Community-acquired pneumonia in people with Human Immunodeficiency Virus (HIV) during the current era of effective antiretroviral therapy: A multi-center retrospective cohort study.

BACKGROUND: It is unclear if Human Immunodeficiency Virus (HIV) infection affects the prognosis for community acquired pneumonia (CAP) in the current era of effective anti-retroviral therapy. In this multi-center retrospective cohort study of patients admitted for CAP, we compared the in-hospital mortality rate between people with HIV (PWH) and people without HIV. METHODS: The study included consecutive patients admitted with a diagnosis of CAP across 31 hospitals in Ontario, Canada from 2015 to 2022. HIV infection was based on discharge diagnoses and anti-retroviral prescription. The primary outcome was in-hospital mortality. Competing risk models were used to describe time to death in hospital or discharge. Potential confounders were balanced using overlap weighting of propensity scores. RESULTS: Of 82,822 patients admitted with CAP, 1,518 (1.8%) patients had a diagnosis of HIV. PWH were more likely to be younger, be male and have less comorbidities. In hospital, 67 (4.4%) PWH and 6,873 (8.5%) people without HIV died. HIV status had an adjusted sub-distribution hazard ratio (sHR) of 1.02 (95% CI 0.80-1.31 P=0.8440) for dying in hospital. Of 1,518 PWH, 440 (29.0%) patients had a diagnosis of acquired immunodeficiency syndrome (AIDS). AIDS diagnosis had an adjusted sHR of 3.04 (95% CI 1.69-5.45 P=0.0002) for dying in hospital compared to HIV without AIDS. CONCLUSION: People with and without HIV admitted for CAP had a similar in-hospital mortality rate. For PWH, AIDS significantly increased the mortality risk. HIV infection by itself without AIDS should not be considered a poor prognostic factor for CAP.

Characterizing the burden of biliary tract cancers across 28 hospitals in Ontario, Canada.

BACKGROUND AND AIMS: The incidence of biliary tract cancers (BTC) appears to be increasing worldwide. We analyzed the characteristics of BTC-related hospitalizations under medical services across 28 hospitals in Ontario, Canada. METHODS: This study uses data collected by GEMINI, a hospital research data network. BTC-related hospitalizations from 2015 to 2021 under the Department of Medicine or intensive care unit were captured using the International Classification of Diseases, 10th revision, codes for intrahepatic cholangiocarcinoma (iCCA), extrahepatic cholangiocarcinoma, and gallbladder cancers. RESULTS: A total of 4596 BTC-related hospitalizations (2720 iCCA, 1269 extrahepatic cholangiocarcinoma, 607 gallbladder cancers) were analyzed. The number of unique patients with BTC-related hospitalizations increased over time. For iCCA-related hospitalizations, the total number of hospitalizations increased (from 385 in 2016 to 420 in 2021, p = .005), the hospital length of stay decreased over the study period (mean 10 days [SD, 12] in 2016 to 9 days [SD, 8] in 2021, p = .04), and the number of in-hospital deaths was stable (from 68 [18%] in 2016 to 55 [13%] in 2021, p = .62). Other outcomes such as 30-day readmissions, medical imaging tests, intensive care unit-specific hospitalizations, and length of stay were stable over time for all cohorts. The cost of hospitalization for the BTC cohort increased from median $8203 CAD (interquartile range, 5063-15,543) in 2017 to $8507 CAD (interquartile range, 5345-14,755) in 2021. CONCLUSIONS: This real-world data analysis showed a rising number of patients with BTC-related hospitalizations and rising number of iCCA-related hospitalizations across 28 hospitals in Ontario between 2015 and 2021.

Advanced imaging use and delays among inpatients with psychiatric comorbidity.

OBJECTIVE: To determine whether presence of a psychiatric comorbidity impacts use of inpatient imaging tests and subsequent wait times. METHODS: This was a retrospective cohort study of all patients admitted to General Internal Medicine (GIM) at five academic hospitals in Toronto, Ontario from 2010 to 2019. Exposure was presence of a coded psychiatric comorbidity on admission. Primary outcome was time to test, as calculated from the time of test ordering to time of test completion, for computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, or peripherally inserted central catheter (PICC) insertion. Multilevel mixed-effects models were used to identify predictors of time to test, and marginal effects were used to calculate differences in absolute units (h). Secondary outcome was the rate of each type of test included. Subgroup analyses were performed according to type of psychiatric comorbidity: psychotic, mood/anxiety, or substance use disorder. RESULTS: There were 196,819 GIM admissions from 2010to 2019. In 77,562 admissions, ≥1 advanced imaging test was performed. After adjusting for all covariates, presence of any psychiatric comorbidity was associated with increased time to test for MRI (adjusted difference: 5.3 h, 95% confidence interval [CI]: 3.9-6.8), PICC (adjusted difference: 3.7 h, 95% CI: 1.6-5.8), and ultrasound (adjusted difference: 3.0 h, 95% CI: 2.3-3.8), but not for CT (adjusted difference: 0.1 h, 95% CI: -0.3 to 0.5). Presence of any psychiatric comorbidity was associated with lower rate of ordering for all test types (adjusted difference: -17.2 tests per 100 days hospitalization, interquartile range: -18.0 to -16.3). CONCLUSIONS: There was a lower rate of ordering of advanced imaging among patients with psychiatric comorbidity. Once ordered, time to test completion was longer for MRI, ultrasound, and PICC. Further exploration, such as quantifying rates of cancelled tests and qualitative studies evaluating hospital, provider, and patient barriers to timely advanced imaging, will be helpful in elucidating causes for these disparities.

Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data.

BACKGROUND: Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions or jurisdictions is challenging because of complex and varying privacy and regulatory requirements. Hence, it is hard but crucial to allow multiple parties to collaboratively train an ML model leveraging the private datasets available at each party without the need for direct sharing of those datasets or compromising the privacy of the datasets through collaboration. METHODS: In this paper, we address this challenge by proposing Decentralized, Collaborative, and Privacy-preserving ML for Multi-Hospital Data (DeCaPH). This framework offers the following key benefits: (1) it allows different parties to collaboratively train an ML model without transferring their private datasets (i.e., no data centralization); (2) it safeguards patients' privacy by limiting the potential privacy leakage arising from any contents shared across the parties during the training process; and (3) it facilitates the ML model training without relying on a centralized party/server. FINDINGS: We demonstrate the generalizability and power of DeCaPH on three distinct tasks using real-world distributed medical datasets: patient mortality prediction using electronic health records, cell-type classification using single-cell human genomes, and pathology identification using chest radiology images. The ML models trained with DeCaPH framework have less than 3.2% drop in model performance comparing to those trained by the non-privacy-preserving collaborative framework. Meanwhile, the average vulnerability to privacy attacks of the models trained with DeCaPH decreased by up to 16%. In addition, models trained with our DeCaPH framework achieve better performance than those models trained solely with the private datasets from individual parties without collaboration and those trained with the previous privacy-preserving collaborative training framework under the same privacy guarantee by up to 70% and 18.2% respectively. INTERPRETATION: We demonstrate that the ML models trained with DeCaPH framework have an improved utility-privacy trade-off, showing DeCaPH enables the models to have good performance while preserving the privacy of the training data points. In addition, the ML models trained with DeCaPH framework in general outperform those trained solely with the private datasets from individual parties, showing that DeCaPH enhances the model generalizability. FUNDING: This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2020-06189 and DGECR-2020-00294), Canadian Institute for Advanced Research (CIFAR) AI Catalyst Grants, CIFAR AI Chair programs, Temerty Professor of AI Research and Education in Medicine, University of Toronto, Amazon, Apple, DARPA through the GARD project, Intel, Meta, the Ontario Early Researcher Award, and the Sloan Foundation. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute.

Anaerobic Antibiotic Coverage in Aspiration Pneumonia and the Associated Benefits and Harms: A Retrospective Cohort Study.

BACKGROUND: Antibiotics with extended anaerobic coverage are used commonly to treat aspiration pneumonia, which is not recommended by current guidelines. RESEARCH QUESTION: In patients admitted to hospital for community-acquired aspiration pneumonia, does a difference exist between antibiotic therapy with limited anaerobic coverage (LAC) vs antibiotic therapy with extended anaerobic coverage (EAC) in terms of in-hospital mortality and risk of Clostridioides difficile colitis? STUDY DESIGN AND METHODS: We conducted a multicenter retrospective cohort study across 18 hospitals in Ontario, Canada, from January 1, 2015, to January 1, 2022. Patients were included if the physician diagnosed aspiration pneumonia and prescribed guideline-concordant first-line community-acquired pneumonia parenteral antibiotic therapy to the patient within 48 h of admission. Patients then were categorized into the LAC group if they received ceftriaxone, cefotaxime, or levofloxacin. Patients were categorized into the EAC group if they received amoxicillin-clavulanate, moxifloxacin, or any of ceftriaxone, cefotaxime, or levofloxacin in combination with clindamycin or metronidazole. The primary outcome was all-cause in-hospital mortality. Secondary outcomes included incident C difficile colitis occurring after admission. Overlap weighting of propensity scores was used to balance baseline prognostic factors. RESULTS: The LAC and EAC groups included 2,683 and 1,316 patients, respectively. In hospital, 814 patients (30.3%) and 422 patients (32.1%) in the LAC and EAC groups died, respectively. C difficile colitis occurred in five or fewer patients (≤ 0.2%) and 11 to 15 patients (0.8%-1.1%) in the LAC and EAC groups, respectively. After overlap weighting of propensity scores, the adjusted risk difference of EAC minus LAC was 1.6% (95% CI, -1.7% to 4.9%) for in-hospital mortality and 1.0% (95% CI, 0.3%-1.7%) for C difficile colitis. INTERPRETATION: We found that extended anaerobic coverage likely is unnecessary in aspiration pneumonia because it was associated with no additional mortality benefit, only an increased risk of C difficile colitis.

Association between new insertion of a long-term enteral feeding tube and mortality in adults admitted to the hospital with aspiration: A retrospective cohort study.

BACKGROUND: We aimed to describe the association between insertion of a new long-term enteral feeding tube during admission for aspiration and in-hospital mortality. METHODS: This retrospective cohort study across 28 Canadian hospitals from 2015 to 2022 included consecutive patients who were admitted for aspiration. Patients were categorized based on new long-term enteral feeding tube insertion during hospital stay or not. The primary outcome was the time to death in hospital. Secondary outcomes included time to discharge alive and hospital readmission for aspiration within 90 days. We used propensity score weighting to balance covariates, and a competing risk model to describe in-hospital death and discharge. RESULTS: Of 12,850 patients admitted for aspiration, 852 (6.6%) patients received a long-term enteral feeding tube. In the hospital, 184 (21.6%) and 2489 (20.8%) patients in the enteral feeding tube group and no enteral feeding tube group died, respectively. Within 90 days of discharge, 127 (14.9%) and 1148 (9.6%) patients in the enteral feeding tube and no enteral feeding tube group were readmitted for aspiration, respectively. After balancing covariates, an enteral feeding tube was associated with a similar in-hospital mortality risk (subdistribution hazard ratio [sHR] = 1.05, 95% CI = 0.89-1.23; P = 0.5800), longer time to discharge alive (sHR = 0.58, 95% CI = 0.54-0.63; P < 0.0001), and a higher risk of readmission (risk difference = 5.0%, 95% CI = 2.4%-7.6%; P = 0.0001). CONCLUSION: Initiation of long-term enteral tube feeding was not uncommon after admission for aspiration and was not associated with an improvement in the probability of being discharged alive from the hospital or readmitted for aspiration.

Real-world use of glucocorticoids and clinical outcomes in adults hospitalized with community-acquired pneumonia on medical wards.

BACKGROUND: Little is known about the real-world use of systemic glucocorticoids to treat patients hospitalized with community-acquired pneumonia (CAP) outside of the intensive care unit (ICU). METHODS: This retrospective cohort study included 11,588 hospitalizations for CAP without chronic pulmonary disease at seven hospitals in Ontario, Canada. We report physician-level variation in the use of glucocorticoids and trends over time. We investigated the association between glucocorticoid prescriptions and clinical outcomes, using propensity score overlap weighting to account for confounding by indication. RESULTS: Glucocorticoids were prescribed in 1283 (11.1%) patients, increasing over time from 10.0% in 2010 to 11.9% in 2020 (p = .008). Physician glucocorticoid prescribing ranged from 2.9% to 34.6% (median 10.0%, inter quartile range [IQR]: 6.7%-14.6%). Patients receiving glucocorticoids tended to be younger (median age 73 vs. 79), have higher Charlson comorbidity scores (score of 2 or more: 42.4% vs. 31.0%), more cancer (26.6% vs. 13.2%), more renal disease (11.5% vs. 6.6%), and less dementia (7.8% vs. 14.8%). Patients treated with glucocorticoids had higher rates of in-hospital mortality (weighted Risk Difference = 1.72, 95% confidence interval [95% CI]: 0.16-3.3, p = .033). Glucocorticoid use was not associated with ICU admission, hospital length-of-stay, or 30-day readmission. CONCLUSION: Glucocorticoids were prescribed in 11.1% of patients hospitalized with CAP outside of ICU and one in four physicians prescribed glucocorticoids in more than 14% of patients. Glucocorticoid use was associated with greater in-hospital mortality, although these findings are limited by large selection effects. Clinicians should exercise caution in prescribing glucocorticoids for nonsevere CAP, and definitive trials are needed in this population.

Association of sodium glucose co-transporter-2 inhibitors with risk of diabetic ketoacidosis among hospitalized patients: A multicentre cohort study.

INTRODUCTION: Sodium glucose co-transporter-2 inhibitors (SGLT-2i) are increasingly being used among hospitalized patients. Our objective was to assess the risk of diabetic ketoacidosis (DKA) among hospitalized patients receiving an SGLT-2i. RESEARCH DESIGN AND METHODS: We conducted a multicentre cohort study of patients hospitalized at 19 hospitals. We included patients over 18 years of age who received an SGLT-2i or a dipeptidyl peptidase-4 inhibitor (DPP-4i) in hospital. The primary outcome was the risk of DKA during their hospitalization. RESULTS: 61,517 patients received a DPP-4i and 11,061 received an SGLT-2i. The risk of inpatient DKA was 0.07 % (N = 41 events) among adults who received a DPP-4i and 0.18 % (N = 20 events) among adults who received an SGLT-2i; adjusted odds ratio of 3.30 (95 % CI: 1.85-5.72). CONCLUSIONS: In hospitalized patients, the absolute risk of DKA was 0.2 %, which corresponded to a three-fold higher relative risk.

A simple electronic medical record-based predictors of illness severity in sepsis (sepsis) score.

OBJECTIVE: Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort. DESIGN: A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study. SETTING: Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010-March 2015 (primary cohort) and 2015-2019 (secondary cohort). PATIENTS: We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures. MEASUREMENTS AND MAIN RESULTS: The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015-2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality. CONCLUSIONS: An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications.

Validation of the Diagnostic Accuracy Levels of International Classification of Diseases, 10th Revision Codes for Diabetic Ketoacidosis: A Multicentre, Cross-sectional Study of Adults.

OBJECTIVES: International Classification of Diseases (ICD) codes are commonly used to identify cases of diabetic ketoacidosis (DKA) in health services research, but they have not been validated. Our aim in this study was to assess the accuracy of ICD, 10th revision (ICD-10) diagnosis codes for DKA. METHODS: We conducted a multicentre, cross-sectional study using data from 5 hospitals in Ontario, Canada. Each hospitalization event has a single most responsible diagnosis code. We identified all hospitalizations assigned diagnosis codes for DKA. A true case of DKA was defined using laboratory values (serum bicarbonate ≤18 mmol/L, arterial pH ≤7.3, anion gap ≥14 mEq/L, and presence of ketones in urine or blood). Chart review was conducted to validate DKA if laboratory values were missing or the diagnosis of DKA was unclear. Outcome measures included positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity of ICD-10 codes in patients with laboratory-defined DKA. RESULTS: We identified 316,517 hospitalizations. Among these, 312,948 did not have an ICD-10 diagnosis code for DKA and 3,569 had an ICD-10 diagnosis code for DKA. Using a combination of laboratory and chart review, we identified that the overall PPV was 67.0%, the NPV was 99.7%, specificity was 99.6%, and sensitivity was 74.9%. When we restricted our analysis to hospitalizations in which DKA was the most responsible discharge diagnosis (n=3,374 [94.5%]), the test characteristics were PPV 69.8%, NPV 99.7%, specificity 99.7%, and sensitivity 71.9%. CONCLUSION: ICD-10 codes can identify patients with DKA among those admitted to general internal medicine.

Characterizing medical patients with delirium: A cohort study comparing ICD-10 codes and a validated chart review method.

BACKGROUND: Delirium is a major cause of preventable mortality and morbidity in hospitalized adults, but accurately determining rates of delirium remains a challenge. OBJECTIVE: To characterize and compare medical inpatients identified as having delirium using two common methods, administrative data and retrospective chart review. METHODS: We conducted a retrospective study of 3881 randomly selected internal medicine hospital admissions from six acute care hospitals in Toronto and Mississauga, Ontario, Canada. Delirium status was determined using ICD-10-CA codes from hospital administrative data and through a previously validated chart review method. Baseline sociodemographic and clinical characteristics, processes of care and outcomes were compared across those without delirium in hospital and those with delirium as determined by administrative data and chart review. RESULTS: Delirium was identified in 6.3% of admissions by ICD-10-CA codes compared to 25.7% by chart review. Using chart review as the reference standard, ICD-10-CA codes for delirium had sensitivity 24.1% (95%CI: 21.5-26.8%), specificity 99.8% (95%CI: 99.5-99.9%), positive predictive value 97.6% (95%CI: 94.6-98.9%), and negative predictive value 79.2% (95%CI: 78.6-79.7%). Age over 80, male gender, and Charlson comorbidity index greater than 2 were associated with misclassification of delirium. Inpatient mortality and median costs of care were greater in patients determined to have delirium by ICD-10-CA codes (5.8% greater mortality, 95% CI: 2.0-9.5 and $6824 greater cost, 95%CI: 4713-9264) and by chart review (11.9% greater mortality, 95%CI: 9.5-14.2% and $4967 greater cost, 95%CI: 4415-5701), compared to patients without delirium. CONCLUSIONS: Administrative data are specific but highly insensitive, missing most cases of delirium in hospital. Mortality and costs of care were greater for both the delirium cases that were detected and missed by administrative data. Better methods of routinely measuring delirium in hospital are needed.

Development and external validation of tools for categorizing diagnosis codes in international hospital data.

BACKGROUND: The Clinical Classification Software Refined (CCSR) is a tool that groups many thousands of International Classification of Diseases 10th Revision (ICD-10) diagnosis codes into approximately 500 clinically meaningful categories, simplifying analyses. However, CCSR was developed for use in the United States and may not work well with other country-specific ICD-10 coding systems. METHOD: We developed an algorithm for semi-automated matching of Canadian ICD-10 codes (ICD-10-CA) to CCSR categories using discharge diagnoses from adult admissions at 7 hospitals between Apr 1, 2010 and Dec 31, 2020, and manually validated the results. We then externally validated our approach using inpatient hospital encounters in Denmark from 2017 to 2018. KEY RESULTS: There were 383,972 Canadian hospital admissions with 5,186 distinct ICD-10-CA diagnosis codes and 1,855,837 Danish encounters with 4,612 ICD-10 diagnosis codes. Only 46.6% of Canadian codes and 49.4% of Danish codes could be directly categorized using the official CCSR tool. Our algorithm facilitated the mapping of 98.5% of all Canadian codes and 97.7% of Danish codes. Validation of our algorithm by clinicians demonstrated excellent accuracy (97.1% and 97.0% in Canadian and Danish data, respectively). Without our algorithm, many common conditions did not match directly to a CCSR category, such as 96.6% of hospital admissions for heart failure. CONCLUSION: The GEMINI CCSR matching algorithm (available as an open-source package at https://github.com/GEMINI-Medicine/gemini-ccsr) improves the categorization of Canadian and Danish ICD-10 codes into clinically coherent categories compared to the original CCSR tool. We expect this approach to generalize well to other countries and enable a wide range of research and quality measurement applications.

Osmotic Demyelination Syndrome in Patients Hospitalized with Hyponatremia.

BACKGROUND: Osmotic demyelination syndrome (ODS) is a rare but potentially devastating neurologic complication of hyponatremia. The primary objective of this study was to identify the proportion of patients who developed ODS in a large, contemporary, multicenter cohort of patients admitted to the hospital with hyponatremia. METHODS: We conducted a multicenter cohort study of patients admitted with hyponatremia at five academic hospitals in Toronto, Ontario, Canada, between April 1, 2010, and December 31, 2020. All adult patients presenting with hyponatremia (serum sodium level 8 mmol/l in any 24-hour period). RESULTS: Our cohort included 22,858 hospitalizations with hyponatremia. Approximately 50% were women, the average age was 68 years, and mean initial serum sodium was 125 mmol/l (standard deviation, 4.6), including 11.9% with serum sodium from 110 to 119 mmol/l and 1.2% with serum sodium less than 110 mmol/l. Overall, rapid correction of serum sodium occurred in 3632 (17.7%) admissions. Twelve patients developed ODS (0.05%). Seven (58%) patients who developed ODS did not have rapid correction of serum sodium. CONCLUSIONS: In this large multicenter study of patients with hyponatremia, rapid correction of serum sodium was common (n=3632 [17.7%]), but ODS was rare (n=12 [0.05%]). Future studies with a higher number of patients with ODS are needed to better understand potential causal factors for ODS.

Essential requirements for the governance and management of data trusts, data repositories, and other data collaborations.

Introduction: Around the world, many organisations are working on ways to increase the use, sharing, and reuse of person-level data for research, evaluation, planning, and innovation while ensuring that data are secure and privacy is protected. As a contribution to broader efforts to improve data governance and management, in 2020 members of our team published 12 minimum specification essential requirements (min specs) to provide practical guidance for organisations establishing or operating data trusts and other forms of data infrastructure. Approach and Aims: We convened an international team, consisting mostly of participants from Canada and the United States of America, to test and refine the original 12 min specs. Twenty-three (23) data-focused organisations and initiatives recorded the various ways they address the min specs. Sub-teams analysed the results, used the findings to make improvements to the min specs, and identified materials to support organisations/initiatives in addressing the min specs. Results: Analyses and discussion led to an updated set of 15 min specs covering five categories: one min spec for Legal, five for Governance, four for Management, two for Data Users, and three for Stakeholder & Public Engagement. Multiple changes were made to make the min specs language more technically complete and precise. The updated set of 15 min specs has been integrated into a Canadian national standard that, to our knowledge, is the first to include requirements for public engagement and Indigenous Data Sovereignty. Conclusions: The testing and refinement of the min specs led to significant additions and improvements. The min specs helped the 23 organisations/initiatives involved in this project communicate and compare how they achieve responsible and trustworthy data governance and management. By extension, the min specs, and the Canadian national standard based on them, are likely to be useful for other data-focused organisations and initiatives.