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2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 533-534
Author(s):  
Davide Vetrano ◽  
Alberto Zucchelli ◽  
Graziano Onder ◽  
Roberto Bernabei ◽  
Laura Fratiglioni ◽  
...  

Abstract Recognizing frailty in primary care is important to implement personalized care pathways and for prognostication. The aim of this study was to build and validate a frailty index based on routinely collected primary care data in Italy. We used clinical data from 308,280 Italian primary care patients 60+ with at least 5 years of follow-up, part of the Health Search Database. A heuristic algorithm was used to select the deficits to be included in a highly performant frailty index. The fitness of the index was assessed through the c-statistics derived by survival models. Results were externally validated using the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). After testing 3.4 million of deficits combinations, 25 deficits were selected to be included in the Health Search Frailty Index (HS-FI). After adjusting by sex, age and geographical area, the HS-FI was associated with 5-year mortality (HR per 0.1 increase 1.99; 95%CI 1.95-2.02) and hospitalization rate (HR per 0.1 increase 1.25; 95%CI 1.23-1.27). In the external validation cohort, HS-FI independently predicted mortality, hospitalization, incident disability, incident dementia, and incident falls. This is the first frailty index built following a data-driven approach, using national representative primary care data. The implementation of such tool – derived by routinely collected data – in primary care software will ease the prompt, comparable and reliable recognition of frailty at the population level.


Thorax ◽  
2021 ◽  
pp. thoraxjnl-2021-217142
Author(s):  
Emma L O'Dowd ◽  
Kevin ten Haaf ◽  
Jaspreet Kaur ◽  
Stephen W Duffy ◽  
William Hamilton ◽  
...  

Lung cancer screening is effective if offered to people at increased risk of the disease. Currently, direct contact with potential participants is required for evaluating risk. A way to reduce the number of ineligible people contacted might be to apply risk-prediction models directly to digital primary care data, but model performance in this setting is unknown.MethodThe Clinical Practice Research Datalink, a computerised, longitudinal primary care database, was used to evaluate the Liverpool Lung Project V.2 (LLPv2) and Prostate Lung Colorectal and Ovarian (modified 2012) (PLCOm2012) models. Lung cancer occurrence over 5–6 years was measured in ever-smokers aged 50–80 years and compared with 5-year (LLPv2) and 6-year (PLCOm2012) predicted risk.ResultsOver 5 and 6 years, 7123 and 7876 lung cancers occurred, respectively, from a cohort of 842 109 ever-smokers. After recalibration, LLPV2 produced a c-statistic of 0.700 (0.694–0.710), but mean predicted risk was over-estimated (predicted: 4.61%, actual: 0.9%). PLCOm2012 showed similar performance (c-statistic: 0.679 (0.673–0.685), predicted risk: 3.76%. Applying risk-thresholds of 1% (LLPv2) and 0.15% (PLCOm2012), would avoid contacting 42.7% and 27.4% of ever-smokers who did not develop lung cancer for screening eligibility assessment, at the cost of missing 15.6% and 11.4% of lung cancers.ConclusionRisk-prediction models showed only moderate discrimination when applied to routinely collected primary care data, which may be explained by quality and completeness of data. However, they may substantially reduce the number of people for initial evaluation of screening eligibility, at the cost of missing some lung cancers. Further work is needed to establish whether newer models have improved performance in primary care data.


Author(s):  
Josephina G. Kuiper ◽  
Myrthe P. P. Herk‐Sukel ◽  
Valery E. P. P. Lemmens ◽  
Ernst J. Kuipers ◽  
Ron M. C. Herings

BMJ Open ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. e053624
Author(s):  
Daniel Smith ◽  
Kathryn Willan ◽  
Stephanie L Prady ◽  
Josie Dickerson ◽  
Gillian Santorelli ◽  
...  

ObjectivesWe aimed to examine agreement between common mental disorders (CMDs) from primary care records and repeated CMD questionnaire data from ALSPAC (the Avon Longitudinal Study of Parents and Children) over adolescence and young adulthood, explore factors affecting CMD identification in primary care records, and construct models predicting ALSPAC-derived CMDs using only primary care data.Design and settingProspective cohort study (ALSPAC) in Southwest England with linkage to electronic primary care records.ParticipantsPrimary care records were extracted for 11 807 participants (80% of 14 731 eligible). Between 31% (3633; age 15/16) and 11% (1298; age 21/22) of participants had both primary care and ALSPAC CMD data.Outcome measuresALSPAC outcome measures were diagnoses of suspected depression and/or CMDs. Primary care outcome measure were Read codes for diagnosis, symptoms and treatment of depression/CMDs. For each time point, sensitivities and specificities for primary care CMD diagnoses were calculated for predicting ALSPAC-derived measures of CMDs, and the factors associated with identification of primary care-based CMDs in those with suspected ALSPAC-derived CMDs explored. Lasso (least absolute selection and shrinkage operator) models were used at each time point to predict ALSPAC-derived CMDs using only primary care data, with internal validation by randomly splitting data into 60% training and 40% validation samples.ResultsSensitivities for primary care diagnoses were low for CMDs (range: 3.5%–19.1%) and depression (range: 1.6%–34.0%), while specificities were high (nearly all >95%). The strongest predictors of identification in the primary care data for those with ALSPAC-derived CMDs were symptom severity indices. The lasso models had relatively low prediction rates, especially in the validation sample (deviance ratio range: −1.3 to 12.6%), but improved with age.ConclusionsPrimary care data underestimate CMDs compared to population-based studies. Improving general practitioner identification, and using free-text or secondary care data, is needed to improve the accuracy of models using clinical data.


2021 ◽  
Author(s):  
Jessica Erin Butler ◽  
Mintu Nath ◽  
Dimitra Blana ◽  
William P Ball ◽  
Nicola Beech ◽  
...  

Background In March 2020, the government of Scotland identified people deemed clinically extremely vulnerable to COVID due to their pre-existing health conditions. These people were advised to strictly self-isolate (shield) at the start of the pandemic, except for necessary healthcare. We examined who was identified as clinically extremely vulnerable, how their healthcare changed during isolation, and whether this process exacerbated healthcare inequalities. Methods We linked those on the shielding register in NHS Grampian, a health authority in Scotland, to healthcare records from 2015-2020. We described the source of identification, demographics, and clinical history of the cohort. We measured changes in out-patient, in-patient, and emergency healthcare during isolation in the shielding population and compared to the general non-shielding population. Results The register included 16,092 people (3% of the population), clinically vulnerable primarily due to a respiratory disease, immunosuppression, or cancer. Among them, 42% were not identified by national healthcare record screening but added ad hoc, with these additions including more children and fewer economically-deprived. During isolation, all forms of healthcare use decreased (25%-46%), with larger decreases in scheduled care than in emergency care. However, people shielding had better maintained scheduled care compared to the non-shielding general population: out-patient visits decreased 35% vs 49%; in-patient visits decreased 46% vs 81%. Notably, there was substantial variation in whose scheduled care was maintained during isolation: younger people and those with cancer had significantly higher visit rates, but there was no difference between sexes or socioeconomic levels. Conclusions Healthcare changed dramatically for the clinically extremely vulnerable population during the pandemic. The increased reliance on emergency care while isolating indicates that continuity of care for existing conditions was not optimal. However, compared to the general population, there was success in maintaining scheduled care, particularly in young people and those with cancer. We suggest that integrating demographic and primary care data would improve identification of the clinically vulnerable and could aid prioritising their care.


Drug Safety ◽  
2021 ◽  
Author(s):  
Grace N. Okoli ◽  
Puja Myles ◽  
Tarita Murray-Thomas ◽  
Hilary Shepherd ◽  
Ian C. K. Wong ◽  
...  

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