Age and Comorbidities Are Associated With Therapeutic Inertia Among Older Adults With Uncontrolled Blood Pressure

BACKGROUND

Lack of initiation or escalation of blood pressure (BP) lowering medication when BP is uncontrolled, termed therapeutic inertia (TI), increases with age and may be influenced by comorbidities.

METHODS

We examined the association of age and comorbidities with TI in 22,665 visits with a systolic BP ≥140 mm Hg and/or diastolic BP ≥90 mm Hg among 7,415 adults age ≥65 years receiving care in clinics that implemented a hypertension quality improvement program. Generalized linear mixed models were used to determine the association of comorbidity number with TI by age group (65-74 and ≥75 years) after covariate adjustment.

RESULTS

Baseline mean age was 75.0 years (SD 7.8); 41.4% were male. TI occurred in 79.0% and 83.7% of clinic visits in age groups 65-74 and ≥75 years, respectively. In age group 65-74 years, prevalence ratio of TI with 2, 3-4, and ≥5 comorbidities compared with zero comorbidities was 1.07 (95% confidence interval [CI]: 1.04, 1.12), 1.08 (95% CI: 1.05, 1.12), and 1.15 (95% CI: 1.10, 1.20), respectively. The number of comorbidities was not associated with TI prevalence in age group ≥75 years. After implementation of the improvement program, TI declined from 80.3% to 77.2% in age group 65-74 years and from 85.0% to 82.0% in age group ≥75 years (P < 0.001 for both groups).

CONCLUSIONS

TI was common among older adults but not associated with comorbidities after age ≥75 years. A hypertension improvement program had limited impact on TI in older patients.

Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing

BACKGROUND

Artificial intelligence (AI) tools are more effective if accepted by clinicians. We developed an AI-based clinical decision support system (CDSS) to facilitate vancomycin dosing. This qualitative study assesses clinicians' perceptions regarding CDSS implementation.

METHODS

Thirteen semi-structured interviews were conducted with critical care pharmacists, at Mayo Clinic (Rochester, MN), from March through April 2020. Eight clinical cases were discussed with each pharmacist (N = 104). Following initial responses, we revealed the CDSS recommendations to assess participants' reactions and feedback. Interviews were audio-recorded, transcribed, and summarized.

RESULTS

The participants reported considerable time and effort invested daily in individualizing vancomycin therapy for hospitalized patients. Most pharmacists agreed that such a CDSS could favorably affect (N = 8, 62%) or enhance (9, 69%) their ability to make vancomycin dosing decisions. In case-based evaluations, pharmacists' empiric doses differed from the CDSS recommendation in most cases (88/104, 85%). Following revealing the CDSS recommendations, we noted 78% (69/88) discrepant doses. In discrepant cases, pharmacists indicated they would not alter their recommendations. The reasons for declining the CDSS recommendation were general distrust of CDSS, lack of dynamic evaluation and in-depth analysis, inability to integrate all clinical data, and lack of a risk index.

CONCLUSION

While pharmacists acknowledged enthusiasm about the advantages of AI-based models to improve drug dosing, they were reluctant to integrate the tool into clinical practice. Additional research is necessary to determine the optimal approach to implementing CDSS at the point of care acceptable to clinicians and effective at improving patient outcomes.

Image and structured data analysis for prognostication of health outcomes in patients presenting to the ED during the COVID-19 pandemic

BACKGROUND

Patients admitted to the emergency department (ED) with COVID-19 symptoms are routinely required to have chest radiographs and computed tomography (CT) scans. COVID-19 infection has been directly related to the development of acute respiratory distress syndrome (ARDS) and severe infections could lead to admission to intensive care and increased risk of death. The use of clinical data in machine learning models available at time of admission to ED can be used to assess possible risk of ARDS, the need for intensive care (admission to the Intensive Care Unit; ICU) as well as risk of mortality. In addition, chest radiographs can be inputted into a deep learning model to further assess these risks.

PURPOSE

This research aimed to develop machine and deep learning models using both structured clinical data and image data from the electronic health record (EHR) to predict adverse outcomes following ED admission.

MATERIALS AND METHODS

Light Gradient Boosting Machine (LightGBM) was used as the main machine learning algorithm using all clinical data including 42 variables. Compact models were also developed using the 15 most important variables to increase applicability of the models in clinical settings. To predict risk (or early stratified risk) of the aforementioned health outcome events, transfer learning from the CheXNet model was also implemented on the available data. This research utilized clinical data and chest radiographs of 3,571 patients, 18 years and older, admitted to the emergency department between 9th March 2020 and 29th October 2020 at Loyola University Medical Center.

MAIN FINDINGS

The research results show that we can detect COVID-19 infection (AUC = 0.790 (0.746-0.835)), predict the risk of developing ARDS (AUC = 0.781 (0.690-0.872), risk stratification of the need for ICU admission (AUC = 0.675 (0.620-0.713)) and mortality (AUC = 0.759 (0.678-0.840)) at moderate accuracy from both chest X-ray images and clinical data.

PRINCIPAL CONCLUSIONS

The results can help in clinical decision making, especially when addressing ARDS and mortality, during the assessment of patients admitted to the ED with or without COVID-19 symptoms.

CCMapper: An adaptive NLP-based free-text chief complaint mapping algorithm

OBJECTIVE

Chief complaint (CC) is among the earliest health information recorded at the beginning of a patient's visit to an emergency department (ED). We propose a heuristic methodology for automatically mapping the free-text data into a structured list of CCs.

METHODS

A comprehensive structured list categorizing CCs was developed by experienced Emergency Medicine (EM) physicians. Using this list, we developed a natural language processing-based algorithm, referred to as Chief Complaint Mapper (CCMapper), for automatically mapping a CC into the most appropriate category (ies). We trained and validated CCMapper using free-text CC data from the Mayo Clinic ED in Rochester, MN. We developed a consensus-based validation approach to handle both indifferences and disagreements between the two EM physicians who manually mapped a random sample of free-text CCs into categories within the structured list.

RESULTS

The kappa statistic demonstrated a high level of agreement (κ = 0.958) between the two physicians with less than 2% human error. CCMapper achieved a total sensitivity of 94.2% with a specificity of 99.8% and F-score of 94.7% on the validation set. The sensitivity of CCMapper when mapping free-text data with multiple CCs was 82.3% with a specificity of 99.1% and total F-score of 82.3%.

CONCLUSION

Due to its simplicity, high performance, and capability of incorporating new free-text CC data, CCMapper can be readily adopted by other EDs to support clinical decision making. CCMapper can facilitate the development of predictive models for the type and timing of important events in ED (e.g., ICU admission).