Inappropriate prescribing and association with readmission or mortality in hospitalised older adults with frailty: a systematic review and meta-analysis.

BACKGROUND: Inappropriate prescribing (IP) is common in hospitalised older adults with frailty. However, it is not known whether the presence of frailty confers an increased risk of mortality and readmissions from IP nor whether rectifying IP reduces this risk. This review was conducted to determine whether IP increases the risk of adverse outcomes in hospitalised middle-aged and older adults with frailty. METHODS: A systematic review was conducted on IP in hospitalised middle-aged (45-64 years) and older adults (≥ 65 years) with frailty. This review considered multiple types of IP including potentially inappropriate medicines, prescribing omissions and drug interactions. Both observational and interventional studies were included. The outcomes were mortality and hospital readmissions. The databases searched included MEDLINE, CINAHL, EMBASE, World of Science, SCOPUS and the Cochrane Library. The search was updated to 12 July 2024. Meta-analysis was performed to pool risk estimates using the random effects model. RESULTS: A total of 569 studies were identified and seven met the inclusion criteria, all focused on the older population. One of the five observational studies found an association between IP and emergency department visits and readmissions at specific time points. Three of the observational studies were amenable to meta-analysis which showed no significant association between IP and hospital readmissions (OR 1.08, 95% CI 0.90-1.31). Meta-analysis of the subgroup assessing Beers criteria medicines demonstrated that there was a 27% increase in the risk of hospital readmissions (OR 1.27, 95% CI 1.03-1.57) with this type of IP. In meta-analysis of the two interventional studies, there was a 37% reduced risk of mortality (OR 0.63, 95% CI 0.40-1.00) with interventions that reduced IP compared to usual care but no difference in hospital readmissions (OR 0.83, 95% CI 0.19-3.67). CONCLUSIONS: Interventions to reduce IP were associated with reduced risk of mortality, but not readmissions, compared to usual care in older adults with frailty. The use of Beers criteria medicines was associated with hospital readmissions in this group. However, there was limited evidence of an association between IP more broadly and mortality or hospital readmissions. Further high-quality studies are needed to confirm these findings.

The Clinical Impact of Hospital-Acquired Adverse Drug Reactions in Older Adults: An Australian Cohort Study.

INTRODUCTION: Hospital-acquired adverse drug reactions (HA-ADRs) are common in older adults. However, there is limited knowledge regarding the association between HA-ADRs and adverse clinical outcomes. OBJECTIVE: To investigate the incidence and characteristics of HA-ADRs in older adults, and any association with mortality, length of stay, and readmissions. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: Flinders Medical Centre, a large tertiary referral hospital in Adelaide, South Australia. Older adults admitted under the General Medicine and Acute Care of the Elderly units with no previous diagnosis of dementia. METHODS: All patients had a Multidimensional Prognostic Index (MPI) assessment performed within 3 days of the admission. Data collected included age, gender, estimated glomerular filtration rate (eGFR), length of stay, readmissions, and mortality. HA-ADRs were identified by review of individual discharge summaries. Univariate and multivariate analyses were performed to investigate associations with clinical outcomes including mortality, length of stay, and readmissions. Exploratory analyses were performed for HA-ADR groups based on Medical Dictionary for Regulatory Activities System Organ Class and World Health Organization Anatomical Therapeutic Chemical classifications that accounted for ≥10% of all HA-ADRs. RESULTS: There were 737 patients in the cohort with 72 having experienced a HA-ADRs (incidence = 9.8%). Patients with an HA-ADR had increased length of stay and 30-day readmissions compared with those without an HA-ADR. In multivariate analysis, the number of HA-ADRs was associated with in-hospital mortality and length of stay but not post-discharge mortality or readmissions within 30 days. In exploratory analyses, patients with an HA-ADR to antibacterial drugs had significantly higher rates of in-hospital mortality compared with those without these reactions. CONCLUSIONS AND IMPLICATIONS: The number of HA-ADRs are associated with in-hospital mortality and length of stay in older Australian inpatients. The occurrence of HA-ADRs may be a trigger to offer advice to prescribers to prevent future ADRs to similar agents and proactively manage disease to improve health outcomes.

Association of Drug-Disease Interactions with Mortality or Readmission in Hospitalised Middle-Aged and Older Adults: A Systematic Review and Meta-Analysis.

BACKGROUND AND OBJECTIVE: Multimorbidity is common in hospitalised adults who are at increased risk of inappropriate prescribing including drug-disease interactions. These interactions occur when a medicine being used to treat one condition exacerbates a concurrent medical condition and may lead to adverse health outcomes. The aim of this review was to examine the association between drug-disease interactions and the risk of mortality and readmission in hospitalised middle-aged and older adults. METHODS: A systematic review was conducted on drug-disease interactions in hospitalised middle-aged (45-64 years) and older adults (≥65 years). The study protocol was prospectively registered with PROSPERO (Registration Number: CRD42022341998). Drug-disease interactions were defined as a medicine being used to treat one condition with the potential to exacerbate a concurrent medical condition or that were inappropriate based on a comorbid medical condition. Both observational and interventional studies were included. The outcomes of interest were mortality and readmissions. The databases searched included MEDLINE, CINAHL, EMBASE, Web of Science, SCOPUS and the Cochrane Library from inception to 12 July, 2022. A meta-analysis was performed to pool risk estimates using the random-effects model. RESULTS: A total of 563 studies were identified and four met the inclusion criteria. All were observational studies in older adults, with no studies identified in middle-aged adults. Most of the studies were at risk of bias because of an inadequate adjustment for covariates and a lack of clarity around individuals lost to follow-up. There were various definitions of drug-disease interactions within these four studies. Two studies assessed drugs that were contraindicated based on renal function, one assessed an individual drug-disease combination, and one was based on the clinical judgement of a pharmacist. There were two studies that showed an association between drug-disease interactions and the outcomes of interest. One reported that the use of diltiazem in patients with heart failure was associated with an increased risk of readmissions. The second reported that the use of medicines contraindicated according to renal function were associated with increased risk of all-cause mortality and a composite of mortality and readmission. Three of the studies (total study population = 5705) were amenable to a meta-analysis, which showed no significant association between drug-disease interactions and readmissions (odds ratio = 1.0, 95% confidence interval 0.80-1.38). CONCLUSIONS: Few studies were identified examining the risk of drug-disease interactions and mortality and readmission in hospitalised adults. Most of the identified studies were at risk of bias. There is no universal accepted definition of drug-disease interactions in the literature. Further studies are needed to develop a standardised and accepted definition of these interactions to guide further research in this area.

Improving the performance of machine learning penicillin adverse drug reaction classification with synthetic data and transfer learning.

BACKGROUND: Machine learning may assist with the identification of potentially inappropriate penicillin allergy labels. Strategies to improve the performance of existing models for this task include the use of additional training data, synthetic data and transfer learning. AIMS: The aims of this study were to investigate the use of additional training data and novel machine learning strategies, namely synthetic data and transfer learning, to improve the performance of penicillin adverse drug reaction (ADR) machine learning classification. METHODS: Machine learning natural language processing was applied to free-text penicillin ADR data extracted from a public health system electronic health record (EHR). The models were developed by training on various labelled data sets. ADR entries were split into training and testing data sets and used to develop and test a variety of machine learning models. The effect of training on additional data and synthetic data versus the use of transfer learning was analysed. RESULTS: Following the application of these techniques, the area under the receiver operator curve of best-performing models for the classification of penicillin allergy (vs intolerance) and high-risk allergy (vs low-risk allergy) improved to 0.984 (using the artificial neural network model) and 0.995 (with the transfer learning approach) respectively. CONCLUSIONS: Machine learning models demonstrate high levels of accuracy in the classification and risk stratification of penicillin ADR labels using the reaction documented in the EHR. The model can be further optimised by incorporating additional training data and using transfer learning. Practical applications include automating case detection for penicillin allergy delabelling programmes.

SGLT2 Inhibitor-Associated Ketoacidosis vs Type 1 Diabetes-Associated Ketoacidosis.

This cohort study examines the natural history and response to treatment of sodium glucose cotransporter 2 (SGLT2) inhibitor­associated ketoacidosis compared with that of type 1 diabetes­associated ketoacidosis.

Antibiotic prophylaxis in immunosuppressed patients - Missed opportunities from trimethoprim-sulfamethoxazole allergy label.

Trimethoprim-sulfamethoxazole (TMP-SMX) is a broad spectrum antibiotic in use for more than 50 years. It has an important indication as first line agent in the prophylaxis of opportunistic infections, particularly Pneumocystis jirovecii pneumonia (PJP), in immunosuppressed patients. For those who have a history of allergy or severe intolerance to TMP-SMX, pentamidine, dapsone or atovaquone may be substituted; however there is evidence that TMP-SMX offers superior coverage for PJP, toxoplasmosis, and nocardiosis. Compared to pentamidine, it has the added benefit of cost-effectiveness and self-administration as opposed to required hospital attendance for administration. Many patients who report a history of allergy or adverse reaction to TMP-SMX (or "sulfur allergy") will be found not to be allergic; and even those who are allergic may be able to be desensitized. The evaluation and, where appropriate, removal of TMP-SMX allergy label enables the use of TMP-SMX for prophylaxis against opportunistic infections. This is a cost-effective intervention to optimize antimicrobial prescribing and reduce the risk of opportunistic infections in immunosuppressed patients.

Prescribing of antivirals for COVID-19 in a South Australian local health network according to statewide guidelines.

Antiviral drugs were rapidly implemented into clinical practice for the treatment of high-risk patients with COVID-19, prompting the development of statewide guidelines. This South-Australian study reviewed guideline adherence, assessed prescribing patterns and highlighted the inappropriate management of relative drug-drug interactions and dosing for renal function. Additionally, it evaluated the impact of inappropriate antiviral drug use and suggested methods to improve quality use of medicines.

Artificial intelligence-enabled penicillin allergy delabelling: an implementation study.

Inaccurate penicillin allergy labels may be delabelled following evaluation. The intervention in this study was an email-based notification system regarding the appropriateness for penicillin allergy evaluation, with a view to delabelling, as identified by a deep learning artificial intelligence algorithm. Of the intervention group (n = 59), three (5.1%) individuals had their penicillin allergies delabelled, which was significantly more than the control group (0%, P = 0.002). Further research to optimise such approaches is required.

A community of practice to address system-based issues and promote clinical leadership among trainee medical officers in a large public health service: an evaluation of a trainee-led forum.

Clinical leadership is necessary to improve the performance of large public hospitals. Trainee medical officers (TMOs) are important stakeholders in organisation-wide initiatives that affect the medical workforce and support clinician engagement. This case study describes the development of a representative body known as the 'TMO Forum' within the Central Adelaide Local Health Network as a mechanism to promote engagement between medical trainees and the hospital executive to facilitate escalation and discussion of system-based issues. Over the past 8 years, this group has evolved into a community of practice with steady and sustained growth since inception. Trainees have fostered relationships with the executive, and have engaged in leadership and quality improvement initiatives. Here we explore the evolution, value and barriers to success of the TMO Forum. Our discussion is supplemented with findings from anonymous online evaluation surveys of both the TMO and executive stakeholder groups. We propose that initiatives such as the described may offer reciprocal benefits to both constituent groups regarding communication, and that the development of a dedicated community of practice will enhance engagement of TMOs in health service improvement initiatives and advocacy. However, there are obstacles to overcome in order to attract a greater number of trainees and maximise the benefits from this initiative.

Protecting the heart.

Blocking a protein known as EPAC1 may prevent the development of heart-related side effects caused by a chemotherapy drug.

Prevalence of Trimethoprim-Sulfamethoxazole Adverse Reaction Mislabelling in Australia.

INTRODUCTION: Trimethoprim-sulfamethoxazole (TMP-SMX) is an important antibiotic, with the most compelling indications for Pneumocystis jirovecii pneumonia prophylaxis and methicillin-resistant Staphylococcus aureus treatment. Previous adverse reactions (AR) to TMP-SMX may limit the usability of TMP-SMX. Electronic medical record (EMR) of AR for other antibiotics has previously been shown to be inaccurate; however, the extent to which this occurs for TMP-SMX is unknown. METHODS: A multi-centre retrospective observational study was conducted for consecutive inpatient admissions over a 2.5-year period commencing 2020. Adverse reactions to TMP-SMX recorded in the EMR were collected and reviewed by two independent medical officers using pre-defined expert criteria for the classification of allergies and intolerances. RESULTS: TMP-SMX AR were present in the EMR of 759 individuals (prevalence 0.6%). The majority were labelled as allergy (725, 95.5%) rather than intolerance (34, 4.5%). Most common AR were rash, vomiting, and swelling. When classified against the gold-standard expert criteria, there were 437 allergies (57.6%) and 159 intolerances (21.0%). Overall, the number of incorrect EMR AR labels was 133/759 (17.5%). Both medical and surgical specialties had significant numbers of patients with TMP-SMX AR labels and incorrectly classified EMR AR labels. CONCLUSION: TMP-SMX AR labels affect inpatients admitted under multiple specialty units. The user-entered categorization as allergy or intolerance labels in EMRs are frequently used incorrectly. These incorrect labels may inappropriately contraindicate the use of TMP-SMX, and formal evaluation of TMP-SMX ARs with immunological assessment and relabelling where appropriate may increase the use of this agent.

Machine learning models automate classification of penicillin adverse drug reaction labels.

There is a growing interest in the appropriate evaluation of penicillin adverse drug reaction (ADR) labels. We have developed machine learning models for classifying penicillin ADR labels using free-text reaction descriptions, and here report external and practical validation. The models performed comparably with expert criteria for the categorisation of allergy or intolerance and identification of high-risk allergies. These models have practical applications in detecting individuals suitable for penicillin ADR evaluation. Implementation studies are required.

Peri-colonoscopy Implications of Sodium-Glucose Cotransporter-2 Inhibitor Therapy: A Mini-review of Available Evidence.

Sodium-glucose cotransporter-2 inhibitors (SGLT2i) are a class of oral glucose-lowering agents commonly used for the treatment of type 2 diabetes. With increased use, there has been an increase in the incidence of the rare but life-threatening complication of euglycemic diabetic ketoacidosis. A common but underappreciated precipitant is colonoscopy. In this work, we outline the pathophysiology of the interaction between colonoscopy and SGLT2i use, the evidence regarding SGLT2i use in the periprocedural setting and Australian Diabetes Society guidelines.

Artificial intelligence and the potential for perioperative delabeling of penicillin allergies for neurosurgery inpatients.

PURPOSE OF THE ARTICLE: Patients with penicillin allergy labels are more likely to have postoperative wound infections. When penicillin allergy labels are interrogated, a significant number of individuals do not have penicillin allergies and may be delabeled. This study was conducted to gain preliminary evidence into the potential role of artificial intelligence in assisting with perioperative penicillin adverse reaction (AR) evaluation. MATERIAL AND METHODS: A single-centre retrospective cohort study of consecutive emergency and elective neurosurgery admissions was conducted over a two-year period. Previously derived artificial intelligence algorithms for the classification of penicillin AR were applied to the data. RESULTS: There were 2063 individual admissions included in the study. The number of individuals with penicillin allergy labels was 124; one patient had a penicillin intolerance label. Of these labels, 22.4% were not consistent with classifications using expert criteria. When the artificial intelligence algorithm was applied to the cohort, the algorithm maintained a high level of classification performance (classification accuracy 98.1% for allergy versus intolerance classification). CONCLUSIONS: Penicillin allergy labels are common among neurosurgery inpatients. Artificial intelligence can accurately classify penicillin AR in this cohort, and may assist in identifying patients suitable for delabeling.

Antibiotic Prescribing Practices Differ between Patients with Penicillin Intolerance and Penicillin Allergy Labels.

INTRODUCTION: Penicillin allergy labels are common. However, many penicillin allergy labels have been applied incorrectly and in fact represent penicillin intolerance. Patients with penicillin intolerance can receive penicillin antibiotics. The effect of penicillin intolerance labels on prescribing practices is uncertain. METHODS: This multicenter retrospective cohort study included consecutive general medicine patients admitted to two tertiary hospitals over a 12-month period. Electronic medical records were reviewed for allergy and prescribing practices. Instances of penicillin prescription to patients with previously labeled penicillin allergies underwent case note review. RESULTS: There were 12,134 individual hospital admissions included in the study. The number of admissions with a previous penicillin allergy label was 1,312 (10.8%) and with a penicillin intolerance label was 60 (0.5%). Penicillin allergy labels were associated with increased likelihood of being prescribed vancomycin (odds ratio 1.42, 95% confidence interval 1.16-1.75, p = 0.001) and moxifloxacin (odds ratio 20.0, 95% confidence interval 13.4-29.9, p < 0.001). Penicillin intolerance was not associated with increased likelihood of receiving these antibiotics. There were 75 admissions during which an individual with a penicillin allergy label was prescribed one of the specified penicillins and only one adverse reaction in this group. These cases included eight deliberate challenges and 15 cases in which allergy history clarification was sufficient to delabel the allergy. CONCLUSIONS: This study supports that prescribing practices differ between patients with penicillin allergy labels and intolerance labels. Penicillin challenges may be undertaken safely in the inpatient setting. Further studies are required to investigate how best to interrogate penicillin allergy labels in this cohort.

Noninvasive respiratory supports for the relief of terminal breathlessness.

PURPOSE OF REVIEW: Breathlessness is a common symptom in patients with respiratory failure in the terminal phase of their illness. Noninvasive methods of oxygen delivery are frequently used in the palliative setting. We review the evidence supporting noninvasive respiratory supports for the relief of terminal breathlessness in those with life-limiting illnesses. RECENT FINDINGS: There is limited evidence to support the use of supplemental oxygen for patients without hypoxia. It is unclear whether the symptomatic benefit of oxygen therapy relates to the oxygen delivery and/or airflow across the nasal mucosa. Early trials suggest that high-flow nasal cannula (HFNC) oxygen therapy improves breathlessness at rest and on exertion for patients with cancer. Noninvasive ventilation (NIV) also appears to improve breathlessness in the palliative setting; however, potential harms include facial pressure injuries, claustrophobia and anxiety. Goals of care should be explicitly discussed and frequently reviewed given that these interventions have the potential for harm and can be challenging to withdraw. SUMMARY: HFNC oxygen therapy and NIV appear to reduce breathlessness in the palliative setting. Further high-quality trials are needed to confirm the symptomatic benefits of noninvasive respiratory supports on breathlessness for patients with cancer.

Automation of penicillin adverse drug reaction categorisation and risk stratification with machine learning natural language processing.

BACKGROUND: The penicillin adverse drug reaction (ADR) label is common in electronic health records (EHRs). However, there is significant misclassification between allergy and intolerance within the EHR and most patients can be delabelled after an immunologic assessment. Machine learning natural language processing may be able to assist with the categorisation and risk stratification of penicillin ADRs. OBJECTIVE: The aim of this study was to use text entered into an EHR to derive and evaluate machine learning models to classify penicillin ADRs and assess the risk of true allergy. METHODS: Machine learning natural language processing was applied to free-text penicillin ADR data extracted from a public health system EHR. The model was developed by training on labelled dataset. ADR entries were split into training and testing datasets and used to develop and test a variety of machine learning models. These were compared to categorisation with a simple algorithm using keyword search. RESULTS: The best performing model for the classification of penicillin ADRs as being consistent with allergy or intolerance was the artificial neural network (AUC 0.994, sensitivity 0.99, specificity 0.96). The artificial neural network also achieved the highest AUC in the classification of high- or low-risk of true allergy (AUC 0.988, sensitivity 0.99, specificity 0.99). All ADR labels were able to be classified using these machine learning models, whereas a small proportion were unclassifiable using the simple algorithm as they contained no keywords. CONCLUSION: Machine learning natural language processing performed similarly to expert criteria in classifying and risk stratifying penicillin ADRs labels. These models outperformed simpler algorithms in their ability to interpret free-text data contained in the EHR. The automated evaluation of penicillin ADR labels may allow real-time risk stratification to facilitate delabelling and improve the specificity of prescribing alerts.