Clinical inertia in hypertension: a new holistic and practical concept within the cardiovascular <i>continuum</i> and clinical care process

Pallarés-Carratalá, Vicente; Bonig-Trigueros, I.; Palazón‐Bru, Antonio; Esteban-Giner, María José; Gil-Guillén, Vicente Francisco; Giner-Galvañ, Vicente

doi:10.1080/08037051.2019.1608134

Cited by 16 publications

(22 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A certain number of patients were found to be failing to monitor glycemia regularly nor receiving timely treatment intensification ( Aujoulat et al, 2014 ; Reach et al, 2017 ; Giugliano et al, 2019 ; Lu et al, 2020 ). Early identification of potential adverse outcomes due to patient nonadherence should be an urgent priority for individualized treatment of T2D ( Zarkogianni et al, 2018 ; Pallarés-Carratalá et al, 2019 ). Therefore, it was necessary to establish a prediction model that could predict the prognosis of nonadherent T2D.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

et al. 2021

View full text Add to dashboard Cite

Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D).Materials and Methods: This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People’s Hospital from January 2010 to December 2015 were collected. The T2D patients who had neither been monitored for glycosylated hemoglobin A nor had changed their hyperglycemia treatment regimens within the last 12 months were the object of this study. Seven types of machine learning algorithms were used to develop 18 prediction models. The predictive performance was mainly assessed using the area under the curve of the testing set.Results: Of 800 T2D patients, 165 (20.6%) met the inclusion criteria, of which 129 (78.2%) had poor glycemic control (defined as glycosylated hemoglobin A ≥7%). The highest area under the curves of the testing set for diabetic nephropathy, diabetic peripheral neuropathy, diabetic angiopathy, diabetic eye disease, and glycosylated hemoglobin A were 0.902 ± 0.040, 0.859 ± 0.050, 0.889 ± 0.059, 0.832 ± 0.086, and 0.825 ± 0.092, respectively.Conclusion: Both univariate analysis and machine learning methods reached the same conclusion. The duration of T2D and the duration of unadjusted hypoglycemic treatment were the key risk factors of diabetic complications, and the number of hypoglycemic drugs was the key risk factor of glycemic control of nonadherent T2D. This was the first study to use machine learning algorithms to explore the potential adverse outcomes of nonadherent T2D. The performances of the final prediction models we developed were acceptable; our prediction performances outperformed most other previous studies in most evaluation measures. Those models have potential clinical applicability in improving T2D care.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

et al. 2021

View full text Add to dashboard Cite

show abstract

“…These findings also underline the need to identify and work on factors interfering with sustained BP control and thereby increasing the visit to visit variability of BP [13], as well as cardiovascular disease risk. Medication adherence [14] and medical inertia [15,16] are recognised as the most important determinants of BP control in the real life management of patients with hypertension [17], including those enrolled in large randomised clinical trials [18]. Even in the context of large randomised controlled outcome trials in hypertension, inertia and the lack of appropriate titration of study drugs to higher doses have been found to be major causes of not reaching BP targets [18].…”

Section: Editorialmentioning

confidence: 99%

Blood pressure target in patients with hypertension and type-2 diabetes older than 65 years. Is <130/80 mmHg the right target or an excessive objective preventing from achieving the clinical goals we are aiming at?

Burnier

Oparil

2021

Blood Pressure

View full text Add to dashboard Cite

“…Clinical inertia occurs when healthcare practitioners (hereafter ‘practitioners’) do not intensify patients’ medication despite raised readings during a consultation [ 1 ] and contributes to sub-optimal hypertension control [ 2 ]. Clinical inertia can be attributed to reluctance to base decisions on one-off clinic readings, low confidence in medication effectiveness, concerns about side effects or patient reluctance to escalate medication, and lack of time during appointments [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Implementing a digital intervention for managing uncontrolled hypertension in Primary Care: a mixed methods process evaluation

et al. 2021

View full text Add to dashboard Cite

Background A high proportion of hypertensive patients remain above the target threshold for blood pressure, increasing the risk of adverse health outcomes. A digital intervention to facilitate healthcare practitioners (hereafter practitioners) to initiate planned medication escalations when patients’ home readings were raised was found to be effective in lowering blood pressure over 12 months. This mixed-methods process evaluation aimed to develop a detailed understanding of how the intervention was implemented in Primary Care, possible mechanisms of action and contextual factors influencing implementation. Methods One hundred twenty-five practitioners took part in a randomised controlled trial, including GPs, practice nurses, nurse-prescribers, and healthcare assistants. Usage data were collected automatically by the digital intervention and antihypertensive medication changes were recorded from the patients’ medical notes. A sub-sample of 27 practitioners took part in semi-structured qualitative process interviews. The qualitative data were analysed using thematic analysis and the quantitative data using descriptive statistics and correlations to explore factors related to adherence. The two sets of findings were integrated using a triangulation protocol. Results Mean practitioner adherence to escalating medication was moderate (53%), and the qualitative analysis suggested that low trust in home readings and the decision to wait for more evidence influenced implementation for some practitioners. The logic model was partially supported in that self-efficacy was related to adherence to medication escalation, but qualitative findings provided further insight into additional potential mechanisms, including perceived necessity and concerns. Contextual factors influencing implementation included proximity of average readings to the target threshold. Meanwhile, adherence to delivering remote support was mixed, and practitioners described some uncertainty when they received no response from patients. Conclusions This mixed-methods process evaluation provided novel insights into practitioners’ decision-making around escalating medication using a digital algorithm. Implementation strategies were proposed which could benefit digital interventions in addressing clinical inertia, including facilitating tracking of patients’ readings over time to provide stronger evidence for medication escalation, and allowing more flexibility in decision-making whilst discouraging clinical inertia due to borderline readings. Implementation of one-way notification systems could be facilitated by enabling patients to send a brief acknowledgement response. Trial registration (ISRCTN13790648). Registered 14 May 2015.

show abstract

Clinical inertia in hypertension: a new holistic and practical concept within the cardiovascular continuum and clinical care process

Cited by 16 publications

References 50 publications

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

Blood pressure target in patients with hypertension and type-2 diabetes older than 65 years. Is <130/80 mmHg the right target or an excessive objective preventing from achieving the clinical goals we are aiming at?

Implementing a digital intervention for managing uncontrolled hypertension in Primary Care: a mixed methods process evaluation

Contact Info

Product

Resources

About