Survival Analysis with Electronic Health Record Data: Experiments with Chronic Kidney Disease.
Ontology highlight
ABSTRACT:
SUBMITTER: Hagar Y
PROVIDER: S-EPMC8112603 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundAs adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling.ObjectiveThe authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation.MethodsThe study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic.ResultsA risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001).ConclusionsA risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration.
| S-EPMC4482276 | biostudies-literature
Project description:Recent evidence has shown that fine particulate matter (PM2.5) may be an important environmental risk factor for chronic kidney disease (CKD), but few studies have examined this association for individual patients using fine spatial data. To investigate the association between PM2.5 and CKD (estimated glomerular filtration rate [eGFR]<45 ml/min/1.73 m2) in the Twin-Cities area in Minnesota using a large electronic health care database (2012-2019). We estimated the previous 1-year average PM2.5 from the first eGFR (measured with the CKD Epidemiology Collaboration equation using the first available creatinine measure during the baseline period [2012-2014]) using Environmental Protection Agency downscaler modeling data at the census tract level. We evaluated the spatial relative risk and clustering of CKD prevalence using a K-function test statistic. We assessed the prevalence ratio of the PM2.5 association with CKD incidence using a mixed effect Cox model, respectively. Patients (n = 20,289) in the fourth (PM2.5 > 10.4), third (10.3 < PM2.5 < 10.8) and second quartile (9.9 < PM2.5 < 10.3) vs. the first quartile (<9.9 μg/m3) had a 2.52[2.21, 2.87], 2.18[1.95, 2.45], and 1.72[1.52, 1.97] hazard rate of developing CKD in the fully adjusted models, respectively. We identified spatial heterogeneities and evidence of CKD clustering across our study region, but this spatial variation was accounted for by air pollution and individual covariates. Exposure to higher PM2.5 is associated with a greater risk for incident CKD. Improvements in air quality, specifically at hotspots, may reduce CKD.
| S-EPMC8202050 | biostudies-literature
Project description:BACKGROUND:Electronic health record (EHR) based chronic kidney disease (CKD) registries are central to population health strategies to improve CKD care. In 2015, Partners Healthcare System (PHS), encompassing multiple academic and community hospitals and outpatient care facilities in Massachusetts, developed an EHR-based CKD registry to identify opportunities for quality improvement, defined as improvement on both process measures and outcomes measures associated with clinical care. METHODS:Patients are included in the registry based on the following criteria: 1) two estimated glomerular filtration rate (eGFR) results < 60 ml/min/1.73m2 separated by 90 days, including the most recent eGFR being < 60 ml/min/1.73m2; or 2) the most recent two urine protein values > 300 mg protein/g creatinine on either urine total protein/creatinine ratio or urine albumin/creatinine ratio; or 3) an EHR problem list diagnosis of end stage renal disease (ESRD). The registry categorizes patients by CKD stage and includes rates of annual testing for eGFR and proteinuria, blood pressure control, use of angiotensin converting enzyme inhibitors (ACE-Is) or angiotensin receptor blockers (ARBs), nephrotoxic medication use, hepatitis B virus (HBV) immunization, vascular access placement, transplant status, CKD progression risk; number of outpatient nephrology visits, and hospitalizations. RESULTS:The CKD registry includes 60,503 patients and has revealed several opportunities for care improvement including 1) annual proteinuria testing performed for 17% (stage 3) and 31% (stage 4) of patients; 2) ACE-I/ARB used in 41% (stage 3) and 46% (stage 4) of patients; 3) nephrotoxic medications used among 23% of stage 4 patients; and 4) 89% of stage 4 patients lack HBV immunity. For advanced CKD patients there are opportunities to improve vascular access placement, transplant referrals and outpatient nephrology contact. CONCLUSIONS:A CKD registry can identify modifiable care gaps across the spectrum of CKD care and enable population health strategy implementation. No linkage to Social Security Death Master File or US Renal Data System (USRDS) databases limits our ability to track mortality and progression to ESRD.
| S-EPMC6397481 | biostudies-literature
Project description:ImportanceLarge gaps in clinical care in patients with chronic kidney disease (CKD) lead to poor outcomes.ObjectiveTo compare the effectiveness of an electronic health record-based population health management intervention vs usual care for reducing CKD progression and improving evidence-based care in high-risk CKD.Design, setting, and participantsThe Kidney Coordinated Health Management Partnership (Kidney CHAMP) was a pragmatic cluster randomized clinical trial conducted between May 2019 and July 2022 in 101 primary care practices in Western Pennsylvania. It included patients aged 18 to 85 years with an estimated glomerular filtration rate (eGFR) of less than 60 mL/min/1.73m2 with high risk of CKD progression and no outpatient nephrology encounter within the previous 12 months.InterventionsMultifaceted intervention for CKD comanagement with primary care clinicians included a nephrology electronic consultation, pharmacist-led medication management, and CKD education for patients. The usual care group received CKD care from primary care clinicians as usual.Main outcomes and measuresThe primary outcome was time to 40% or greater reduction in eGFR or end-stage kidney disease.ResultsAmong 1596 patients (754 intervention [47.2%]; 842 control [52.8%]) with a mean (SD) age of 74 (9) years, 928 (58%) were female, 127 (8%) were Black, 9 (0.6%) were Hispanic, and the mean (SD) estimated glomerular filtration rate was 36.8 (7.9) mL/min/1.73m2. Over a median follow-up of 17.0 months, there was no significant difference in rate of primary outcome between the 2 arms (adjusted hazard ratio, 0.96; 95% CI, 0.67-1.38; P = .82). Angiotensin-converting enzyme inhibitor/angiotensin receptor blocker exposure was more frequent in intervention arm compared with the control group (rate ratio, 1.21; 95% CI, 1.02-1.43). There was no difference in the secondary outcomes of hypertension control and exposure to unsafe medications or adverse events between the arms. Several COVID-19-related issues contributed to null findings in the study.Conclusion and relevanceIn this study, among patients with moderate-risk to high-risk CKD, a multifaceted electronic health record-based population health management intervention resulted in more exposure days to angiotensin-converting enzyme inhibitors/angiotensin receptor blockers but did not reduce risk of CKD progression or hypertension control vs usual care.Trial registrationClinicalTrials.gov Identifier: NCT03832595.
| S-EPMC11019443 | biostudies-literature
Project description:INTRODUCTION:Chronic kidney disease (CKD) is an important public health problem worldwide. However, there are few active disease surveillance systems for it. The China Kidney Disease Network (CK-NET) was established as a comprehensive surveillance system for CKD using various data sources. As part of this, the proposed CK-NET-Yinzhou study aims to build a regional surveillance system in a developed coastal area in China to obtain detailed dynamic information about kidney disease and to improve the ability to manage the disease effectively. METHODS AND ANALYSIS:Yinzhou is a district of Ningbo city, Zhejiang province. The district has a population of more than 1?million. By 2016, 98% were registered in a regional health information system that started in 2009. This system includes administrative databases containing general demographic characteristics, health check information, inpatient and outpatient electronic medical records, health insurance information, disease surveillance and management information, and death certificates. We will use longitudinal individual electronic health record data to identify people with CKD by repeated laboratory measurements and diagnostic codes. We will also evaluate the associated risk factors, prognosis and disease management. An intelligent clinical decision support system (CDSS) will be developed based on clinical guidelines, domain expert knowledge and real-world data, and will be integrated into the hospital information system. ETHICS AND DISSEMINATION:The CK-NET-Yinzhou study has been reviewed and approved by the Peking University First Hospital Ethics Committee. Privacy of local residents registered with the health information system will be tightly protected through the study process. The findings of the study will be disseminated through peer-reviewed journal articles, posters and presentations in national and international scientific conferences, as well as among local practitioners through the CDSS.
| S-EPMC6719833 | biostudies-literature
Project description:Chronic kidney disease-mineral bone disorder (CKD-MBD) is the most common complication in CKD patients. Although there is a consensus on treatment guidelines for CKD-MBD, it remains uncertain whether these treatment recommendations reflect actual practice. Therefore, the aim of this study was to investigate the CKD-MBD medication trend in real-world practice. This was a retrospective and observational study using a 12-year period database transformed into a common data model from three tertiary university hospitals. Study populations were subjects initially diagnosed as CKD. The date of diagnosis was designated as the index date. New patients were categorized year to year from 2008 to 2019 with a fixed observation period of 365 days to check the prescription of CKD-MBD medications including calcium-containing phosphate binder, noncalcium-containing phosphate binder, aluminium hydroxide, vitamin D receptor activator (VDRA), and cinacalcet. The numbers of CKD patients in the three hospitals were 7555, 2424, and 5351, respectively. The proportion for patients with CKD-MBD medication prescription decreased yearly regardless of hospital and CKD stage (p for trend < 0.05). The use of aluminium hydroxide disappeared steadily while the use of VDRA increased annually in all settings. Despite these changes in prescription patterns, the mean value for CKD-MBD-related serologic markers was almost within target range. The proportion of the population within the target value was not significantly changed. Irrespective of hospital and CKD stage, similar trends of prescription for CKD-MBD medications were observed in real-world practice. Further research with a distributed network study may be helpful to understand medication trends in CKD-MBD treatment.
| S-EPMC8629641 | biostudies-literature
Project description:The Japan Chronic Kidney Disease (CKD) Database (J-CKD-DB) is a large-scale, nation-wide registry based on electronic health record (EHR) data from participating university hospitals. Using a standardized exchangeable information storage, the J-CKD-DB succeeded to efficiently collect clinical data of CKD patients across hospitals despite their different EHR systems. CKD was defined as dipstick proteinuria ≥1+ and/or estimated glomerular filtration rate <60 mL/min/1.73 m2 base on both out- and inpatient laboratory data. As an initial analysis, we analyzed 39,121 CKD outpatients (median age was 71 years, 54.7% were men, median eGFR was 51.3 mL/min/1.73 m2) and observed that the number of patients with a CKD stage G1, G2, G3a, G3b, G4 and G5 were 1,001 (2.6%), 2,612 (6.7%), 23,333 (59.6%), 8,357 (21.4%), 2,710 (6.9%) and 1,108 (2.8%), respectively. According to the KDIGO risk classification, there were 30.1% and 25.5% of male and female patients with CKD at very high-risk, respectively. As the information from every clinical encounter from those participating hospitals will be continuously updated with an anonymized patient ID, the J-CKD-DB will be a dynamic registry of Japanese CKD patients by expanding and linking with other existing databases and a platform for a number of cross-sectional and prospective analyses to answer important clinical questions in CKD care.
| S-EPMC7192920 | biostudies-literature
Project description:BackgroundElectronic health record (EHR) data is increasingly used to identify patients with chronic kidney disease (CKD). EHR queries used to capture CKD status, identify comorbid conditions, measure awareness by providers, and track adherence to guideline-concordant processes of care have not been validated.MethodsWe extracted EHR data for primary-care patients with two eGFRcreat 15-59 mL/min/1.73 m^2 at least 90 days apart. Two nephrologists manually reviewed a random sample of 50 charts to determine CKD status, associated comorbidities, and physician awareness of CKD. We also assessed the documentation of a CKD diagnosis with guideline-driven care.ResultsComplete data were available on 1767 patients with query-defined CKD of whom 822 (47%) had a CKD diagnosis in their chart. Manual chart review confirmed the CKD diagnosis in 34 or 50 (68%) patients. Agreement between the reviewers and the EHR diagnoses on the presence of comorbidities was good (κ > 0.70, p < 0.05), except for congestive heart failure, (κ = 0.45, p < 0.05). Reviewers felt the providers were aware of CKD in 23 of 34 (68%) of the confirmed CKD cases. A CKD diagnosis was associated with higher odds of guideline-driven care including CKD-specific laboratory tests and prescriptions for statins. After adjustment, CKD diagnosis documentation was not significantly associated with ACE/ARB prescription.ConclusionsIdentifying CKD status by historical eGFRs overestimates disease prevalence. A CKD diagnosis in the patient chart was a reasonable surrogate for provider awareness of disease status, but CKD awareness remains relatively low. CKD in the patient chart was associated with higher rates of albuminuria testing and use of statins, but not use of ACE/ARB.
| S-EPMC6318865 | biostudies-literature
Project description:PURPOSE:To validate an algorithm for acute exacerbations of chronic obstructive pulmonary disease (AECOPD) episodes derived in an electronic health record (EHR) database, against AECOPD episodes collected in a randomized clinical trial using an electronic case report form (eCRF). METHODS:We analyzed two data sources from the Salford Lung Study in COPD: trial eCRF and the Salford Integrated Record, a linked primary-secondary routine care EHR database of all patients in Salford. For trial participants, AECOPD episodes reported in eCRF were compared with algorithmically derived moderate/severe AECOPD episodes identified in EHR. Episode characteristics (frequency, duration), sensitivity, and positive predictive value (PPV) were calculated. A match between eCRF and EHR episodes was defined as at least 1-day overlap. RESULTS:In the primary effectiveness analysis population (n = 2269), 3791 EHR episodes (mean [SD] length: 15.1 [3.59] days; range: 14-54) and 4403 moderate/severe AECOPD eCRF episodes (mean length: 13.8 [16.20] days; range: 1-372) were identified. eCRF episodes exceeding 28 days were usually broken up into shorter episodes in the EHR. Sensitivity was 63.6% and PPV 71.1%, where concordance was defined as at least 1-day overlap. CONCLUSIONS:The EHR algorithm performance was acceptable, indicating that EHR-derived AECOPD episodes may provide an efficient, valid method of data collection. Comparing EHR-derived AECOPD episodes with those collected by eCRF resulted in slightly fewer episodes, and eCRF episodes of extreme lengths were poorly captured in EHR. Analysis of routinely collected EHR data may be reasonable when relative, rather than absolute, rates of AECOPD are relevant for stakeholders' decision making.
| S-EPMC7028141 | biostudies-literature
Project description:ObjectiveOutpatient clinics lack guidance for tackling modern efficiency and productivity demands. Workflow studies require large amounts of timing data that are prohibitively expensive to collect through observation or tracking devices. Electronic health records (EHRs) contain a vast amount of timing data - timestamps collected during regular use - that can be mapped to workflow steps. This study validates using EHR timestamp data to predict outpatient ophthalmology clinic workflow timings at Oregon Health and Science University and demonstrates their usefulness in 3 different studies.Materials and methodsFour outpatient ophthalmology clinics were observed to determine their workflows and to time each workflow step. EHR timestamps were mapped to the workflow steps and validated against the observed timings.ResultsThe EHR timestamp analysis produced times that were within 3 min of the observed times for >80% of the appointments. EHR use patterns affected the accuracy of using EHR timestamps to predict workflow times.DiscussionEHR timestamps provided a reasonable approximation of workflow and can be used for workflow studies. They can be used to create simulation models, analyze EHR use, and quantify the impact of trainees on workflow.ConclusionThe secondary use of EHR timestamp data is a valuable resource for clinical workflow studies. Sample timestamp data files and algorithms for processing them are provided and can be used as a template for more studies in other clinical specialties and settings.
| S-EPMC6080808 | biostudies-literature