Identifying patients at increased risk of hypoglycaemia in primary care: Development of a machine learning‐based screening tool

ObjectivesFrequent attenders (FAs) create a substantial portion of primary care workload but little is known about FAs’ sickness absences. The aim of the study is to investigate how occasional and persistent frequent attendance is associated with sickness absences among the working population in occupational health (OH) primary care.Setting and participantsThis is a longitudinal study using medical record data (2014–2016) from an OH care provider in Finland. In total, 59 676 patients were included and categorised into occasional and persistent FAs or non-FAs. Sick-leave episodes and their lengths were collected along with associated diagnostic codes. Logistic regression was used to analyse associations between FA status and sick leaves of different lengths (1–3, 4–14 and ≥15 days).ResultsBoth occasional and persistent FA had more and longer duration of sick leave than non-FA through the study years. Persistent FAs had consistently high absence rates. Occasional FAs had elevated absence rates even 2 years after their frequent attendance period. Persistent FAs (OR=11 95% CI 7.54 to 16.06 in 2016) and occasional FAs (OR=2.95 95% CI 2.50 to 3.49 in 2016) were associated with long (≥15 days) sickness absence when compared with non-FAs. Both groups of FAs had an increased risk of long-term sick leaves indicating a risk of disability pension.ConclusionBoth occasional and persistent FAs should be identified in primary care units caring for working-age patients. As frequent attendance is associated with long sickness absences and possibly disability pensions, rehabilitation should be directed at this group to prevent work disability.

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Predictive modeling for peri-implantitis by using machine learning techniques

Scientific Reports ◽

10.1038/s41598-021-90642-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tomoaki Mameno ◽

Masahiro Wada ◽

Kazunori Nozaki ◽

Toshihito Takahashi ◽

Yoshitaka Tsujioka ◽

...

Keyword(s):

Machine Learning ◽

Demographic Data ◽

Risk Indicators ◽

Machine Learning Techniques ◽

Support Vector ◽

Machine Learning Methods ◽

Complex Interactions ◽

Learning Techniques ◽

Increased Risk ◽

Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

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Quit Experiences among Primary Care Patients Enrolled in a Smoking Cessation Pilot RCT Early in the COVID-19 Pandemic

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18031011 ◽

2021 ◽

Vol 18 (3) ◽

pp. 1011

Author(s):

Andrea A. Joyce ◽

Grace M. Styklunas ◽

Nancy A. Rigotti ◽

Jordan M. Neil ◽

Elyse R. Park ◽

...

Keyword(s):

Primary Care ◽

Text Messaging ◽

Risk Perceptions ◽

Environmental Changes ◽

Age Groups ◽

Text Messages ◽

Increased Risk ◽

Primary Care Patients ◽

The Impact ◽

At Home

The impact of the COVID-19 pandemic on US adults’ smoking and quitting behaviors is unclear. We explored the impact of COVID-19 on smoking behaviors, risk perceptions, and reactions to text messages during a statewide stay-at-home advisory among primary care patients who were trying to quit. From May–June 2020, we interviewed smokers enrolled in a 12-week, pilot cessation trial providing text messaging and mailed nicotine replacement medication (NCT04020718). Twenty-two individuals (82% white, mean age 55 years), representing 88% of trial participants during the stay-at-home advisory, completed exit interviews; four (18%) of them reported abstinence. Interviews were thematically analyzed by two coders. COVID-19-induced environmental changes had mixed effects, facilitating quitting for some and impeding quitting for others. While stress increased for many, those who quit found ways to cope with stress. Generally, participants felt at risk for COVID-19 complications but not at increased risk of becoming infected. Reactions to COVID-19 and quitting behaviors differed across age groups, older participants reported difficulties coping with isolation (e.g., feeling disappointed when a text message came from the study and not a live person). Findings suggest that cessation interventions addressing stress and boredom are needed during COVID-19, while smokers experiencing isolation may benefit from live-person supports.

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Withdrawal of inhaled corticosteroids versus continuation of triple therapy in patients with COPD in real life: observational comparative effectiveness study

Respiratory Research ◽

10.1186/s12931-021-01615-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Helgo Magnussen ◽

◽

Sarah Lucas ◽

Therese Lapperre ◽

Jennifer K. Quint ◽

...

Keyword(s):

Primary Care ◽

Triple Therapy ◽

Comparative Effectiveness ◽

Inhaled Corticosteroids ◽

Blood Eosinophil ◽

Research Database ◽

Effectiveness Study ◽

Increased Risk ◽

Comparative Effectiveness Study ◽

Oral Corticosteroids

Abstract Background Inhaled corticosteroids (ICS) are indicated for prevention of exacerbations in patients with COPD, but they are frequently overprescribed. ICS withdrawal has been recommended by international guidelines in order to prevent side effects in patients in whom ICS are not indicated. Method Observational comparative effectiveness study aimed to evaluate the effect of ICS withdrawal versus continuation of triple therapy (TT) in COPD patients in primary care. Data were obtained from the Optimum Patient Care Research Database (OPCRD) in the UK. Results A total of 1046 patients who withdrew ICS were matched 1:4 by time on TT to 4184 patients who continued with TT. Up to 76.1% of the total population had 0 or 1 exacerbation the previous year. After controlling for confounders, patients who discontinued ICS did not have an increased risk of moderate or severe exacerbations (adjusted HR: 1.04, 95% confidence interval (CI) 0.94–1.15; p = 0.441). However, rates of exacerbations managed in primary care (incidence rate ratio (IRR) 1.33, 95% CI 1.10–1.60; p = 0.003) or in hospital (IRR 1.72, 95% CI 1.03–2.86; p = 0.036) were higher in the cessation group. Unsuccessful ICS withdrawal was significantly and independently associated with more frequent courses of oral corticosteroids the previous year and with a blood eosinophil count ≥ 300 cells/μL. Conclusions In this primary care population of patients with COPD, composed mostly of infrequent exacerbators, discontinuation of ICS from TT was not associated with an increased risk of exacerbation; however, the subgroup of patients with more frequent courses of oral corticosteroids and high blood eosinophil counts should not be withdrawn from ICS. Trial registration European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (EUPAS30851).

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Relation of severe COVID-19 to polypharmacy and prescribing of psychotropic drugs: the REACT-SCOT case-control study

BMC Medicine ◽

10.1186/s12916-021-01907-8 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Paul M. McKeigue ◽

◽

Sharon Kennedy ◽

Amanda Weir ◽

Jen Bishop ◽

...

Keyword(s):

Primary Care ◽

Rate Ratio ◽

Case Control Study ◽

Fatal Outcome ◽

Case Control ◽

Care Practice ◽

Drug Classes ◽

Increased Risk ◽

Co Morbidity ◽

Control Study

Abstract Background The objective of this study was to investigate the relation of severe COVID-19 to prior drug prescribing. Methods Severe cases were defined by entry to critical care or fatal outcome. For this matched case-control study (REACT-SCOT), all 4251 cases of severe COVID-19 in Scotland since the start of the epidemic were matched for age, sex and primary care practice to 36,738 controls from the population register. Records were linked to hospital discharges since June 2015 and dispensed prescriptions issued in primary care during the last 240 days. Results Severe COVID-19 was strongly associated with the number of non-cardiovascular drug classes dispensed. This association was strongest in those not resident in a care home, in whom the rate ratio (95% CI) associated with dispensing of 12 or more drug classes versus none was 10.8 (8.8, 13.3), and in those without any of the conditions designated as conferring increased risk of COVID-19. Of 17 drug classes postulated at the start of the epidemic to be “medications compromising COVID”, all were associated with increased risk of severe COVID-19 and these associations were present in those without any of the designated risk conditions. The fraction of cases in the population attributable to exposure to these drug classes was 38%. The largest effect was for antipsychotic agents: rate ratio 4.18 (3.42, 5.11). Other drug classes with large effects included proton pump inhibitors (rate ratio 2.20 (1.72, 2.83) for = 2 defined daily doses/day), opioids (3.66 (2.68, 5.01) for = 50 mg morphine equivalent/day) and gabapentinoids. These associations persisted after adjusting for covariates and were stronger with recent than with non-recent exposure. Conclusions Severe COVID-19 is associated with polypharmacy and with drugs that cause sedation, respiratory depression, or dyskinesia; have anticholinergic effects; or affect the gastrointestinal system. These associations are not easily explained by co-morbidity. Measures to reduce the burden of mortality and morbidity from COVID-19 should include reinforcing existing guidance on reducing overprescribing of these drug classes and limiting inappropriate polypharmacy. Registration ENCEPP number https://EUPAS35558

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Associations between left bundle branch block with different PR intervals, QRS durations, heart rates and the risk of heart failure: a register-based cohort study using ECG data from the primary care setting

Open Heart ◽

10.1136/openhrt-2020-001425 ◽

2021 ◽

Vol 8 (1) ◽

pp. e001425

Author(s):

Marc Meller Søndergaard ◽

Johannes Riis ◽

Karoline Willum Bodker ◽

Steen Møller Hansen ◽

Jesper Nielsen ◽

...

Keyword(s):

Heart Failure ◽

Primary Care ◽

Heart Rate ◽

Left Bundle Branch Block ◽

Bundle Branch Block ◽

Increased Risk ◽

Pr Interval ◽

Qrs Duration ◽

The Impact

AimLeft bundle branch block (LBBB) is associated with an increased risk of heart failure (HF). We assessed the impact of common ECG parameters on this association using large-scale data.Methods and resultsUsing ECGs recorded in a large primary care population from 2001 to 2011, we identified HF-naive patients with a first-time LBBB ECG. We obtained information on sex, age, emigration, medication, diseases and death from Danish registries. We investigated the association between the PR interval, QRS duration, and heart rate and the risk of HF over a 2-year follow-up period using Cox regression analysis.Of 2471 included patients with LBBB, 464 (18.8%) developed HF during follow-up. A significant interaction was found between QRS duration and heart rate (p<0.01), and the analyses were stratified on these parameters. Using a QRS duration <150 ms and a heart rate <70 beats per minute (bpm) as the reference, all groups were statistically significantly associated with the development of HF. Patients with a QRS duration ≥150 ms and heart rate ≥70 bpm had the highest risk of developing HF (HR 3.17 (95% CI 2.41 to 4.18, p<0.001). There was no association between the PR interval and HF after adjustment.ConclusionProlonged QRS duration and higher heart rate were associated with increased risk of HF among primary care patients with LBBB, while no association was observed with PR interval. Patients with LBBB with both a prolonged QRS duration (≥150 ms) and higher heart rate (≥70 bpm) have the highest risk of developing HF.

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The Rapid Mood Screener: A Novel and Pragmatic Screener Tool for Bipolar I Disorder

CNS Spectrums ◽

10.1017/s1092852920002709 ◽

2021 ◽

Vol 26 (2) ◽

pp. 167-168

Author(s):

C. Brendan Montano ◽

Mehul Patel ◽

Rakesh Jain ◽

Prakash S. Masand ◽

Amanda Harrington ◽

...

Keyword(s):

Risk Factors ◽

Primary Care ◽

Health Outcomes ◽

Screening Tool ◽

Clinical Interview ◽

Clinical Practices ◽

Predictive Values ◽

Bipolar I Disorder ◽

Manic Symptoms ◽

Depressive Episodes

AbstractIntroductionApproximately 70% of patients with bipolar disorder (BPD) are initially misdiagnosed, resulting in significantly delayed diagnosis of 7–10 years on average. Misdiagnosis and diagnostic delay adversely affect health outcomes and lead to the use of inappropriate treatments. As depressive episodes and symptoms are the predominant symptom presentation in BPD, misdiagnosis as major depressive disorder (MDD) is common. Self-rated screening instruments for BPD exist but their length and reliance on past manic symptoms are barriers to implementation, especially in primary care settings where many of these patients initially present. We developed a brief, pragmatic bipolar I disorder (BPD-I) screening tool that not only screens for manic symptoms but also includes risk factors for BPD-I (eg, age of depression onset) to help clinicians reduce the misdiagnosis of BPD-I as MDD.MethodsExisting questionnaires and risk factors were identified through a targeted literature search; a multidisciplinary panel of experts participated in 2 modified Delphi panels to select concepts thought to differentiate BPD-I from MDD. Individuals with self-reported BPD-I or MDD participated in cognitive debriefing interviews (N=12) to test and refine item wording. A multisite, cross-sectional, observational study was conducted to evaluate the screening tool’s predictive validity. Participants with clinical interview-confirmed diagnoses of BPD-I or MDD completed a draft 10-item screening tool and additional questionnaires/questions. Different combinations of item sets with various item permutations (eg, number of depressive episodes, age of onset) were simultaneously tested. The final combination of items and thresholds was selected based on multiple considerations including clinical validity, optimization of sensitivity and specificity, and pragmatism.ResultsA total of 160 clinical interviews were conducted; 139 patients had clinical interview-confirmed BPD-I (n=67) or MDD (n=72). The screening tool was reduced from 10 to 6 items based on item-level analysis. When 4 items or more were endorsed (yes) in this analysis sample, the sensitivity of this tool for identifying patients with BPD-I was 0.88 and specificity was 0.80; positive and negative predictive values were 0.80 and 0.88, respectively. These properties represent an improvement over the Mood Disorder Questionnaire, while using >50% fewer items.ConclusionThis new 6-item BPD-I screening tool serves to differentiate BPD-I from MDD in patients with depressive symptoms. Use of this tool can provide real-world guidance to primary care practitioners on whether more comprehensive assessment for BPD-I is warranted. Use of a brief and valid tool provides an opportunity to reduce misdiagnosis, improve treatment selection, and enhance health outcomes in busy clinical practices.FundingAbbVie Inc.

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Isotypes of autoantibodies against novel differential 4-hydroxy-2-nonenal-modified peptide adducts in serum is associated with rheumatoid arthritis in Taiwanese women

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01380-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kai-Leun Tsai ◽

Che-Chang Chang ◽

Yu-Sheng Chang ◽

Yi-Ying Lu ◽

I-Jung Tsai ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Machine Learning ◽

Disease Activity ◽

Characteristic Curve ◽

Factor H ◽

Protein Adducts ◽

Complement Factor ◽

Increased Risk ◽

Tandem Mass Spectrometric ◽

Peptide Adducts

Abstract Background Rheumatoid arthritis (RA) is an autoimmune disorder with systemic inflammation and may be induced by oxidative stress that affects an inflamed joint. Our objectives were to examine isotypes of autoantibodies against 4-hydroxy-2-nonenal (HNE) modifications in RA and associate them with increased levels of autoantibodies in RA patients. Methods Serum samples from 155 female patients [60 with RA, 35 with osteoarthritis (OA), and 60 healthy controls (HCs)] were obtained. Four novel differential HNE-modified peptide adducts, complement factor H (CFAH)1211–1230, haptoglobin (HPT)78–108, immunoglobulin (Ig) kappa chain C region (IGKC)2–19, and prothrombin (THRB)328–345, were re-analyzed using tandem mass spectrometric (MS/MS) spectra (ProteomeXchange: PXD004546) from RA patients vs. HCs. Further, we determined serum protein levels of CFAH, HPT, IGKC and THRB, HNE-protein adducts, and autoantibodies against unmodified and HNE-modified peptides. Significant correlations and odds ratios (ORs) were calculated. Results Levels of HPT in RA patients were greatly higher than the levels in HCs. Levels of HNE-protein adducts and autoantibodies in RA patients were significantly greater than those of HCs. IgM anti-HPT78−108 HNE, IgM anti-IGKC2−19, and IgM anti-IGKC2−19 HNE may be considered as diagnostic biomarkers for RA. Importantly, elevated levels of IgM anti-HPT78−108 HNE, IgM anti-IGKC2−19, and IgG anti-THRB328−345 were positively correlated with the disease activity score in 28 joints for C-reactive protein (DAS28-CRP). Further, the ORs of RA development through IgM anti-HPT78−108 HNE (OR 5.235, p < 0.001), IgM anti-IGKC2−19 (OR 12.655, p < 0.001), and IgG anti-THRB328−345 (OR 5.761, p < 0.001) showed an increased risk. Lastly, we incorporated three machine learning models to differentiate RA from HC and OA, and performed feature selection to determine discriminative features. Experimental results showed that our proposed method achieved an area under the receiver operating characteristic curve of 0.92, which demonstrated that our selected autoantibodies combined with machine learning can efficiently detect RA. Conclusions This study discovered that some IgG- and IgM-NAAs and anti-HNE M-NAAs may be correlated with inflammation and disease activity in RA. Moreover, our findings suggested that IgM anti-HPT78−108 HNE, IgM anti-IGKC2−19, and IgG anti-THRB328−345 may play heavy roles in RA development.

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38. Development of a Screening Tool to Identify PrEP-Eligible Youth in Primary Care

Journal of Adolescent Health ◽

10.1016/j.jadohealth.2020.12.047 ◽

2021 ◽

Vol 68 (2) ◽

pp. S21-S22

Author(s):

Natalie J. Labossier ◽

Angela R. Bazzi ◽

Kimberly M. Nelson ◽

Eugene S.G. Massey ◽

Julie Potter ◽

...

Keyword(s):

Primary Care ◽

Screening Tool

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