally; the second step was developing solutions for the encountered challenges. Expected patient mortality risk was calculated by applying the Acute Physiology and Chronic Health Evaluation II (APACHE II) model to data from OMOP CDM databases containing adult ICU encounters between July 1, 2019 and December 31, 2022. An analysis script was developed to calculate comparable, registry level standardized mortality ratios. Challenges were recorded and categorized into predefined categories: "data preparation,""data analysis plan,"and "data interpretation."Challenges specific to the OMOP CDM were further categorized using published steps from an existing generic harmonization process. Results A total of 7 challenges were identified, 4 of which were related to data preparation, 1 to data analysis, and 1 to data interpretation. Out of all 7 challenges, 4 stemmed from decisions made during the implementation of OMOP CDM. Several recommended solutions were distilled from the naive approach. Discussion Federated analysis facilitated by a CDM is a feasible option for critical care quality registries. However, future analysis is influenced by decisions made during the CDM implementation process. Thus, prior publication of data dictionaries and the use of metadata to communicate data handling and data source classification during CDM implementation will improve the efficiency and accuracy of subsequent analysis.

KW - APACHE II

KW - OMOP CDM

KW - critical care

KW - data standardization

KW - federated analysis

KW - global health

KW - registries

UR - https://www.scopus.com/pages/publications/105011529814

U2 - 10.1093/jamiaopen/ooaf052

DO - 10.1093/jamiaopen/ooaf052

M3 - Article

AN - SCOPUS:105011529814

SN - 2574-2531

VL - 8

JO - JAMIA Open

JF - JAMIA Open

IS - 4

M1 - ooaf052

ER -

Using the Observational Medical Outcomes Partnership Common Data Model for a multi-registry intensive care unit benchmarking federated analysis: lessons learned

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