Pulmonary arterial hypertension treatment: an individual participant data network meta-analysis.

BACKGROUND AND AIMS: Effective therapies that target three main signalling pathways are approved to treat pulmonary arterial hypertension (PAH). However, there are few large patient-level studies that compare the effectiveness of these pathways. The aim of this analysis was to compare the effectiveness of the treatment pathways in PAH and to assess treatment heterogeneity. METHODS: A network meta-analysis was performed using individual participant data of 6811 PAH patients from 20 Phase III randomized clinical trials of therapy for PAH that were submitted to the US Food and Drug Administration. Individual drugs were grouped by the following treatment pathways: endothelin, nitric oxide, and prostacyclin pathways. RESULTS: The mean (±standard deviation) age of the sample was 49.2 (±15.4) years; 78.4% were female, 59.7% had idiopathic PAH, and 36.5% were on background PAH therapy. After covariate adjustment, targeting the endothelin + nitric oxide pathway {ß: 43.7 m [95% confidence interval (CI): 32.9, 54.4]}, nitric oxide pathway [ß: 29.4 m (95% CI: 22.6, 36.3)], endothelin pathway [ß: 25.3 m (95% CI: 19.8, 30.8)], and prostacyclin pathway [oral/inhaled ß: 19.1 m (95% CI: 14.2, 24.0), intravenous/subcutaneous ß: 24.4 m (95% CI: 15.1, 33.7)] significantly increased 6 min walk distance at 12 or 16 weeks compared with placebo. Treatments also significantly reduced the likelihood of having clinical worsening events. There was significant heterogeneity of treatment effects by age, body mass index, hypertension, diabetes, and coronary artery disease. CONCLUSIONS: Drugs targeting the three traditional treatment pathways significantly improve outcomes in PAH, with significant treatment heterogeneity in patients with some comorbidities. Randomized clinical trials are warranted to identify the most effective treatment strategies in a personalized approach.

Electrocardiographic Abnormalities and Their Association with Outcomes in Randomized Clinical Trials of Pulmonary Arterial Hypertension.

Rationale PAH is a progressive disease with manifestations including right atrial enlargement, right ventricular dysfunction, dilation and hypertrophy. ECG is a non-invasive, inexpensive test that is routinely performed in clinical settings. Prior studies have described separate abnormal findings in ECGs of patients with PAH. However, the role of composite ECG findings reflective of right heart disease for risk stratification, clinical trial enrichment and management of patients with PAH has not been explored. Objectives i. Describe a pattern of right heart disease on ECG in patients with PAH. ii. Investigate the association of this pattern with clinical measures of disease severity and outcomes. Methods We harmonized individual participant data from 18 Phase-III randomized clinical trials of therapies for PAH (1998 - 2013) submitted to the FDA. Right heart disease (RHD) was defined as the presence of RV hypertrophy, right axis deviation, right atrial enlargement, or right bundle branch block on ECG. Random effects linear regression, multilevel ordinal regression (cumulative link model), and Cox proportional hazards models were used to assess the association of RHD by ECG with six-minute walk distance (6MWD), WHO functional class, and clinical worsening after a priori adjustment for age, sex, body mass index and PAH etiology. Effect modification of treatment and ECG abnormalities was assessed by including an interaction term. Results 4439 patients had baseline ECGs and 68% patients had evidence of RHD. RHD on ECG was associated with higher PVR (p<0.001) and higher mean PA pressures (p<0.001). Patients with RHD on ECG had 10 meters shorter 6MWD (p=0.005) and worse WHO functional class (p<0.001) at baseline. RHD on baseline ECG was associated with increased risk of clinical worsening (HR=1.42, 95%CI=1.21,1.67, p<0.001). Patients with RHD had greater treatment effect in terms of 6MWD, WHO-FC and time to clinical worsening compared to those without (p for interaction= 0.03, 0.001 and 0.03, respectively). Conclusion Right heart disease by ECG may be associated with a worse outcomes and potentially greater treatment effect. ECGs could be an inexpensive, widely available noninvasive method to enrich clinical trial populations in PAH.

Parent and Youth Athlete Perceptions of Concussion Injury: Establishing a Factor Structure.

OBJECTIVE: The first objective was to establish the respective factor structures of a concussion perceptions inventory that was adapted for youth athletes (ages 8-14 years) and their parents from the Perceptions of Concussion Inventory for Athletes. The second objective was to understand the associations between the concussion perceptions of youth athlete-parent dyads. METHOD: In this cross-sectional study, 329 parent-youth athlete dyads completed a respective concussion perception inventory. Mean age of youth respondents was 10.9 ± 1.8 years (70.1% male) and mean age of parent respondents was 40.5 ± 13.6 years (60.9% female). RESULTS: Exploratory factor analyses revealed unique 7-factor structures for both the youth athlete and parent inventories (youth athlete: anxiety, clarity, treatment, permanent injury, symptom variability, long-term outcomes, and personal control; parent: anxiety, clarity, treatment, permanent injury, symptom variability, and long-term outcomes, and affect others). Weak associations were found between dyads on the 5 factors that were composed of identical items (anxiety, clarity, treatment, permanent injury, and symptom variability). CONCLUSIONS: Findings suggest that this adapted inventory has adequate psychometric properties to be used in the study of the concussion perceptions of youth athletes and their parents. Weak correlations across the concussion perceptions in the dyads suggest that parents and children hold different concussion perceptions and this should be considered in instrument selection of future studies.

Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study.

Background: Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning. Methods: We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PASC sub-phenotypes. Findings: Our framework characterized seven PASC sub-phenotypes. We estimated that on average 15.7% of the hospitalized COVID-19 patients were likely to suffer from at least one PASC symptom and almost 5.98%, on average, had multiple symptoms. Joint pain and dyspnea had the highest prevalence, with an average prevalence of 5.45% and 4.53%, respectively. Interpretation: We provided a scalable framework to every participating healthcare system for estimating PASC sub-phenotypes prevalence and temporal attributes, thus developing a unified model that characterizes augmented sub-phenotypes across the different systems. Funding: Authors are supported by National Institute of Allergy and Infectious Diseases, National Institute on Aging, National Center for Advancing Translational Sciences, National Medical Research Council, National Institute of Neurological Disorders and Stroke, European Union, National Institutes of Health, National Center for Advancing Translational Sciences.

A retrospective cohort analysis leveraging augmented intelligence to characterize long COVID in the electronic health record: A precision medicine framework.

Physical and psychological symptoms lasting months following an acute COVID-19 infection are now recognized as post-acute sequelae of COVID-19 (PASC). Accurate tools for identifying such patients could enhance screening capabilities for the recruitment for clinical trials, improve the reliability of disease estimates, and allow for more accurate downstream cohort analysis. In this retrospective cohort study, we analyzed the EHR of hospitalized COVID-19 patients across three healthcare systems to develop a pipeline for better identifying patients with persistent PASC symptoms (dyspnea, fatigue, or joint pain) after their SARS-CoV-2 infection. We implemented distributed representation learning powered by the Machine Learning for modeling Health Outcomes (MLHO) to identify novel EHR features that could suggest PASC symptoms outside of typical diagnosis codes. MLHO applies an entropy-based feature selection and boosting algorithms for representation mining. These improved definitions were then used for estimating PASC among hospitalized patients. 30,422 hospitalized patients were diagnosed with COVID-19 across three healthcare systems between March 13, 2020 and February 28, 2021. The mean age of the population was 62.3 years (SD, 21.0 years) and 15,124 (49.7%) were female. We implemented the distributed representation learning technique to augment PASC definitions. These definitions were found to have positive predictive values of 0.73, 0.74, and 0.91 for dyspnea, fatigue, and joint pain, respectively. We estimated that 25 percent (CI 95%: 6-48), 11 percent (CI 95%: 6-15), and 13 percent (CI 95%: 8-17) of hospitalized COVID-19 patients will have dyspnea, fatigue, and joint pain, respectively, 3 months or longer after a COVID-19 diagnosis. We present a validated framework for screening and identifying patients with PASC in the EHR and then use the tool to estimate its prevalence among hospitalized COVID-19 patients.

The IHI Rochester Report 2022 on Healthcare Informatics Research: Resuming After the CoViD-19.

In 2020, the CoViD-19 pandemic spread worldwide in an unexpected way and suddenly modified many life issues, including social habits, social relationships, teaching modalities, and more. Such changes were also observable in many different healthcare and medical contexts. Moreover, the CoViD-19 pandemic acted as a stress test for many research endeavors, and revealed some limitations, especially in contexts where research results had an immediate impact on the social and healthcare habits of millions of people. As a result, the research community is called to perform a deep analysis of the steps already taken, and to re-think steps for the near and far future to capitalize on the lessons learned due to the pandemic. In this direction, on June 09th-11th, 2022, a group of twelve healthcare informatics researchers met in Rochester, MN, USA. This meeting was initiated by the Institute for Healthcare Informatics-IHI, and hosted by the Mayo Clinic. The goal of the meeting was to discuss and propose a research agenda for biomedical and health informatics for the next decade, in light of the changes and the lessons learned from the CoViD-19 pandemic. This article reports the main topics discussed and the conclusions reached. The intended readers of this paper, besides the biomedical and health informatics research community, are all those stakeholders in academia, industry, and government, who could benefit from the new research findings in biomedical and health informatics research. Indeed, research directions and social and policy implications are the main focus of the research agenda we propose, according to three levels: the care of individuals, the healthcare system view, and the population view.

Heterogeneity of treatment effects by risk in pulmonary arterial hypertension.

BACKGROUND: It is currently unknown if disease severity modifies response to therapy in pulmonary arterial hypertension (PAH). We aimed to explore if disease severity, as defined by established risk-prediction algorithms, modified response to therapy in randomised clinical trials in PAH. METHODS: We performed a meta-analysis using individual participant data from 18 randomised clinical trials of therapy for PAH submitted to the United States Food and Drug Administration to determine if predicted risk of 1-year mortality at randomisation modified the treatment effect on three outcomes: change in 6-min walk distance (6MWD), clinical worsening at 12 weeks and time to clinical worsening. RESULTS: Of 6561 patients with a baseline US Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL 2.0) score, we found that individuals with higher baseline risk had higher probabilities of clinical worsening but no difference in change in 6MWD. We detected a significant interaction of REVEAL 2.0 risk and treatment assignment on change in 6MWD. For every 3-point increase in REVEAL 2.0 score, there was a 12.49 m (95% CI 5.86-19.12 m; p=0.001) greater treatment effect in change in 6MWD. We did not detect a significant risk by treatment interaction on clinical worsening with most of the risk-prediction algorithms. CONCLUSIONS: We found that predicted risk of 1-year mortality in PAH modified treatment effect as measured by 6MWD, but not clinical worsening. Our findings highlight the importance of identifying sources of treatment heterogeneity by predicted risk to tailor studies to patients most likely to have the greatest treatment response.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

BACKGROUND: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients. METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern. RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors. CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.

Minimal Clinically Important Difference in the 6-minute-walk Distance for Patients with Pulmonary Arterial Hypertension.

Rationale: The 6-minute-walk distance (6MWD) is an important clinical and research metric in pulmonary arterial hypertension (PAH); however, there is no consensus about what minimal change in 6MWD is clinically significant. Objectives: We aimed to determine the minimal clinically important difference in the 6MWD. Methods: We performed a meta-analysis using individual participant data from eight randomized clinical trials of therapy for PAH submitted to the U.S. Food and Drug Administration to derive minimal clinically important differences in the 6MWD. The estimates were externally validated using the Pulmonary Hypertension Association Registry. We anchored the change in 6MWD to the change in the Medical Outcomes Survey Short Form physical component score. Measurements and Main Results: The derivation (clinical trial) and validation (Pulmonary Hypertension Association Registry) samples were comprised of 2,404 and 537 adult patients with PAH, respectively. The mean ± standard deviation age of the derivation sample was 50.5 ± 15.2 years, and 1,849 (77%) were female, similar to the validation sample. The minimal clinically important difference in the derivation sample was 33 meters (95% confidence interval, 27-38), which was almost identical to that in the validation sample (36 m [95% confidence interval, 29-43]). The minimal clinically important difference did not differ by age, sex, race, pulmonary hypertension etiology, body mass index, use of background therapy, or World Health Organization functional class. Conclusions: We estimated a 6MWD minimal clinically important difference of approximately 33 meters for adults with PAH. Our findings can be applied to the design of clinical trials of therapies for PAH.

Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study.

Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings: Advanced age (HR 2.77, 95%CI 2.53-3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI. Interpretation: COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. Funding: Authors are supported by various funders, with full details stated in the acknowledgement section.

Preoperative Depression Status and 5 Year Metabolic and Bariatric Surgery Outcomes in the PCORnet Bariatric Study Cohort.

OBJECTIVE: To examine whether depression status before metabolic and bariatric surgery (MBS) influenced 5-year weight loss, diabetes, and safety/utilization outcomes in the PCORnet Bariatric Study. SUMMARY OF BACKGROUND DATA: Research on the impact of depression on MBS outcomes is inconsistent with few large, long-term studies. METHODS: Data were extracted from 23 health systems on 36,871 patients who underwent sleeve gastrectomy (SG; n=16,158) or gastric bypass (RYGB; n=20,713) from 2005-2015. Patients with and without a depression diagnosis in the year before MBS were evaluated for % total weight loss (%TWL), diabetes outcomes, and postsurgical safety/utilization (reoperations, revisions, endoscopy, hospitalizations, mortality) at 1, 3, and 5 years after MBS. RESULTS: 27.1% of SG and 33.0% of RYGB patients had preoperative depression, and they had more medical and psychiatric comorbidities than those without depression. At 5 years of follow-up, those with depression, versus those without depression, had slightly less %TWL after RYGB, but not after SG (between group difference = 0.42%TWL, P = 0.04). However, patients with depression had slightly larger HbA1c improvements after RYGB but not after SG (between group difference = - 0.19, P = 0.04). Baseline depression did not moderate diabetes remission or relapse, reoperations, revision, or mortality across operations; however, baseline depression did moderate the risk of endoscopy and repeat hospitalization across RYGB versus SG. CONCLUSIONS: Patients with depression undergoing RYGB and SG had similar weight loss, diabetes, and safety/utilization outcomes to those without depression. The effects of depression were clinically small compared to the choice of operation.

Baseline Sex Differences in Pulmonary Arterial Hypertension Randomized Clinical Trials.

Rationale: Sex-based differences in pulmonary arterial hypertension (PAH) are known, but the contribution to disease measures is understudied. Objectives: We examined whether sex was associated with baseline 6-minute-walk distance (6MWD), hemodynamics, and functional class. Methods: We conducted a secondary analysis of participant-level data from randomized clinical trials of investigational PAH therapies conducted between 1998 and 2014 and provided by the U.S. Food and Drug Administration. Outcomes were modeled as a function of an interaction between sex and age or sex and body mass index (BMI), respectively, with generalized mixed modeling. Results: We included a total of 6,633 participants from 18 randomized clinical trials. A total of 5,197 (78%) were female, with a mean age of 49.1 years and a mean BMI of 27.0 kg/m2. Among 1,436 males, the mean age was 49.7 years, and the mean BMI was 26.4 kg/m2. The most common etiology of PAH was idiopathic. Females had shorter 6MWD. For every 1 kg/m2 increase in BMI for females, 6MWD decreased 2.3 (1.6-3.0) meters (P < 0.001), whereas 6MWD did not significantly change with BMI in males (0.31 m [-0.30 to 0.92]; P = 0.32). Females had lower right atrial pressure (RAP) and mean pulmonary artery pressure, and higher cardiac index than males (all P < 0.03). Age significantly modified the sex by RAP and mean pulmonary artery pressure relationships. For every 10-year increase in age, RAP was lower in males (0.5 mm Hg [0.3-0.7]; P < 0.001), but not in females (0.13 [-0.03 to 0.28]; P = 0.10). There was a significant decrease in pulmonary vascular resistance (PVR) with increasing age regardless of sex (P < 0.001). For every 1 kg/m2 increase in BMI, there was a 3% decrease in PVR for males (P < 0.001), compared with a 2% decrease in PVR in females (P < 0.001). Conclusions: Sexual dimorphism in subjects enrolled in clinical trials extends to 6MWD and hemodynamics; these relationships are modified by age and BMI. Sex, age, and body size should be considered in the evaluation and interpretation of surrogate outcomes in PAH.

Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.

Electronic health records (EHRs) contain a wealth of information that can be used to further precision health. One particular data element in EHRs that is not only under-utilized but oftentimes unaccounted for is missing data. However, missingness can provide valuable information about comorbidities and best practices for monitoring patients, which could save lives and reduce burden on the healthcare system. We characterize patterns of missing data in laboratory measurements collected at the University of Pennsylvania Hospital System from long-term COVID-19 patients and focus on the changes in these patterns between 2020 and 2021. We investigate how these patterns are associated with comorbidities such as acute respiratory distress syndrome (ARDS), and 90-day mortality in ARDS patients. This work displays how knowledge and experience can change the way clinicians and hospitals manage a novel disease. It can also provide insight into best practices when it comes to patient monitoring to improve outcomes.

Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.

Electronic health records (EHRs) contain a wealth of information that can be used to further precision health. One particular data element in EHRs that is not only under-utilized but oftentimes unaccounted for is missing data. However, missingness can provide valuable information about comorbidities and best practices for monitoring patients, which could save lives and reduce burden on the healthcare system. We characterize patterns of missing data in laboratory measurements collected at the University of Pennsylvania Hospital System from long-term COVID-19 patients and focus on the changes in these patterns between 2020 and 2021. We investigate how these patterns are associated with comorbidities such as acute respiratory distress syndrome (ARDS), and 90-day mortality in ARDS patients. This work displays how knowledge and experience can change the way clinicians and hospitals manage a novel disease. It can also provide insight into best practices when it comes to patient monitoring to improve outcomes.

Special Section on Inclusive Digital Health: Notable Papers on Addressing Bias, Equity, and Literacy to Strengthen Health Systems.

OBJECTIVE: To summarize significant research contributions on addressing bias, equity, and literacy in health delivery systems published in 2021. METHODS: An extensive search using PubMed and Scopus was conducted to identify peer-reviewed articles published in 2021 that examined ways that informatics methods, approaches, and tools could address bias, equity, and literacy in health systems and care delivery processes. The selection process comprised three steps: (1) 15 candidate best papers were first selected by the two section editors; (2) external reviewers from internationally renowned research teams reviewed each candidate best paper; and (3) the final selection of three best papers was conducted by the editorial committee of the Yearbook. RESULTS: Selected best papers represent studies that characterized significant challenges facing biomedical informatics with respect to equity and practices that support equity and literacy in the design of health information systems. Selected papers represent the full spectrum of this year's yearbook theme. In general, papers identified in the search fell into one of the following categories: (1) descriptive accounts of algorithmic bias in medical software or machine learning approaches; (2) enabling health information systems to appropriately encode for gender identity and sex; (3) approaches to support health literacy among individuals who interact with information systems and mobile applications; and (4) approaches to engage diverse populations in the use of health information systems and the biomedical informatics workforce CONCLUSIONS: : Although the selected papers are notable, our collective efforts as a biomedical informatics community to address equity, literacy, and bias remain nascent. More work is needed to ensure health information systems are just in their use of advanced computing approaches and all persons have equal access to health care and informatics tools.

A manifesto on explainability for artificial intelligence in medicine.

The rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, output to users. This concern is especially legitimate in biomedical contexts, where patient safety is of paramount importance. This position paper brings together seven researchers working in the field with different roles and perspectives, to explore in depth the concept of explainable AI, or XAI, offering a functional definition and conceptual framework or model that can be used when considering XAI. This is followed by a series of desiderata for attaining explainability in AI, each of which touches upon a key domain in biomedicine.

SurvMaximin: Robust federated approach to transporting survival risk prediction models.

OBJECTIVE: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

International electronic health record-derived post-acute sequelae profiles of COVID-19 patients.

The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09-1.55), heart failure (RR 1.22, 95% CI 1.10-1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07-1.31), and fatigue (RR 1.18, 95% CI 1.07-1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58-2.76), venous embolism (RR 1.34, 95% CI 1.17-1.54), atrial fibrillation (RR 1.30, 95% CI 1.13-1.50), type 2 diabetes (RR 1.26, 95% CI 1.16-1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09-1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90-3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21-2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04-1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality.

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.