Risk factors, outcomes and healthcare utilisation in individuals with multimorbidity including heart failure, chronic kidney disease and type 2 diabetes mellitus: a national electronic health record study

BACKGROUND

Heart failure (HF), type 2 diabetes (T2D) and chronic kidney disease (CKD) commonly coexist. We studied characteristics, prognosis and healthcare utilisation of individuals with two of these conditions.

METHODS

We performed a retrospective, population-based linked electronic health records study from 1998 to 2020 in England to identify individuals diagnosed with two of: HF, T2D or CKD. We described cohort characteristics at time of second diagnosis and estimated risk of developing the third condition and mortality using Kaplan-Meier and Cox regression models. We also estimated rates of healthcare utilisation in primary care and hospital settings in follow-up.

FINDINGS

We identified cohorts of 64 226 with CKD and HF, 82 431 with CKD and T2D, and 13 872 with HF and T2D. Compared with CKD and T2D, those with CKD and HF and HF and T2D had more severe risk factor profile. At 5 years, incidence of the third condition and all-cause mortality occurred in 37% (95% CI: 35.9%, 38.1%%) and 31.3% (30.4%, 32.3%) in HF+T2D, 8.7% (8.4%, 9.0%) and 51.6% (51.1%, 52.1%) in HF+CKD, and 6.8% (6.6%, 7.0%) and 17.9% (17.6%, 18.2%) in CKD+T2D, respectively. In each of the three multimorbid groups, the order of the first two diagnoses was also associated with prognosis. In multivariable analyses, we identified risk factors for developing the third condition and mortality, such as age, sex, medical history and the order of disease diagnosis. Inpatient and outpatient healthcare utilisation rates were highest in CKD and HF, and lowest in CKD and T2D.

INTERPRETATION

HF, CKD and T2D carry significant mortality and healthcare burden in combination. Compared with other disease pairs, individuals with CKD and HF had the most severe risk factor profile, prognosis and healthcare utilisation. Service planning, policy and prevention must take into account and monitor data across conditions.

Linkage of Primary Care Prescribing Records and Pharmacy Dispensing Records in Asthma Controller Medications

Background In the UK, issued prescriptions are typically taken to pharmacies, where medications are prepared, recorded, and dispensed. Data Linkage between prescribing and pharmacy dispensing records is not routinely conducted at the individual prescription level for clinical care in England and Wales, however it can be particularly useful for the study of pharmacoepidemiology. With no unique prescribing event identifiers between records, an algorithmic approach is required for this linkage. Aims To create a linkage system for primary care prescribed asthma controller medications and pharmacy dispensing records. Methods Free text labels were used to populate fields for data linkage, relating to medication strength, medication type (active ingredients; allows matching of generic substitutions to named brands), doses per medication unit, prescribed units, and prescribed doses. Prescribing and dispensing records were merged using an inner (many to many) join; generating a candidate link for every combination of records matching on unique patient identifier and medicine. A recursive algorithm was developed and applied, working backwards chronologically through dispensing records and finding the most appropriate match based on the time since prescribing and agreement between the medication description fields. Unmatched records were assessed for quality assurance, and the distribution of linkage strength for matches was examined. Results We developed a harmonisation algorithm in a dataset of over 3 million asthma controller medication prescription records, for which almost 3 in 4 were coded according to the number of units (predominantly inhalers). Incorporating the estimated number of doses prescribed/dispensed into our wider matching algorithm, we were able to find unique prescription records for almost 95% of our dispensing records. Conclusion Early findings demonstrate the accuracy of the developed algorithm linking prescribing and dispensing records. This algorithm can easily be generalised to other conditions.

Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model

INTRODUCTION

Asthma is a long-term condition with rapid onset worsening of symptoms ('attacks') which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificity to avoid unnecessary prescribing of preventative medications that carry an associated risk of adverse events. We aim to create a risk score to predict asthma attacks in primary care using a statistical learning approach trained on routinely collected electronic health record data.

METHODS AND ANALYSIS

We will employ machine-learning classifiers (naïve Bayes, support vector machines, and random forests) to create an asthma attack risk prediction model, using the Asthma Learning Health System (ALHS) study patient registry comprising 500 000 individuals across 75 Scottish general practices, with linked longitudinal primary care prescribing records, primary care Read codes, accident and emergency records, hospital admissions and deaths. Models will be compared on a partition of the dataset reserved for validation, and the final model will be tested in both an unseen partition of the derivation dataset and an external dataset from the Seasonal Influenza Vaccination Effectiveness II (SIVE II) study.

ETHICS AND DISSEMINATION

Permissions for the ALHS project were obtained from the South East Scotland Research Ethics Committee 02 [16/SS/0130] and the Public Benefit and Privacy Panel for Health and Social Care (1516-0489). Permissions for the SIVE II project were obtained from the Privacy Advisory Committee (National Services NHS Scotland) [68/14] and the National Research Ethics Committee West Midlands-Edgbaston [15/WM/0035]. The subsequent research paper will be submitted for publication to a peer-reviewed journal and code scripts used for all components of the data cleaning, compiling, and analysis will be made available in the open source GitHub website (https://github.com/hollytibble).

An Effective Method for Producing a Nutritive Protein Extract Powder from Shrimp-head Waste

Enzymatic hydrolysis has been widely applied for production of protein hydrolysate from shrimp waste and for purification of chitin. In the present study, shrimp (P. semisulcatus) head waste was hydrolysed, using a commercial proteolytic enzyme, Alcalase. In order to improve protein extraction efficiency, certain chemicals such as sodium sulphite and Triton x-100 were used along with the enzyme. When Alcalase (12 AU/kg) used alone, the yield of protein extraction was 45.1% and by using Triton x-100 (0.01 g/kg) together with Alcalase, the yield was decreased to 39%, whereas the presence of sodium sulphite (200 mmol/L) with the enzyme or with the enzyme and Triton x-100 increased the level of protein extraction to 62% and 65.1%, respectively. The resulting protein powder contained sufficient amounts of essential amino acids to be used in feed formulations. By precipitating proteins from the resulting protein extract at pH 3.1, the residual sulphite in protein powder was decreased by 97% and thus the powder can be considered suitable for animal and/or aquaculture feed formulations.