scholarly journals Medication Management of Anxiety and Depression by Primary Care Pediatrics Providers: A Retrospective Electronic Health Record Study

2021 ◽  
Author(s):  
Talia Roshini Lester ◽  
Yair Bannett ◽  
Rebecca M. Gardner ◽  
Heidi M. Feldman ◽  
Lynne C. Huffman
Keyword(s):  
Primary Care ◽  
Logistic Regression ◽  
Electronic Health Record ◽  
Regression Models ◽  
Medication Management ◽  
Anxiety And Depression ◽  
Health Record ◽  
Logistic Regression Models ◽  
Medication Prescription ◽  
Anxiety Depression

Objectives: To describe medication management of children diagnosed with anxiety and depression by primary care providers. Study Design/Methods: We performed a retrospective cross-sectional analysis of electronic health record (EHR) structured data. All visits for pediatric patients seen at least twice during a four-year period within a network of primary care clinics in Northern California were included. Descriptive statistics summarized patient variables and most commonly prescribed medications. For each subcohort (anxiety, depression, and both (anxiety+depression)), logistic regression models examined the variables associated with medication prescription. Results: Of all patients (N=93,025), 2.8% (n=2635) had a diagnosis of anxiety only, 1.5% (n=1433) depression only, and 0.79% (n=737) both anxiety and depression (anxiety+depression); 18% of children with anxiety and/or depression had comorbid ADHD. A total of 14.0% with anxiety (n=370), 20.3% with depression (n=291), and 47.5% with anxiety+depression (n=350) received a psychoactive non-stimulant medication. For anxiety only and depression only, sertraline, citalopram, and fluoxetine were most commonly prescribed. For anxiety+depression, citalopram, sertraline, and escitalopram were most commonly prescribed. The top prescribed medications also included benzodiazepines. Logistic regression models showed that older age and having developmental or mental health comorbidities were independently associated with increased likelihood of medication prescription for children with anxiety, depression, and anxiety+depression. Insurance type and sex were not associated with medication prescription. Conclusions: PCPs prescribe medications more frequently for patients with anxiety+depression than for patients with either diagnosis alone. Medication choices generally align with current recommendations. Future research should focus on the use of benzodiazepines due to safety concerns in children.

scholarly journals Use of Electronic Health Records for Early Detection of High-Cost, Low Back Pain Patients

2015 ◽  
Vol 20 (5) ◽  
pp. 234-240 ◽  
Author(s):  
Daniel D Maeng ◽  
Walter F Stewart ◽  
Xiaowei Yan ◽  
Joseph A Boscarino ◽  
Jack Mardekian ◽  
...  
Keyword(s):  
Primary Care ◽  
Logistic Regression ◽  
Low Back Pain ◽  
Back Pain ◽  
Care Provider ◽  
Regression Models ◽  
Low Back ◽  
Health Records ◽  
Logistic Regression Models ◽  
Electronic Health

BACKGROUND: Low back pain (LBP) is a debilitating condition that is complex to manage. One reason is that clinicians lack means to identify early on patients who are likely to become high care utilizers.OBJECTIVE: To explore the feasibility of developing a ‘dynamic’ predictive model using electronic health record data to identify costly LBP patients within the first year after their initial LBP encounter with a primary care provider. Dynamic, in this context, indicates a process in which the decision on how to manage patients is dependent on whether they are at their first, second or third LBP visit with the provider.METHODS: A series of logistic regression models was developed to predict who will be a high-cost patient (defined as top 30% of the cost distribution) at each of the first three LBP visits.RESULTS: The c-statistics of the three logistic regression models corresponding to each of the first three visits were 0.683, 0.795 and 0.741, respectively. The overall sensitivity of the model was 42%, the specificity was 86% and the positive predictive value was 48%. Men were more likely to become expensive than women, while patients who had workers’ compensation as their primary payer type had higher use of prescription opioid drugs or were smokers before the first LBP visit were also more likely to become expensive.CONCLUSION: The results suggest that it is feasible to develop a dynamic, primary care provider visit-based predictive model for LBP care based on longitudinal data obtained via electronic health records.

scholarly journals A-140 Single-Item Screening for Anxiety and Depression using the Neurobehavioral Symptom Inventory

2020 ◽  
Vol 35 (6) ◽  
pp. 933-933
Author(s):  
Rolin S ◽  
Kitchen Andren K ◽  
Mullen C ◽  
Kurniadi N ◽  
Davis J
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Anxiety And Depression ◽  
Average Score ◽  
Characteristic Analysis ◽  
Logistic Regression Models ◽  
Symptom Inventory

Abstract Objective Previous research in a Veterans Affairs sample proposed using single items on the Neurobehavioral Symptom Inventory (NSI) to screen for anxiety (item 19) and depression (item 20). This study examined the approach in an outpatient physical medicine and rehabilitation sample. Method Participants (N = 84) underwent outpatient neuropsychological evaluation using the NSI, BDI-II, GAD-7, MMPI-2-RF, and Memory Complaints Inventory (MCI) among other measures. Anxiety and depression were psychometrically determined via cutoffs on the GAD-7 (>4) and MMPI-2-RF ANX (>64 T), and BDI-II (>13) and MMPI-2-RF RC2 (>64 T), respectively. Analyses included receiver operating characteristic analysis (ROC) and logistic regression. Logistic regression models used dichotomous anxiety and depression as outcomes and relevant NSI items and MCI average score as predictors. Results ROC analysis using NSI items to classify cases showed area under the curve (AUC) values of .77 for anxiety and .85 for depression. The logistic regression model predicting anxiety correctly classified 80% of cases with AUC of .86. The logistic regression model predicting depression correctly classified 79% of cases with AUC of .88. Conclusion Findings support the utility of NSI anxiety and depression items as screening measures in a rehabilitation population. Consideration of symptom validity via the MCI improved classification accuracy of the regression models. The approach may be useful in other clinical settings for quick assessment of psychological issues warranting further evaluation.

scholarly journals Anxiety and Depression in Health Workers and General Population During COVID-19 Epidemic in IRAN: A Web-Based Cross-Sectional Study

2020 ◽  
Author(s):  
Leila Hassannia ◽  
Fatemeh Taghizadeh ◽  
Mahmood Moosazadeh ◽  
Mehran Zarghami ◽  
Hassan Taghizadeh ◽  
...  
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Regression Models ◽  
Health Workers ◽  
Cross Sectional Study ◽  
Anxiety And Depression ◽  
Depression Symptoms ◽  
Sectional Study ◽  
Cross Sectional ◽  
Logistic Regression Models

ABSTRACTBackgroundThe COVID-19 outbreak has exerted a great deal of psychological pressure on Iranian health workers and the general population. In the present study, the prevalence of anxiety and depression symptoms along with the related variables in this epidemic were investigated.MethodAn online cross-sectional study was conducted for the general public and healthcare workers in IRAN using a questionnaire comprised of demographic questions and Hospital Anxiety and Depression Scale. Chi square test was used to compare categorical variables, and univariate and multivariate logistic regression models were conducted.ResultsOf the 2045 participants,1136 (65.6%) were considered to have moderate and severe anxiety symptoms, and 865(42.3%) had moderate and severe depression symptoms. Based on the logistic regression models, the prevalence of anxiety was higher in the females than in the males (OR=1.4, 95% CI: 1.123-1.643, P=.002); the prevalence of anxiety was significantly higher in those aged 30-39 years than in other age groups (OR=1.6, 95% CI: 1.123-2.320, P=0.001); furthermore, the prevalence of anxiety and depression was significantly higher in doctors and nurses compared with other occupations (OR=1.9, 95% CI: 1.367-2.491, P< 0.001) and(OR=1.5, 95% CI: 1.154-2.021, P=0.003). In addition, the prevalence of anxiety symptoms in the likely-infected COVID-19 group was higher than in the noninfected COVID-19 group (OR=1.35, 95% CI: 1.093-1.654, P=0.005).ConclusionsRegarding the high prevalence of anxiety and depression symptoms, especially among health care workers, appropriate psychological/psychiatric intervention necessitates.

scholarly journals Electronic Cigarette Use Is Not Associated with COVID-19 Diagnosis

2021 ◽  
Vol 12 ◽  
pp. 215013272110243
Author(s):  
Thulasee Jose ◽  
Ivana T. Croghan ◽  
J. Taylor Hays ◽  
Darrell R. Schroeder ◽  
David O. Warner
Keyword(s):  
Logistic Regression ◽  
Cigarette Smoking ◽  
Medical Care ◽  
Mayo Clinic ◽  
Regression Models ◽  
Health Record ◽  
Cigarette Use ◽  
Logistic Regression Models ◽  
Beneficial Effects ◽  
Increased Risk

This analysis tested the hypothesis that current e-cigarette use was associated with an increased risk of SARS-CoV-2 infection in patients seeking medical care. E-cigarette and conventional cigarette use were ascertained using a novel electronic health record tool, and COVID-19 diagnosis was ascertained by a validated institutional registry. Logistic regression models were fit to assess whether current e-cigarette use was associated with an increased risk of COVID-19 diagnosis. A total of 69,264 patients who were over the age of 12 years, smoked cigarettes or vaped, and were sought medical care at Mayo Clinic between September 15, 2019 and November 30, 2020 were included. The average age was 51.5 years, 62.1% were females and 86.3% were white; 11.1% were currently smoking cigarettes or using e-cigarettes and 5.1% tested positive for SARS-CoV-2. Patients who used only e-cigarettes were not more likely to have a COVID-19 diagnosis (OR 0.93 [0.69-1.25], P = .628), whereas those who used only cigarettes had a decreased risk (OR 0.43 [0.35-0.53], P < .001). The OR for dual users fell between these 2 values (OR 0.67 [0.49-0.92], P = .013). Although e-cigarettes have the well-documented potential for harm, they do not appear to increase susceptibility to SARS-CoV-2 infection. This result suggests the hypothesis that any beneficial effects of conventional cigarette smoking on susceptibility are not mediated by nicotine.

Variability in Electronic Health Record Use for Medication Management in Primary Care (Preprint)

2018 ◽  
Author(s):  
Enid Montague
Keyword(s):  
Primary Care ◽  
Electronic Health Record ◽  
Review Process ◽  
Medication Management ◽  
Controlled Environment ◽  
Health Record ◽  
Coding Scheme ◽  
Primary Care Settings ◽  
Electronic Health ◽  
Acute Condition

UNSTRUCTURED Background: Traditional medication management complexity now combined with EHR systems, which are still novel in primary care settings, propose new challenges in trying to improve physician workflow. Objective: The purpose of this study was to understand how workflow variability in medication management using an electronic health record (EHR) system is related to patient and physician factors. Methods: Two different patient cases (chronic vs. acute condition) were presented to participants in a controlled environment. A task action coding scheme was used to analyze the videotaped data from the physician’s EHR usage. A usability survey was administered after the task. Results: High variability in the medication review process and EHR perceptions were revealed. Patient conditions and physicians’ EHR perceptions were related to the found variability in the workflow. Conclusions: Interventions designed to improve EHR medication management in primary care should consider alignment with physician’s varied workflow linked with their perceptions and differing patient condition.

scholarly journals Postoperative delirium prediction using machine learning models and preoperative electronic health record data

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Andrew Bishara ◽  
Catherine Chiu ◽  
Elizabeth L. Whitlock ◽  
Vanja C. Douglas ◽  
Sei Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Risk Stratification ◽  
Electronic Health Record ◽  
Regression Models ◽  
Postoperative Delirium ◽  
Anesthesia Care ◽  
Health Record ◽  
Neural Net ◽  
Preoperative Risk

Abstract Background Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression. Methods This was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models (“clinician-guided” and “ML hybrid”), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded. Results POD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 [95% CI 0. 816–0.863] and for XGBoost was 0.851 [95% CI 0.827–0.874], which was significantly better than the clinician-guided (AUC-ROC 0.763 [0.734–0.793], p < 0.001) and ML hybrid (AUC-ROC 0.824 [0.800–0.849], p < 0.001) regression models and AWOL-S (AUC-ROC 0.762 [95% CI 0.713–0.812], p < 0.001). Neural Net, XGBoost, and ML hybrid models demonstrated excellent calibration, while calibration of the clinician-guided and AWOL-S models was moderate; they tended to overestimate delirium risk in those already at highest risk. Conclusion Using pragmatically collected EHR data, two ML models predicted POD in a broad perioperative population with high discrimination. Optimal application of the models would provide automated, real-time delirium risk stratification to improve perioperative management of surgical patients at risk for POD.

The impact of SPY angiography on intraoperative decision making and outcomes for post-mastectomy reconstruction

2019 ◽  
pp. 1-5
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Skin Necrosis ◽  
Regression Models ◽  
Multivariable Analysis ◽  
P Value ◽  
Flap Necrosis ◽  
Single Institution ◽  
Logistic Regression Models ◽  
The Impact

Objective: While the use of intraoperative laser angiography (SPY) is increasing in mastectomy patients, its impact in the operating room to change the type of reconstruction performed has not been well described. The purpose of this study is to investigate whether SPY angiography influences post-mastectomy reconstruction decisions and outcomes. Methods and materials: A retrospective analysis of mastectomy patients with reconstruction at a single institution was performed from 2015-2017.All patients underwent intraoperative SPY after mastectomy but prior to reconstruction. SPY results were defined as ‘good’, ‘questionable’, ‘bad’, or ‘had skin excised’. Complications within 60 days of surgery were compared between those whose SPY results did not change the type of reconstruction done versus those who did. Preoperative and intraoperative variables were entered into multivariable logistic regression models if significant at the univariate level. A p-value <0.05 was considered significant. Results: 267 mastectomies were identified, 42 underwent a change in the type of planned reconstruction due to intraoperative SPY results. Of the 42 breasts that underwent a change in reconstruction, 6 had a ‘good’ SPY result, 10 ‘questionable’, 25 ‘bad’, and 2 ‘had areas excised’ (p<0.01). After multivariable analysis, predictors of skin necrosis included patients with ‘questionable’ SPY results (p<0.01, OR: 8.1, 95%CI: 2.06 – 32.2) and smokers (p<0.01, OR:5.7, 95%CI: 1.5 – 21.2). Predictors of any complication included a change in reconstruction (p<0.05, OR:4.5, 95%CI: 1.4-14.9) and ‘questionable’ SPY result (p<0.01, OR: 4.4, 95%CI: 1.6-14.9). Conclusion: SPY angiography results strongly influence intraoperative surgical decisions regarding the type of reconstruction performed. Patients most at risk for flap necrosis and complication post-mastectomy are those with questionable SPY results.

scholarly journals Effect of prostatic apex shape (Lee types) and urethral sphincter length in preoperative MRI on very early continence rates after radical prostatectomy

2021 ◽  
Author(s):  
Mike Wenzel ◽  
Felix Preisser ◽  
Matthias Mueller ◽  
Lena H. Theissen ◽  
Maria N. Welte ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Regression Models ◽  
Urethral Sphincter ◽  
Anatomical Characteristics ◽  
Logistic Regression Models ◽  
Pad Test ◽  
Type D ◽  
Multivariable Logistic Regression ◽  
Type C

Abstract Purpose To test the effect of anatomic variants of the prostatic apex overlapping the membranous urethra (Lee type classification), as well as median urethral sphincter length (USL) in preoperative multiparametric magnetic resonance imaging (mpMRI) on the very early continence in open (ORP) and robotic-assisted radical prostatectomy (RARP) patients. Methods In 128 consecutive patients (01/2018–12/2019), USL and the prostatic apex classified according to Lee types A–D in mpMRI prior to ORP or RARP were retrospectively analyzed. Uni- and multivariable logistic regression models were used to identify anatomic characteristics for very early continence rates, defined as urine loss of ≤ 1 g in the PAD-test. Results Of 128 patients with mpMRI prior to surgery, 76 (59.4%) underwent RARP vs. 52 (40.6%) ORP. In total, median USL was 15, 15 and 10 mm in the sagittal, coronal and axial dimensions. After stratification according to very early continence in the PAD-test (≤ 1 g vs. > 1 g), continent patients had significantly more frequently Lee type D (71.4 vs. 54.4%) and C (14.3 vs. 7.6%, p = 0.03). In multivariable logistic regression models, the sagittal median USL (odds ratio [OR] 1.03) and Lee type C (OR: 7.0) and D (OR: 4.9) were independent predictors for achieving very early continence in the PAD-test. Conclusion Patients’ individual anatomical characteristics in mpMRI prior to radical prostatectomy can be used to predict very early continence. Lee type C and D suggest being the most favorable anatomical characteristics. Moreover, longer sagittal median USL in mpMRI seems to improve very early continence rates.

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