scholarly journals Linking cohort data with administrative health data to develop a new hypertension prediction model to aid precision health approach

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Mohammad Chowdhury ◽  
Tanvir Turin ◽  
Alex Leung ◽  
Maeve O’Beirne ◽  
Khokan Sikdar ◽  
...  
Keyword(s):  
Prediction Model ◽  
Behavioral Interventions ◽  
Medical Condition ◽  
Claim Data ◽  
Health Data ◽  
Care Utilization ◽  
Incident Hypertension ◽  
Administrative Health Data ◽  
Cohort Data

IntroductionHypertension is a common medical condition, affecting 1 in 5 Canadians, and is a major risk factor for heart attack, stroke, and kidney disease. Predicting the risk of developing incident hypertension may help to inform targeted preventive strategies. Objectives and ApproachIdentification of major risk factors and incorporation into a multivariable model for risk stratification may help to identify individuals who are at highest risk for developing incident hypertension and would potentially benefit most from intervention. The goal of the proposed research is to develop a robust hypertension prediction model for the general population using the Alberta Tomorrow Project (ATP) cohort data linked with Alberta’s administrative health data. ATP is Alberta's largest population health cohort, contains baseline data on socio-demographic characteristic, personal and family history of disease, medication use, lifestyle and health behavior, environmental exposures, physical measures and bio samples. ResultsAlberta’s administrative health data additionally provides information on health care utilization, enrollment, drugs, physician services, and hospital services. A prediction model for hypertension will be developed using logistic regression where information on candidate variables for the model will be gathered from ATP data and outcome (incident hypertension) will be ascertained from administrative health data (physicians/practitioner claim data and hospital discharge abstract data). Lacking follow-up information in current ATP data has laid the foundation of linking the two data sources through an anonymous unique person identifier (e.g. PHN) that will eventually provide follow-up information on ATP participants who are free of hypertension at baseline developed the disease as well as information on other potential variables. Conclusion/ImplicationsThe proposed prediction model will help to identify individuals at highest risk for developing hypertension and those who may benefit most from targeted healthy behavioral interventions and/or treatment. Such identification of high risk people may help prevent hypertension as well as the continuing costly cycle of managing hypertension and its complications.

scholarly journals Detection of adverse drug events in e-prescribing and administrative health data: a validation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bettina Habib ◽  
Robyn Tamblyn ◽  
Nadyne Girard ◽  
Tewodros Eguale ◽  
Allen Huang
Keyword(s):  
Administrative Data ◽  
Gold Standard ◽  
Adverse Drug Events ◽  
Study Drug ◽  
Health Data ◽  
Diagnostic Code ◽  
Administrative Health Data ◽  
Potential Ades ◽  
Patient Reported

Abstract Background Administrative health data are increasingly used to detect adverse drug events (ADEs). However, the few studies evaluating diagnostic codes for ADE detection demonstrated low sensitivity, likely due to narrow code sets, physician under-recognition of ADEs, and underreporting in administrative data. The objective of this study was to determine if combining an expanded ICD code set in administrative data with e-prescribing data improves ADE detection. Methods We conducted a prospective cohort study among patients newly prescribed antidepressant or antihypertensive medication in primary care and followed for 2 months. Gold standard ADEs were defined as patient-reported symptoms adjudicated as medication-related by a clinical expert. Potential ADEs in administrative data were defined as physician, ED, or hospital visits during follow-up for known adverse effects of the study medication, as identified by ICD codes. Potential ADEs in e-prescribing data were defined as study drug discontinuations or dose changes made during follow-up for safety or effectiveness reasons. Results Of 688 study participants, 445 (64.7%) were female and mean age was 64.2 (SD 13.9). The study drug for 386 (56.1%) patients was an antihypertensive, and for 302 (43.9%) an antidepressant. Using the gold standard definition, 114 (16.6%) patients experienced an ADE, with 40 (10.4%) among antihypertensive users and 74 (24.5%) among antidepressant users. The sensitivity of the expanded ICD code set was 7.0%, of e-prescribing data 9.7%, and of the two combined 14.0%. Specificities were high (86.0–95.0%). The sensitivity of the combined approach increased to 25.8% when analysis was restricted to the 27% of patients who indicated having reported symptoms to a physician. Conclusion Combining an expanded diagnostic code set with e-prescribing data improves ADE detection. As few patients report symptoms to their physician, higher detection rates may be achieved by collecting patient-reported outcomes via emerging digital technologies such as patient portals and mHealth applications.

Trying to Predict in Real Time the Risk of Unplanned Hospital Readmissions

2020 ◽  
pp. 299-310
Author(s):  
Nilmini Wickramasinghe
Keyword(s):  
High Risk ◽  
Real Time ◽  
Predictive Models ◽  
Medical Condition ◽  
Hospital Readmissions ◽  
Claim Data ◽  
Health Data ◽  
Australian Health ◽  
Insurance Claim Data ◽  
Readmission Risk

This study aims to identify predictors for patients likely to be readmitted to a hospital within 28 days of discharge and to develop and validate a prediction model for identifying patients at a high risk of readmission. Numerous attempts have been made to build similar predictive models. However, the majority of existing models suffer from at least one of the following shortcomings: the model is not based on Australian Health Data; the model uses insurance claim data, which would not be available in a real-time clinical setting; the model does not consider socio-demographic determinants of health, which have been demonstrated to be predictive of readmission risk; or the model is limited to a particular medical condition and is thus limited in scope.

scholarly journals Health Care Indicators of Moderate to Severe IBD and Subsequent IBD-Related Disability: A Longitudinal Study

2019 ◽  
Vol 25 (12) ◽  
pp. 1996-2005 ◽  
Author(s):  
Leigh Anne Shafer ◽  
John R Walker ◽  
Tarun Chhibba ◽  
Laura E Targownik ◽  
Harminder Singh ◽  
...  
Keyword(s):  
Health Care ◽  
Longitudinal Study ◽  
Health Care Utilization ◽  
Severe Disease ◽  
Population Based ◽  
Health Data ◽  
Care Utilization ◽  
Administrative Health Data ◽  
Health Care Indicators

Using administrative health data of a population based sample of persons with IBD we found that milestones of health care utilization suggesting moderate to severe disease (higher number of IBD-related hospitalizations, IBD-related surgeries, and corticosteroid or anti-TNF usage) predicted later development of IBD-related disability.

scholarly journals Chronic Disease Surveillance in Alberta’s Tomorrow Project using Administrative Health Data

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Ming Ye ◽  
Jennifer Vena ◽  
Jeffrey Johnson ◽  
Grace Shen-Tu ◽  
Dean Eurich
Keyword(s):  
Chronic Disease ◽  
Cardiovascular Diseases ◽  
Chronic Diseases ◽  
Prospective Cohort ◽  
Chronic Conditions ◽  
Population Based ◽  
Health Data ◽  
Incidence Rates ◽  
Administrative Health Data

IntroductionAlberta's Tomorrow Project (ATP) is the largest population-based prospective cohort study of cancer and chronic diseases in Alberta, Canada. The ATP cohort data were primarily self-reported by participants on lifestyle behaviors and disease risk factors at the enrollment, which lacks sufficient and accurate data on chronic disease diagnosis for longer-term follow-up. ObjectivesTo characterize the occurrence rate and trend of chronic diseases in the ATP cohort by linking with administrative healthcare data. MethodsA set of validated algorithms using ICD codes were applied to Alberta Health (AH) administrative data (October 2000-March 2018) linked to the ATP cohort to determine the prevalence and incidence of common chronic diseases. ResultsThere were 52,770 ATP participants (51.2± 9.4 years old at enrollment and 63.7% females) linked to the AH data with average follow-up of 10.1± 4.4 years. In the ATP cohort, hypertension (18.5%), depression (18.1%), chronic pain (12.8%), osteoarthritis (10.1%) and cardiovascular diseases (8.7%) were the most prevalent chronic conditions. The incidence rates varied across diseases, with the highest rates for hypertension (22.1 per 1000 person-year), osteoarthritis (16.2 per 1000 person-year) and ischemic heart diseases (13.0 per 1000 person-year). All chronic conditions had increased prevalence over time (p <0.001 for trend tests), while incidence rates were relatively stable. The proportion of participants with two or more of these conditions (multi-morbidity) increased from 3.9% in 2001 to 40.3% in 2017. ConclusionsThis study shows an increasing trend of chronic diseases in the ATP cohort, particularly related to cardiovascular diseases and multi-morbidity. Using administrative health data to monitor chronic diseases for large population-based prospective cohort studies is feasible in Alberta, and our approach could be further applied in a broader research area, including health services research, to enhance research capacity of these population-based studies in Canada.

Serum Thyroid Peroxidase Antibody Level and Incident Hypertension in Iranian Men: A Suggestion for the Role of Thyroid Autoimmunity

2020 ◽  
Vol 20 (10) ◽  
pp. 1711-1718
Author(s):  
Maryam Tohidi ◽  
Aidin Baghbani-Oskouei ◽  
Atieh Amouzegar ◽  
Ladan Mehran ◽  
Fereidoun Azizi ◽  
...  
Keyword(s):  
Total Population ◽  
Thyroid Autoimmunity ◽  
Elevated Serum ◽  
Thyroid Peroxidase ◽  
Incident Hypertension ◽  
Natural Logarithm ◽  
Hazard Ratios ◽  
Thyroid Peroxidase Antibody ◽  
Unit Increase

Background: Dysfunction of the thyroid gland has profound effects on the cardiovascular system. Objective: We aimed to explore the relation of serum thyroid peroxidase antibody (TPO-Ab), as a marker of thyroid autoimmunity with incident hypertension among a euthyroid population. Methods: A total of 3681 participants (1647 men) entered the study. Multivariate Cox proportional hazard models were conducted to estimate the association between TPO-Ab and incident hypertension. Results: The mean age (standard deviation) of the participants was 37.5 (12.8) years. During a median follow-up of 12.2 years, 511 men and 519 women developed hypertension. The multivariable hazard ratios (HRs) and related 95% confidence intervals (CIs) of 1-unit increase in natural logarithm (ln) of TPO-Ab for incident hypertension were 1.09 (1.00-1.19), 1.03 (0.97-1.10), and 1.05 (1.00-1.11) for men, women, and total population, respectively. Moreover, considering the TPO-Ab status as a categorical variable (i.e. TPO-Ab positive or TPO-Ab negative), the multivariate-adjusted HRs (95% CIs) of TPO-Ab positivity for incident hypertension, were 1.33 (0.95-1.85), 1.12 (0.86-1.45) and 1.19 (0.97- 1.46) for men, women, and total population, respectively. Conclusion: Elevated serum TPO-Ab level can contribute to the development of hypertension among euthyroid men during a long follow-up; suggesting a role for thyroid autoimmunity.

scholarly journals COVID-19 Lockdown Effect on Not Institutionalized Patients with Dementia and Caregivers

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 893
Author(s):  
Rita Moretti ◽  
Paola Caruso ◽  
Mauro Giuffré ◽  
Claudio Tiribelli
Keyword(s):  
Medical Condition ◽  
Direct Consequence ◽  
Anxiety And Depression ◽  
Behavioral Reactions ◽  
Old Patients ◽  
Subcortical Vascular Dementia ◽  
Behavior Disturbances ◽  
Institutionalized Patients ◽  
Severity Degree

SARS-COV-2 is a severe medical condition. Old patients are very vulnerable, but they have been studied only as institutionalized patients. During the lock-down, little attention is dedicated to old, demented patients who lived at home. This study wants to examine their behavioral reactions by video-phone follow-up. We conducted a longitudinal study in subcortical vascular dementia (sVAD) patients. We enrolled 221 sVAD, not institutionalized patients. We divided sVAD patients into low-medium grade sVAD (A) and severe sVAD (B), based on neuroimaging severity degree and executive alterations. At baseline, at the end of lock-down, and two months later, global behavioral symptoms were recorded for each patient. We found significantly higher scores of general behavioral deterioration, anxiety, delusions, hallucinations and apathy after controlling for sVAD severity. The direct consequence was a drastic increment of psychotropic drugs prescribed and employed during the lock-down. Moreover, caregivers’ stress has been evaluated, together with their anxiety and depression levels. During the lock-down, their scores increased and reflected a severe worsening of their behavior. Our data demonstrate that social isolation induces a severe perception of loneliness and abandonment; these fears can exacerbate behavior disturbances in old-aged frail persons. Thus, these can be considered as indirect victims of SARS-COV-2.

scholarly journals Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy

2021 ◽  
Vol 20 ◽  
pp. 153303382110246
Author(s):  
Jihwan Park ◽  
Mi Jung Rho ◽  
Hyong Woo Moon ◽  
Jaewon Kim ◽  
Chanjung Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Random Forest ◽  
Radical Prostatectomy ◽  
Prediction Model ◽  
Biochemical Recurrence ◽  
Clinical Decision ◽  
Outcome Variable ◽  
Tertiary Hospitals ◽  
Input Variables

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

scholarly journals Stereotactic radiosurgery for treating meningiomas eligible for complete resection

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Maximilian I. Ruge ◽  
Juman Tutunji ◽  
Daniel Rueß ◽  
Eren Celik ◽  
Christian Baues ◽  
...  
Keyword(s):  
Adverse Events ◽  
Stereotactic Radiosurgery ◽  
Medical Condition ◽  
Local Tumor Control ◽  
Complete Resection ◽  
Neurological Deficits ◽  
Tumor Control ◽  
Recurrence Rates ◽  
Treatment Volume

Abstract Background For meningiomas, complete resection is recommended as first-line treatment while stereotactic radiosurgery (SRS) is established for meningiomas of smaller size considered inoperable. If the patient´s medical condition or preference excludes surgery, SRS remains a treatment option. We evaluated the efficacy and safety of SRS in a cohort comprising these cases. Methods In this retrospective single-centre analysis we included patients receiving single fraction SRS either by modified LINAC or robotic guidance by Cyberknife for potentially resectable intracranial meningiomas. Treatment-related adverse events as well as local and regional control rates were determined from follow-up imaging and estimated by the Kaplan–Meier method. Results We analyzed 188 patients with 218 meningiomas. The median radiological, and clinical follow-up periods were 51.4 (6.2–289.6) and 55.8 (6.2–300.9) months. The median tumor volume was 4.2 ml (0.1–22), and the mean marginal radiation dose was 13.0 ± 3.1 Gy, with reference to the 80.0 ± 11.2% isodose level. Local recurrence was observed in one case (0.5%) after 239 months. The estimated 2-, 5-, 10- and 15-year regional recurrence rates were 1.5%, 3.0%, 6.6% and 6.6%, respectively. Early adverse events (≤ 6 months after SRS) occurred in 11.2% (CTCEA grade 1–2) and resolved during follow-up in 7.4% of patients, while late adverse events were documented in 14.4% (grade 1–2; one case grade 3). Adverse effects (early and late) were associated with the presence of symptoms or neurological deficits prior to SRS (p < 0.03) and correlated with the treatment volume (p < 0.02). Conclusion In this analysis SRS appears to be an effective treatment for patients with meningiomas eligible for complete resection and provides reliable long-term local tumor control with low rates of mild morbidity.

scholarly journals DERIVATION OF A MODEL THAT ACCURATELY PREDICTS CARDIOVASCULAR FROM NON-CARDIOVASCULAR CAUSE OF DEATH USING ADMINISTRATIVE HEALTH DATA SOURCES

2019 ◽  
Vol 35 (10) ◽  
pp. S17
Author(s):  
S. Patel ◽  
A. Khan ◽  
A. Sivaswamy ◽  
L. Ferreira-Legere ◽  
P. Austin ◽  
...  
Keyword(s):  
Cause Of Death ◽  
Health Data ◽  
Data Sources ◽  
Administrative Health Data
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