scholarly journals Neutrophil-lymphocyte ratio across psychiatric diagnoses: An electronic health record investigation

2020 ◽  
Author(s):  
Aimee Brinn ◽  
James M Stone
Keyword(s):  
Mental Health ◽  
Electronic Health Records ◽  
National Health ◽  
Psychiatric Patients ◽  
Secondary Outcome ◽  
Health Records ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Mental Health Diagnoses ◽  
Electronic Health

AbstractObjectivesThe main objective of this study was to compare neutrophil-lymphocyte ratio, a marker of systemic inflammation, between patients diagnosed with ICD-10 psychiatric disorders and control participants.DesignA cross-sectional methodology was employed to retrospectively analyse electronic health records and records derived from a national health survey.SettingA secondary mental health care service consisting of four boroughs in South London.ParticipantsA diverse sample of 13,888 psychiatric patients extracted from South London and Maudsley electronic health records database and 3,920 control participants extracted from National Health and Nutrition Survey (2015-2016) were included in the study.Primary and secondary outcome measuresPrimary: NLR levels in patients with mental health diagnoses, NLR between patients with different mental health diagnoses. Secondary: Relationship of NLR to length of hospitalisation and to mortality.ResultsNLR was elevated compared to controls in patients with diagnoses including dementia, alcohol dependence, schizophrenia, bipolar affective disorder, depression, non-phobic anxiety disorders, and mild mental retardation (p < 0.05). NLR also correlated with age, antipsychotic use and hypnotic use. NLR was found to be higher in individuals of “White” ethnicity and lower in individuals of “Black” ethnicity. Elevated NLR was associated with increased mortality (β = 0.103, p = 2.9e-08) but not with hospital admissions or face-to-face contacts.ConclusionsElevated NLR may reflect a transdiagnostic pathological process occurring in a subpopulation of psychiatric patients. NLR may be useful to identify and stratify patients who could benefit from adjunctive anti-inflammatory treatment.Article SummaryStrengths and limitations of this studyLargest study to date of cross-diagnostic neutrophil-lymphocyte measurements in a psychiatric population.Sample is representative of diverse adult psychiatric patients in South London.Systematic differences between patients and controls reduce the validity of these comparisons.This study was retrospective and thus, confounding measures such as body mass index, smoking status and diet were unavailable.

scholarly journals Neutrophil–lymphocyte ratio across psychiatric diagnoses: a cross-sectional study using electronic health records

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e036859
Author(s):  
Aimee Brinn ◽  
James Stone
Keyword(s):  
Mental Health ◽  
Electronic Health Records ◽  
National Health ◽  
Psychiatric Patients ◽  
Cross Sectional ◽  
Health Records ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio ◽  
Mental Health Diagnoses ◽  
Electronic Health

ObjectivesThe main objective of this study was to compare neutrophil–lymphocyte ratio (NLR), a marker of systemic inflammation, between patients diagnosed with International Classification of Diseases 10th Revision (ICD-10) psychiatric disorders and control participants.DesignA cross-sectional methodology was employed to retrospectively analyse electronic health records and records derived from a national health survey.SettingA secondary mental healthcare service consisting of four boroughs in South London.ParticipantsA diverse sample of 13 888 psychiatric patients extracted from South London and Maudsley electronic health records database and 3920 control participants extracted from National Health and Nutrition Survey (2015–2016) were included in the study.Primary and secondary outcome measuresPrimary: NLR levels in patients with mental health diagnoses, NLR between patients with different mental health diagnoses. Secondary: relationship of NLR to length of hospitalisation and to mortality.ResultsNLR was elevated compared with controls in patients with diagnoses including dementia, alcohol dependence, schizophrenia, bipolar affective disorder, depression, non-phobic anxiety disorders and mild mental retardation (p<0.05). NLR also correlated with age, antipsychotic use and hypnotic use. NLR was found to be higher in individuals of ‘white’ ethnicity and lower in individuals of ‘black’ ethnicity. Elevated NLR was associated with increased mortality (β=0.103, p=2.9e−08) but not with hospital admissions or face-to-face contacts.ConclusionsElevated NLR may reflect a transdiagnostic pathological process occurring in a subpopulation of psychiatric patients. NLR may be useful to identify and stratify patients who could benefit from adjunctive anti-inflammatory treatment.

scholarly journals Maximizing the use of social and behavioural information from secondary care mental health electronic health records

2020 ◽  
Vol 107 ◽  
pp. 103429
Author(s):  
S.M. Goodday ◽  
A. Kormilitzin ◽  
N. Vaci ◽  
Q. Liu ◽  
A. Cipriani ◽  
...  
Keyword(s):  
Mental Health ◽  
Electronic Health Records ◽  
Secondary Care ◽  
Health Records ◽  
Electronic Health

scholarly journals Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: cross-sectional study in a Mediterranean population

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e029594 ◽  
Author(s):  
Concepción Violán ◽  
Quintí Foguet-Boreu ◽  
Sergio Fernández-Bertolín ◽  
Marina Guisado-Clavero ◽  
Margarita Cabrera-Bean ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Electronic Health Records ◽  
Cross Sectional Study ◽  
Secondary Outcome ◽  
Sectional Study ◽  
Cross Sectional ◽  
Health Records ◽  
Soft Clustering ◽  
Fuzzy C Means ◽  
Electronic Health

ObjectivesThe aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population ≥65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. Fuzzy cluster analysis allows individuals to be linked simultaneously to multiple clusters and is more consistent with clinical experience than other approaches frequently found in the literature.DesignA cross-sectional study was conducted based on data from electronic health records.Setting284 primary healthcare centres in Catalonia, Spain (2012).Participants916 619 eligible individuals were included (women: 57.7%).Primary and secondary outcome measuresWe extracted data on demographics, International Classification of Diseases version 10 chronic diagnoses, prescribed drugs and socioeconomic status for patients aged ≥65. Following principal component analysis of categorical and continuous variables for dimensionality reduction, machine learning techniques were applied for the identification of disease clusters in a fuzzy c-means analysis. Sensitivity analyses, with different prevalence cut-off points for chronic diseases, were also conducted. Solutions were evaluated from clinical consistency and significance criteria.ResultsMultimorbidity was present in 93.1%. Eight clusters were identified with a varying number of disease values: nervous and digestive; respiratory, circulatory and nervous; circulatory and digestive; mental, nervous and digestive, female dominant; mental, digestive and blood, female oldest-old dominant; nervous, musculoskeletal and circulatory, female dominant; genitourinary, mental and musculoskeletal, male dominant; and non-specified, youngest-old dominant. Nuclear diseases were identified for each cluster independently of the prevalence cut-off point considered.ConclusionsMultimorbidity patterns were obtained using fuzzy c-means cluster analysis. They are clinically meaningful clusters which support the development of tailored approaches to multimorbidity management and further research.

scholarly journals Evaluation of Smoking Status Identification Using Electronic Health Records and Open-Text Information in a Large Mental Health Case Register

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e74262 ◽  
Author(s):  
Chia-Yi Wu ◽  
Chin-Kuo Chang ◽  
Debbie Robson ◽  
Richard Jackson ◽  
Shaw-Ji Chen ◽  
...  
Keyword(s):  
Mental Health ◽  
Electronic Health Records ◽  
Smoking Status ◽  
Health Records ◽  
Case Register ◽  
Text Information ◽  
Electronic Health

scholarly journals Patient Use and Experience With Online Access to Electronic Health Records in Norway: Results From an Online Survey (Preprint)

2019 ◽  
Author(s):  
Paolo Zanaboni ◽  
Per Egil Kummervold ◽  
Tove Sørensen ◽  
Monika Alise Johansen
Keyword(s):  
Health Care ◽  
Electronic Health Records ◽  
Health Care Providers ◽  
National Health ◽  
Chronic Conditions ◽  
Online Survey ◽  
Health Condition ◽  
Care Providers ◽  
Health Records ◽  
Electronic Health

BACKGROUND The electronic health record (EHR) has been fully established in all Norwegian hospitals. Patient-accessible electronic health records (PAEHRs) are available to citizens aged 16 years and older through the national health portal Helsenorge. OBJECTIVE This study aimed at understanding how patients use PAEHRs. Three research questions were addressed in order to explore (1) characteristics of users, (2) patients’ use of the service, and (3) patient experience with the service. METHODS We conducted an online survey of users who had accessed their EHR online at least once through the national health portal. Patients from two of the four health regions in Norway were invited to participate. Quantitative data were supplemented by qualitative information. RESULTS A total of 1037 respondents participated in the survey, most of whom used the PAEHR regularly (305/1037, 29.4%) or when necessary (303/1037, 29.2%). Service utilization was associated with self-reported health, age, gender, education, and health care professional background. Patients found the service useful to look up health information (687/778, 88.3%), keep track of their treatment (684/778, 87.9%), prepare for a hospital appointment (498/778, 64.0%), and share documents with their general practitioner (292/778, 37.5%) or family (194/778, 24.9%). Most users found it easy to access their EHR online (965/1037, 93.1%) and did not encounter technical challenges. The vast majority of respondents (643/755, 85.2%) understood the content, despite over half of them acknowledging some difficulties with medical terms or phrases. The overall satisfaction with the service was very high (700/755, 92.7%). Clinical advantages to the patients included enhanced knowledge of their health condition (565/691, 81.8%), easier control over their health status (685/740, 92.6%), better self-care (571/653, 87.4%), greater empowerment (493/674, 73.1%), easier communication with health care providers (493/618, 79.8%), and increased security (655/730, 89.7%). Patients with complex, long-term or chronic conditions seemed to benefit the most. PAEHRs were described as useful, informative, effective, helpful, easy, practical, and safe. CONCLUSIONS PAEHRs in Norway are becoming a mature service and are perceived as useful by patients. Future studies should include experimental designs focused on specific populations or chronic conditions that are more likely to achieve clinically meaningful benefits. Continuous evaluation programs should be conducted to assess implementation and changes of wide-scale routine services over time.

scholarly journals Epidemiology and determinants of non-diabetic hyperglycaemia and its conversion to type 2 diabetes mellitus, 2000–2015: cohort population study using UK electronic health records

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e040201 ◽  
Author(s):  
Rathi Ravindrarajah ◽  
David Reeves ◽  
Elizabeth Howarth ◽  
Rachel Meacock ◽  
Claudia Soiland-Reyes ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Electronic Health Records ◽  
Secondary Outcome ◽  
Health Records ◽  
Conversion Rates ◽  
Electronic Health ◽  
Over Time

ObjectivesTo study the characteristics of UK individuals identified with non-diabetic hyperglycaemia (NDH) and their conversion rates to type 2 diabetes mellitus (T2DM) from 2000 to 2015, using the Clinical Practice Research Datalink.DesignCohort study.SettingsUK primary Care Practices.ParticipantsElectronic health records identified 14 272 participants with NDH, from 2000 to 2015.Primary and secondary outcome measuresBaseline characteristics and conversion trends from NDH to T2DM were explored. Cox proportional hazards models evaluated predictors of conversion.ResultsCrude conversion was 4% within 6 months of NDH diagnosis, 7% annually, 13% within 2 years, 17% within 3 years and 23% within 5 years. However, 1-year conversion fell from 8% in 2000 to 4% in 2014. Individuals aged 45–54 were at the highest risk of developing T2DM (HR 1.20, 95% CI 1.15 to 1.25— compared with those aged 18–44), and the risk reduced with older age. A body mass index (BMI) above 30 kg/m2 was strongly associated with conversion (HR 2.02, 95% CI 1.92 to 2.13—compared with those with a normal BMI). Depression (HR 1.10, 95% CI 1.07 to 1.13), smoking (HR 1.07, 95% CI 1.03 to 1.11—compared with non-smokers) or residing in the most deprived areas (HR 1.17, 95% CI 1.11 to 1.24—compared with residents of the most affluent areas) was modestly associated with conversion.ConclusionAlthough the rate of conversion from NDH to T2DM fell between 2010 and 2015, this is likely due to changes over time in the cut-off points for defining NDH, and more people of lower diabetes risk being diagnosed with NDH over time. People aged 45–54, smokers, depressed, with high BMI and more deprived are at increased risk of conversion to T2DM.

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