Collected Health Care Data Research Articles

Breastfeeding frequency and incidence of type 2 diabetes among women with previous gestational diabetes compared to those without: a historical cohort study in the UK

BackgroundThere is a growing body of research to suggest that women with gestational diabetes are less likely to initiate and continue breastfeeding than those who have not had however findings are mixed. There is limited research in the UK assessing the frequency of breastfeeding in women with gestational diabetes, none reporting the association of breastfeeding with incidence of type 2 diabetes and existing research has not adequately adjusted for potential confounders. This study aims to assess frequency of breastfeeding among women with gestational diabetes compared to those without, and to explore how breastfeeding influences risk of future type 2 diabetes in women with gestational diabetes while adjusting for known confounders.MethodsHistorical cohort study using routinely collected health care data from Fife and Tayside Health Boards, Scotland, UK including all women diagnosed with gestational diabetes between 1993 and 2015 and a matched comparator cohort (n = 4,968). Women with gestational diabetes were followed up until a diagnosis of type 2 diabetes, the end of the study, or date of death. Multinomial logistic regression was used to estimate odds ratios for breastfeeding for the whole sample and the association between breastfeeding and development of type 2 diabetes in women with gestational diabetes was assessed by Cox regression.ResultsWomen with a diagnosis of gestational diabetes, who were younger, overweight/obese or living in the most deprived areas were significantly less likely to exclusively breastfeed for a duration of longer than eight weeks. Risk of developing type 2 diabetes among women with gestational diabetes was significantly higher for those who exclusively breastfed less than 8 weeks, lived in the most deprived areas or had a family history of diabetes.ConclusionsThis study confirms the important role of a short duration of exclusive breastfeeding in protecting women with gestational diabetes against type 2 diabetes but highlights the challenges to breastfeeding in this group. Interventions are needed to support breastfeeding among women with gestational diabetes that are acceptable to younger, overweight/obese women living in deprived areas.

Model Complexity Reduction for ZKML Healthcare applications

Abstract Web 3.0 represents the next significant evolution of the internet that embodies the underlying decentralized network architectures, distributed ledgers, and advanced AI capabilities. Though the technologies are maturing rapidly, considerable barriers exist to high-scale adoption. The author discussed the barriers and the mitigations through specific technologies maturing to solve those issues. These include privacy-preserving technologies, off-chain and on-chain design optimizations, and the multi-dimensional approach needed in planning and adopting these technologies. As an extension, this paper discusses how one such enabler, ZKML, combines two streams of technology that are merging in unique ways to address problems in privacy and the cost of inference. The authors have conceptualized the technical and operational feasibility and implemented a reference healthcare implementation using the synthetic ICHOM in the evaluation phase in a global healthcare setting for high-volume data collection, including patient-reported outcomes. Model complexity reduction is researched and reported for the ICHOM diabetes dataset to advance the usage of ML models in global standards of healthcare data collection in network decentralized architectures for increased data protection and efficiencies.

US Real-World Effectiveness Study of Nirmatrelvir/Ritonavir in Preventing Hospitalization of High-Risk COVID-19 Patients

PurposeWe describe nirmatrelvir/ritonavir (NMV/r) effectiveness in preventing hospitalization among COVID-19 patients at high risk of severe disease. MethodsAn ongoing US population-based observational cohort study with retrospective and prospective collection of national electronic healthcare data collected from the US Optum® deidentified COVID-19 Electronic Health Record dataset during December 22, 2021−July 20, 2022. Participants were ≥12 years old; had a positive SARS-CoV-2 test, COVID-19 diagnosis, or NMV/r prescription; and were at high risk of severe COVID-19 based on demographic/clinical characteristics. Potential confounders between groups were balanced using propensity score matching. Immortal time bias was addressed. Hospitalization rates within 30 days from COVID-19 diagnosis were evaluated. Sensitivity analyses included 15-day hospitalization, chart review to investigate incidental hospitalization effects, and exclusion of patients identified as having COVID-19 based on NMV/r prescription alone. Outcomes were also evaluated by race, age, and COVID-19 vaccine status. FindingsOverall, 12,440 and 234,123 patients were included in the NMV/r and non-NMV/r groups, respectively. After propensity score matching, baseline characteristics were well balanced across groups (NMV/r, n = 12,439; non-NMV/r, n = 36,490). Incidence of hospitalization (95% CI) within 30 days was 0.90% (0.74%−1.08%) for the NMV/r group and 5.91% (5.67%−6.16%) for the non-NMV/r group, with relative risk (95% CI) of 0.15 (0.13−0.18; 85% risk reduction). NMV/r was comparably effective in Black patients (relative risk, 0.19 [0.10−0.34]; 81% risk reduction). Sensitivity analyses supported the main outcomes. ImplicationsReal-world NMV/r effectiveness against hospitalization during Omicron predominance among COVID-19 patients at high risk of severe disease supports demonstrated clinical trial efficacy. Black patients underutilized NMV/r despite high effectiveness.

The Association of Adverse Pregnancy Outcomes With Overactive Bladder Anticholinergics.

The pregnancy safety and teratogenicity of overactive bladder (OAB) anticholinergic medications in humans are unknown. The aim of this study was to determine if the use of OAB anticholinergics during pregnancy was associated with adverse pregnancy outcomes or congenital malformation. Using routinely collected health care data from Ontario, Canada, we conducted a retrospective cohort study and identified women aged 18-45 years who gave birth between 2004 and 2022 and were eligible for provincial drug benefits. We used gestational age and birth dates to approximate conception dates. The primary exposure was filling a prescription for an OAB anticholinergic during pregnancy (compared with nonusers). The coprimary outcomes were pregnancy complication (which included preterm birth or low birth weight) and congenital malformation. Poisson regression models with generalized estimating equations and inverse probability of treatment weighting were used to estimate risk ratios (RRs). We identified 138,271 births, of which 479 (0.3%) had exposure to an OAB anticholinergic, for a median of 60 (interquartile range, 30-120) days. The most common OAB anticholinergic was oxybutynin (84%). In the weighted cohort, the risk of a pregnancy complication was significantly elevated (RR, 1.65; 95% confidence interval [CI], 1.40-1.95; P < 0.01; absolute risk difference + 9.9% [95% CI, +5.9, +13.9]), with a dose-response relationship close to statistical significance (P = 0.07). The risk of congenital malformations with OAB anticholinergic use was not significant (RR, 1.24; 95% CI, 0.85-1.80; P = 0.26). Pregnant women who take OAB anticholinergic medications have a higher risk of pregnancy complications, but not congenital malformations, compared with non-OAB anticholinergic users. This information can be used when counseling women of child-bearing age about these medications.

Smartphone applications are used for self-management, telerehabilitation, evaluation and data collection in low back pain healthcare: a scoping review

Background Smartphone use has grown in providing healthcare for patients with low back pain (LBP), but the literature lacks an analysis of the use of smartphone apps. This scoping review aimed to identify current areas of smartphone apps use for managing LBP. We also aimed to evaluate the current status of the effectiveness or scientific validity of such use and determine perspectives for their potential development. Methods We searched PubMed, PEDro and Embase for articles published in English up to May 3rd, 2021 that investigated smartphone use for LBP healthcare and their purpose. All types of study design were accepted. Studies concerning telemedicine or telerehabilitation but without use of a smartphone were not included. The same search strategy was performed by two researchers independently and a third researcher validated the synthesis of the included studies. Results We included 43 articles: randomised controlled trials (RCTs) (n=12), study protocols (n=6), reliability/validity studies (n=6), systematic reviews (n=7), cohort studies (n=4), qualitative studies (n=6), and case series (n=1). The purposes of the smartphone app were for 1) evaluation, 2) telerehabilitation, 3) self-management, and 4) data collection. Self-management was the most-studied use, showing promising results derived from moderate- to good-quality RCTs for patients with chronic LBP and patients after spinal surgery. Promising results exist regarding evaluation and data collection use and contradictory results regarding measurement use. Conclusions This scoping review revealed a notable interest in the scientific literatures regarding the use of smartphone apps for LBP patients. The identified purposes point to current scientific status and perspectives for further studies including RCTs and systematic reviews targeting specific usage.

Data Checks Before Registering Study Protocols for Health Care Database Analyses

This Viewpoint discusses the challenges involved with secondary health care data collection vs primary data collection and provides a list of suggested data checks before registration of a study protocol using secondary data.

Enabling pandemic‐resilient healthcare: Narrowband Internet of Things and edge intelligence for real‐time monitoring

AbstractThe Internet of Things (IoT) in deploying robotic sprayers for pandemic‐associated disinfection and monitoring has garnered significant attention in recent research. The authors introduce a novel architectural framework designed to interconnect smart monitoring robotic devices within healthcare facilities using narrowband Internet of Things (NB‐IoT) technology. The core objective is establishing a seamless data transmission pipeline that bridges these devices with the nearest base station. The associated data is routed directly from the edge computing infrastructure to the Cloud storage. The proposed architecture increases data security and optimises system performance by avoiding dependencies on central data centres. The critical components of this system allow the edge node to transmit data to the cloud, where primary data processing and analysis occur. The selection of NB‐IoT as the underlying protocol facilitates real‐time data collection and monitoring in healthcare with boosted capacity and coverage, extended battery life, and cost efficiency.

How flash mobs can be used for data collection in healthcare? A scoping review protocol.

Traditional research methods often involve a lengthy process, but the emergence of flash mobs as an innovative data collection method offers the potential to gather substantial data within a short time frame. Flash mobs draw inspiration from the concept of large groups organizing through the internet or mobile devices to perform a prearranged action in public. In healthcare research, flash mobs serve as research organizing method to investigate clinically relevant questions on a large scale within a limited period. This study aims to present a study protocol for a scoping review that comprehensively maps the existing literature on the use of flash mobs as a data collection method in healthcare research. The review will follow established guidelines and include steps such as identifying the research question, identifying relevant studies, selecting studies, charting the data, and collating and summarizing the results. The review will utilize databases, manual screening of additional sources, and covidence for study selection and data charting. The findings will be summarized using descriptive statistics and a descriptive synthesis of qualitative data. The review protocol has been registered with the Open Science Framework. The results of this scoping review will provide insights into different flash mob designs, motivations, and the data collection process, contributing to the development of high-quality flash mob data collections in healthcare research.

5G NB-IoT System Integrated with High-Performance Fiber Sensor Inspired by Cirrus and Spider Structures.

Real-time telemedicine detection can solve the problem of the shortage of public medical resources caused by the coming aging society. However, the development of such an integrated monitoring system is hampered by the need for high-performance sensors and the strict-requirement of long-distance signal transmission and reproduction. Here, a bionic crack-spring fiber sensor (CSFS) inspired by spider leg and cirrus whiskers for stretchable and weavable electronics is reported. Trans-scale conductive percolation networks of multilayer graphene around the surface of outer spring-like Polyethylene terephthalate (PET) fibers and printing Ag enable a high sensitivity of 28475.6 and broad sensing range over 250%. The electromechanical changes in different stretching stages are simulated by Comsol to explain the response mechanism. The CSFS is incorporated into the fabric and realized the human-machine interactions (HMIs) for robot control. Furthermore, the 5G Narrowband Internet of Things (NB-IoT) system is developed for human healthcare data collection, transmission, and reproduction together with the integration of the CSFS, illustrating the huge potential of the approach in human-machine communication interfaces and intelligent telemedicine rehabilitation and diagnosis monitoring.

Speech for Intelligent cognition change tracking and DEtection of AD (SIDE‐AD) Research Program: SIDE‐AD

AbstractBackgroundThere is emerging evidence that speech could be a potential indicator and manifestation of early Alzheimer’s disease (AD) pathology. Symptomatic AD may change an individual’s language, especially certain elements in speech such as elaboration and attribution. Therefore, the University of Edinburgh and Sony Group Corporation are collaborating within the Sony Research Award Program to create the SIDE‐AD study which aims to develop digital speech‐based biomarkers technology for AD. The study is designed to assess disease status in the pre‐dementia stages in a real‐world at‐risk population in Brain Health Services.MethodSIDE‐AD is an observational longitudinal study developing speech biomarkers for use in neurodegenerative disease. Participants are recruited from Brain Health Services in Scotland with data collection starting in spring 2023. In the context of the SIDE‐AD study, we have developed a secured online data collection infrastructure for collecting voice data by prompting individuals to record a voice sample talking about their brain health. Individuals are asked to rate their mood, anxiety and apathy. The baseline speech biomarkers will be compared to the follow‐up visit’s speech recordings as well as to other variables from routinely collected health care data. The SIDE‐AD study will employ signal processing and machine learning technologies to automate the assessment of the respondents’ speech.ResultThe objective of the SIDE‐AD study is to recruit participants (n = 150) across the AD spectrum. The developed infrastructure will be used to collect data of participants with an objective of analysing baseline speech markers in relation to self‐reported mood, anxiety and apathy as well as clinical outcomes from patients’ health care records.ConclusionThe SIDE‐AD study is carrying out research and development of a speech‐based application recording data on individuals’ self‐perceived brain health. A secured online data collection infrastructure is developed to collect speech data with an objective to analyse speech data in combination with routinely available clinical data in Brain Health Clinics, enabling real world validation of speech‐based digital biomarkers for the early detection of AD. We plan to use automatic speech recognition and acoustic patterns for the automatic assessment of brain health status relevant to emergent AD‐specific speech biomarkers.

Long-term physical and psychiatric morbidities and mortality of untreated, deferred, and immediately treated epilepsy.

In Australia, 30% of newly diagnosed epilepsy patients were not immediately treated at diagnosis. We explored health outcomes between patients receiving immediate, deferred, or no treatment, and compared them to the general population. Adults with newly diagnosed epilepsy in Western Australia between 1999 and 2016 were linked with statewide health care data collections. Health care utilization, comorbidity, and mortality at up to 10 years postdiagnosis were compared between patients receiving immediate, deferred, and no treatment, as well as with age- and sex-matched population controls. Of 603 epilepsy patients (61% male, median age = 40 years) were included, 422 (70%) were treated immediately at diagnosis, 110 (18%) received deferred treatment, and 71 (12%) were untreated at the end of follow-up (median = 6.8 years). Immediately treated patients had a higher 10-year rate of all-cause admissions or emergency department presentations than the untreated (incidence rate ratio [IRR] = 2.0, 95% confidence interval [CI] = 1.4-2.9) and deferred treatment groups (IRR = 1.7, 95% CI = 1.0-2.8). They had similar 10-year risks of mortality and developing new physical and psychiatric comorbidities compared with the deferred and untreated groups. Compared to population controls, epilepsy patients had higher 10-year mortality (hazard ratio = 2.6, 95% CI = 2.1-3.3), hospital admissions (IRR = 2.3, 95% CI = 1.6-3.3), and psychiatric outpatient visits (IRR = 3.2, 95% CI = 1.6-6.3). Patients with epilepsy were also 2.5 (95% CI = 2.1-3.1) and 3.9 (95% CI = 2.6-5.8) times more likely to develop a new physical and psychiatric comorbidity, respectively. Newly diagnosed epilepsy patients with deferred or no treatment did not have worse outcomes than those immediately treated. Instead, immediately treated patients had greater health care utilization, likely reflecting more severe underlying epilepsy etiology. Our findings emphasize the importance of individualizing epilepsy treatment and recognition and management of the significant comorbidities, particularly psychiatric, that ensue following a diagnosis of epilepsy.

Healthcare Data Collection Using Internet of Things and Blockchain Based Decentralized Data Storage

With the increase in usage of Internet of Things devices (IoT), IoT is used in different sectors such as manufacturing, electric vehicles, home automation and healthcare. The IoT devices collected large volumes of data on different parameters at regular intervals. Storing a massive amount volume of IoT data securely is a complicated task. Presently, the majority of IoT devices use cloud storage to store the data, however, cloud servers require large storage and high computation. Due to third party cloud service provider (CSP) interaction, the management of IoT data security fully depends on the CSP. To manage these problems, a decentralized blockchain based secure storage is proposed in this work. In the proposed scheme, instead of CSP storage location, the patient health information is stored in the blockchain technique and the blockchain miners verify the transactions with the help of Elliptic Curve Cryptography (ECC). The miner verification process dynamically avoids adversary access. Similarly, the certificateless access is used in the proposed system to avoid certificate based issues. The blocks in the blockchain is going to be stored patient details in a decentralized storage location to avoid unauthorized access and ensure the authenticity of data. The use of blockchain eliminates the need for third party public auditing process through immutable storage. This work illustrates secure communication and immutable data storage without the intervention of CSP. The communication overhead reduced by nearly 10 to 40% and authentication improved by 10 to 20% while confidentiality increased by 5% in comparison to existing techniques. Through this technique, data confidentiality, integrity and availability is ensured.

Retracted: Evaluation Algorithm for Team Strength Based on the Collected Healthcare Data through IoT and Smart Devices.

[This retracts the article DOI: 10.1155/2022/4969527.].

Revolutionizing Healthcare through Health Monitoring Applications with Wearable Biomedical Devices

The Internet of Things (IoT) has revolutionized the connectivity and communication of tangible objects, and it serves as a versatile and cost-effective solution in the healthcare sector, particularly in regions with limited healthcare infrastructure. This research explores the application of sensors such as LM35, AD8232, and MAX30100 for the detection of vital health indicators, including body temperature, pulse rate, electrocardiogram (ECG), and oxygen saturation levels, with data transmission through IoT cloud, offering real-time parameter access via an Android application for non-invasive remote patient monitoring. The study aims to expand healthcare services to various settings, such as hospitals, commercial areas, educational institutions, workplaces, and residential neighborhoods. After the COVID-19 pandemic, IoT-enabled continuous monitoring of critical health metrics such as temperature and pulse rate has become increasingly crucial for early illness detection and efficient communication with healthcare providers. Our low-cost wearable device, which includes ECG monitoring, aims to bridge the accessibility gap for people with limited financial resources, with the primary goal of providing efficient healthcare solutions to underserved rural areas while also contributing valuable data to future medical research. Our proposed system is a low-cost, high-efficiency solution that outperforms existing systems in healthcare data collection and patient monitoring. It improves access to vital health data and shows economic benefits, indicating a significant advancement in healthcare technology.

Cancer victims’ attitudes towards the importance of supportive treatment and health-care

The Smart Self-Care Unit (SSCU), a fundamental component of this system, enables remote health care data collection from patients being treated or observed at homes. Patients who had been treated for cancer several years prior completed primary data to evaluate psychosocial outcomes such as supportive patient needs, emotional stress, and quality of life (QoL). While anxiety levels have increased, the QoL and depression ratings are comparable to community rates. Almost two survivors reported having at least one unmet need, the majority of which related to fundamental survivorship difficulties, underscoring survivors’ distinct requirements. The number of years since a person was diagnosed has no bearing on their level of need. Almost the three times as several unfulfilled requirements were reported by the victims who were clinically worried, and over two and a half times more were reported by victims who were depressed. There were numerous reports of \hfilneg

Study on myocardial infarction based on nationwide inpatient sample database: a bibliometric analysis from 2000 to 2022

Background Studies on myocardial infarction (MI) based on large medical databases have become popular in recent years. The influence of the National Inpatient Sample (NIS), the largest collection of administrative healthcare data across the United States, on the field of MI has not been well investigated. This study aimed to quantify the contribution of NIS to MI research using bibliometric methods. Methods We searched the Web of Science Core Collection database to identify publications on MI using NIS from 2000 to 2022. Bibliometric indicators, such as the number of publications, citations, and Hirsch index (H-index), were summarized by years, authors, organizations, and journals. VOSviewer and CiteSpace software were used to analyze the keywords and trends of the hot spots. Results A total of 342 articles on MI based on NIS were included. A significant growth in outputs related to MI using the NIS from 2000 to 2020 was observed. The publications were mainly from the United States. The Mayo Clinic was the most prolific institution and had the most citations and the highest H-index. The American Journal of Cardiology ranked first among journals with the highest number of publications, citations, and H-index. Mortality and healthcare management are the main focuses of this field. Personalized risks and care are receiving increased attention. Conclusion This study suggests that NIS significantly contributes to high-quality output in MI research. More efforts are needed to improve the impact of knowledge gained from the NIS on MI.

Securing health care data through blockchain enabled collaborative machine learning.

Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.

Prolonged opioid use after distal radius fracture.

Prolonged opioid use (more than 90 days) after injury puts the patient at risk for adverse effects. We investigated the patterns of opioid prescription after distal radius fracture and the effect of pre- and post-fracture factors on the risk for prolonged use. This register-based cohort study uses routinely collected health care data, including purchases of prescription opioids, in the county of Skåne, Sweden. 9369 adult patients with a radius fracture diagnosed 2015-2018 were followed for 1 year after fracture. We calculated proportions of patients with prolonged opioid use, both in total and according to different exposures. Using modified Poisson regression, we calculated adjusted risk ratios for the following exposures: previous opioid use, mental illness, consultation for pain, surgery for distal radius fracture and occupational/physical therapy after fracture. Prolonged opioid use (4-6 months after fracture) was found in 664 (7.1%) of the patients. A previous, but discontinued, regular use of opioids up to 5 years before fracture increased the risk compared to opioid-naïve patients. Both regular and non-regular opioid use the year before fracture increased the risk. The risk was also higher for patients with mental illness, and those who were treated with surgery, we found no significant effect of pain consultation in previous year. Occupational/physical therapy lowered the risk for prolonged use. Considering history of mental illness and previous opioid use while promoting rehabilitation can be important to prevent prolonged opioid use after distal radius fracture. We show that a common injury such as distal radius fracture can be a gateway to prolonged opioid use, especially among patients with previous history of opioid use or mental illness. Importantly, previous opioid use as far back as 5 years earlier greatly increases the risk of regular use after the reintroduction of opioids. Considering past use is important when planning treatment with opioids. Occupational or physical therapy after injury is associated with lower risk of prolonged use and should be encouraged.

An anonymization-based privacy-preserving data collection protocol for digital health data.

Digital health data collection is vital for healthcare and medical research. But it contains sensitive information about patients, which makes it challenging. To collect health data without privacy breaches, it must be secured between the data owner and the collector. Existing data collection research studies have too stringent assumptions such as using a third-party anonymizer or a private channel amid the data owner and the collector. These studies are more susceptible to privacy attacks due to third-party involvement, which makes them less applicable for privacy-preserving healthcare data collection. This article proposes a novel privacy-preserving data collection protocol that anonymizes healthcare data without using a third-party anonymizer or a private channel for data transmission. A clustering-based k-anonymity model was adopted to efficiently prevent identity disclosure attacks, and the communication between the data owner and the collector is restricted to some elected representatives of each equivalent group of data owners. We also identified a privacy attack, known as "leader collusion", in which the elected representatives may collaborate to violate an individual's privacy. We propose solutions for such collisions and sensitive attribute protection. A greedy heuristic method is devised to efficiently handle the data owners who join or depart the anonymization process dynamically. Furthermore, we present the potential privacy attacks on the proposed protocol and theoretical analysis. Extensive experiments are conducted in real-world datasets, and the results suggest that our solution outperforms the state-of-the-art techniques in terms of privacy protection and computational complexity.

Impact of decompensated cirrhosis in children: A population-based study.

We describe the proportion of children with compensated cirrhosis who develop decompensation in Ontario, Canada over the past two decades. This is a retrospective population-based cohort study using routinely collected health care data from Ontario, Canada held at ICES during 1997-2017. Diagnosis of cirrhosis was made using validated ICES definition, and decompensation events were defined according to validated coding. Rates of decompensation, type of decompensation, and incidence of liver transplantation after decompensation were analyzed. Databases were linked at the individual level and analyzed at ICES-Queen's. A total of 2,755 children with compensated cirrhosis were included and 9% (253) developed decompensation over a median follow-up of 7 years. Children most likely to suffer decompensation were younger (median age 10 versus 4 years, p < 0.001) and female (45% versus 52%, p = 0.03). Ascites (137/253, 54%) was the most frequent complication. 199/2755 (7%) of children with cirrhosis received liver transplantation, of which 64% (128/199) occurred after a decompensation event. Overall, a total of 132 (4.7%) deaths occurred during the study period, with 55 deaths following a decompensating event. We present the first study to describe rates of decompensation, type, and rate of liver transplantation after decompensation in pediatric cirrhosis at the population level. To improve the care of children with liver disease, early detection of liver disease, early initiation of specific treatments as well as identification of children who are at risk of becoming decompensated are crucial.