Diabetes mellitus increases risk of adverse drug reactions and death in hospitalised older people: the SENATOR trial

Diabetes mellitus increases risk of adverse drug reactions and death in hospitalised older people: the SENATOR trial | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Diabetes mellitus increases risk of adverse drug reactions and death in hospitalised older people: the SENATOR trial Anagha Chinmayee, Selvaranai Subbarayan, Phyo K Myint, Antonio Cherubini, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3377254/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Purpose: Adverse drug reactions (ADRs) are a major cause of morbidity and mortality, especially in older people. Older people with diabetes mellitus may be at especially high risk of ADRs but this risk has not been well studied. This study aimed to compare severity and type of ADRs in hospitalised, multimorbid older people with and without diabetes and secondly to assess the impact of ADRs on mortality, rehospitalisation and length of stay. Methods: Participants in the SENATOR (Software Engine for the Assessment and optimization of drug and non-drug Therapy in Older peRsons) trial were assessed for 12 common and ‘other’ prevalent and incident adverse drug reactions using a blinded end-point adjudication process. Descriptive analyses, logistic regression and mediation analyses were undertaken. Results: Of 1537 people in the SENATOR trial, 540 (35.1%) had diabetes mellitus (mean age 77.4 ± 7.3 years, 58.5% male). In the total population, 773 prevalent and 828 incident ADRs were reported. Both prevalent and incident symptomatic hypoglycaemia and incident acute kidney injury (AKI) were significantly more common in people with diabetes (p<0.05). Patients with diabetes had higher all-cause mortality at 12 weeks than those without (9.1% vs 6.3%, p=0.04). Mediation analysis revealed that mortality was significantly higher (OR = 1.43, Sobel test p=0.048) in people with diabetes and ADRs causing AKI. Conclusions: Older multimorbid people with diabetes presenting to hospital with acute illness have significantly more ADRs than those without, and a significantly higher mortality that is mediated by medication-associated AKI and poorer renal function. Adverse drug reactions diabetes mellitus older people multimorbidity polypharmacy Key findings Aim: To compare severity and type of ADRs in hospitalised, multimorbid older people with and without diabetes and to assess the impact of ADRs on mortality, rehospitalisation and length of stay. Findings: Patients with diabetes had significantly more ADRs causing hypoglycaemia and acute kidney injury (AKI), with higher mortality that was mediated by drug-related AKI. Message: Clinicians should be especially aware of ADRs in people with diabetes, especially diuretics causing AKI. 1. Background Adverse drug reactions (ADRs) are defined as “an appreciably harmful or unpleasant reaction, resulting from an intervention related to the use of a medicinal product, which predicts hazard from future administration and warrants prevention or specific treatment, or alteration of the dose regimen or withdrawal of the product” [ 1 ]. The most common risk factors for ADRs are age, multimorbidity and polypharmacy [ 2 , 3 ]. Aside from being a major cause of morbidity and mortality, ADRs can have a significant impact on an individual’s quality of life and place an increased burden on healthcare systems due to higher patient care costs and prolonged hospital stays [ 4 , 5 ]. Two systematic reviews reported a pooled ADR prevalence of 16% (26 studies including 20,153 patients) [ 6 ] and 22% (18 studies, involving 80,695 patients) [ 7 ] respectively in hospitalised older people and individual study prevalence ranged from 6.3–64.4%. A systematic review of 18 observational studies from European countries and the USA reported that costs due to preventable ADRs in an inpatient setting ranged from €2,851 to €9,015 per hospitalisation [ 8 ]. Diabetes mellitus is a chronic condition with a rising prevalence globally and is associated with greater mortality, decreased functional status, and increased hospitalisation among older people [ 9 , 10 ]. According to 2021 data, it is estimated that 536.6 million people worldwide have diabetes, accounting for 10.5% of the global population with the highest prevalence of 19.3% seen in older people aged ≥ 65 years. According to recently reported data, nearly one in five older people has diabetes [ 11 , 12 ]. Older people with diabetes often have multiple co-morbidities, and tend to be prescribed numerous medications [ 13 – 15 ]. As such, the risk of ADRs in this population is higher, not only due to anti-diabetic medications, but also due to the physiological changes that occur with old age and the micro- and macro-vascular complications of the condition [ 3 , 7 , 16 ] plus under-representation of older people from drug trials that would allow accurate estimation of the real risk of ADRs [ 17 ]. A cohort study using diary data conducted by Denig et al. in a primary care setting reported that out of 78 patients with diabetes mellitus almost half (46%) reported at least one ADR. Of the 80 ADRs reported, 71 (90%) were known ADRs based on the summary of product characteristics (SmPC) and no formal causality was assessed [ 18 ]. A few studies from low-and-middle-income countries have reported ADRs in the diabetic population in the hospital setting [ 19 – 23 ]. However, those studies were focused mainly on ADRs due to anti-diabetic medications or reported ADRs as part of drug-therapy problems (DTPs) in diabetes and were methodologically weak with most being observational studies. More importantly, these studies have not been carried out in the older population, despite diabetes being a condition that affects mostly older adults. To our knowledge, there is no study that has evaluated the risk of ADRs in hospitalised older people with diabetes. The primary objective of this study was to compare the incidence, prevalence, severity, and type of ADR in hospitalised multimorbid older people with and without diabetes using data from the SENATOR trial. The secondary objectives were to assess the impact of ADRs in people with diabetes on mortality, rehospitalisation, and length of stay (LOS) using logistic regression and mediation analysis. 2. Methods 2.1. Study design Analyses were carried out using data from the SENATOR (Software Engine for the Assessment and optimization of drug and non-drug Therapy in Older peRsons) trial, clinical trial registration number NCT02097654 (clinicaltrials.gov). The study was approved by each participating institution’s research ethics committee. The SENATOR protocol has been published previously [ 24 ]. In brief, SENATOR was a pragmatic, multi-national, parallel-arm prospective randomized open-label, blinded endpoint (PROBE) controlled trial that tested the impact of the SENATOR software tool in reducing in-hospital ADRs in multimorbid older people with polypharmacy. The study was conducted between July 2016 to February 2018 and included a diverse population from six academic teaching hospitals across Europe (Belgium, Iceland, Ireland, Italy, Spain and United Kingdom) [ 24 , 25 ]. 2.2. Study population The trial included older people aged ≥ 65 years with ≥ three chronic medical conditions requiring pharmacotherapy who were admitted to the hospital with acute medical or surgical illness. The main exclusion criteria were i) elective admission, ii) admission to geriatric medicine, clinical pharmacology, clinical oncology, haematology, psychiatry, palliative medicine, emergency medicine, intensive care units, iii) acute liver failure, iv) renal failure requiring dialysis, v) solid organ transplant graft, vi) non-accidental overdose/self-harm, vii) estimated life expectancy less than three months; viii) anticipated discharge/hospital transfer within 48 hours of admission and ix) admitted to hospital > 60 hours at the time of planned enrolment [ 24 , 25 ]. 2.3. Definition and adjudication of ADRs SENATOR gathered data on both prevalent and incident ADRs in the trial. Prevalent ADRs were defined as the ADR events/processes that were either the primary cause or partly the cause of hospitalization or which occurred in the emergency department or other locations in the hospital up to the time of randomization. Incident ADRs were defined as ADR events/processes that occurred between randomization and the index hospital discharge or day 14 post-randomisation, whichever occurred first. The ADRs were reported using a study-specific endpoint form based on a review of all the available documentation within the medical record including medical, nursing and allied health professional case note entries, laboratory values, radiology reports, electrocardiograms and other investigations [ 24 , 25 ]. The SENATOR trial defined 12 pre-specified ADRs that represent approximately 80% of all ADRs commonly reported in hospitalised multimorbid older people. The 12 pre-specified ADRs were acute bleeding, acute diarrhoea, new-onset constipation, acute dyspepsia/nausea/vomiting, acute kidney injury (AKI), symptomatic hypoglycaemia (SH), new-onset fall/s, delirium, major serum electrolyte disturbance, symptomatic bradycardia, symptomatic orthostatic hypotension and new-onset unsteady gait. Any non-pre-specified ADRs were documented as unspecified adverse events [ 24 , 25 ]. Therefore, a total of 13 types of ADRs were reported in the SENATOR trial. The definitions of the pre-specified ADRs are provided in Supplementary Table 1 . The ADRs reported using the study-specific endpoint assessment form (ADR form) were adjudicated by a blinded Potential Endpoints Adjudication Committee which ascertained the causality and severity of the ADR. The committee consisted of six blinded expert members who reviewed ADRs independently. If the first blinded endpoint committee reviewer agreed with the site principal investigator’s record of causality and severity, the decision was accepted. If not, the ADR forms were reviewed by the second reviewer. If there was no agreement after the second review, the ADR forms were reviewed by the third reviewer and a consensus was reached. Otherwise, the ADR form was adjudicated by consensus at a full committee meeting excluding the site Principal Investigator [ 24 , 25 ]. Causality of ADRs was assessed using the WHO-UMC causality assessment system which categorized ADRs as certain, probable, possible, unlikely and indeterminate [ 26 ] and ADR severity was graded as mild, moderate and severe according to the modified Hartwig & Siegel severity assessment scale [ 27 ]. 2.4. Data handling The SENATOR trial included data from three assessments i.e., baseline, discharge or day 14 whichever occurred first and a 12-week follow-up visit. For this study purpose, patients were categorised based on the presence or absence of diabetes from their medical history (ICD-10 classification). We included patients with both Type 1 and Type 2 diabetes mellitus. The variables of interest included demographic variables (recruiting centre, age, sex, smoking and alcohol use and level of education); clinical variables (admitting ward, the total number of medications, number of co-morbidities, incidence, prevalence, severity and causality of ADRs and laboratory values such as eGFR and albumin; functional, comorbidity status and cognitive rating scale status (Barthel Index of activities of daily living, Cumulative Illness Rating Scale-Geriatric (CIRS-G) and Mini-Mental State Examination score (MMSE)); and secondary outcome variables (mortality, rehospitalisation and length of stay (LOS)). 2.5. Compilation of ADRs data The primary outcome variables included severity and causality of prevalent and incident ADRs. From the total list of unadjudicated ADRs collected in the trial, the SENATOR team excluded those suspected ADRs where no culprit drug was identified and the ADRs with no data for severity and causality before adjudication. After adjudication, the ADRs were deemed non-eligible if the severity rating was missing or graded zero and only eligible adjudicated ADRs were included in our analyses. Although the SENATOR trial data had five categories for causality, to ensure an adequate study power for our analysis, we categorised ADRs into two groups for causality i.e., indeterminate/unlikely/possible and probable/certain. This was done because the numbers of indeterminate and unlikely events were small. Similarly, the severity was grouped into two categories, mild and moderate/severe. 2.6. Potential confounders and mediators Using standard criteria to identify confounders [ 28 ], age, sex, Barthel Index score, CIRS-G score, smoking, alcohol, number of medications and number of co-morbidities were included as confounders. Of the 12 pre-specified ADRs, only those that were clinically relevant to diabetes and those that showed significance between people with and without diabetes (AKI and SH) were considered confounders in the logistic regression analysis. A mediator is a variable that is in a causal sequence between two variables, the independent and dependent variables [ 29 ]. We considered the presence/absence of diabetes as our independent variable and mortality at 12 weeks, rehospitalisation at 12 weeks of discharge and LOS as our dependent variables. Based on this definition, the variables chosen as mediators were eGFR, serum albumin concentration and the 12 pre-specified ADRs. 2.7. Statistical analysis IBM SPSS Statistics version 27.0 was used for data analysis. Depending on the data distribution, continuous data were presented as median (interquartile range, IQR) or mean ± standard deviation (SD). In addition to visual inspection of the distribution, skewness of minus one to plus one was considered normal distribution. Categorical data were presented as frequencies and proportions. Group differences between people with and without diabetes for continuous variables were analysed using parametric (unpaired t-test) and nonparametric (Mann-Whitney U test) tests respectively for normal and skewed data distribution. Categorical data were compared using Chi-square or Fisher’s exact tests. Logistic regression analysis was used to examine the relationship between having diabetes and the impact of ADRs on outcomes i.e., mortality, rehospitalisation and LOS. Models were built with a stepwise approach using multivariable logistic regression, adjusting for confounders one at a time and adding ADRs (AKI and SH) in the final model. LOS was dichotomised as ≤/>6 as 6 days was the median LOS. Odds ratios with 95% confidence intervals and p-values were reported. Mediation analysis was performed according to the framework proposed by Baron and Kenny [ 30 ] to assess whether mediators explain differences in outcomes in people with and without diabetes. The criteria for analysis included: Step 1) the independent variable must be significantly related to the dependent variable; Step 2) the independent variable must be significantly related to the mediator, and Step 3) the association between the independent and dependent variable must be attenuated when the mediator is included in the regression model [ 28 ]. The indirect effect was calculated as a*b and c is the direct effect. Sobel’s test was used to determine the significance of the effect. For all analyses, a two-sided p-value of < 0.05 was considered statistically significant. 3. Results 3.1. Participant characteristics A total of 1537 participants were recruited to the SENATOR trial; 405 (26.4%) from Cork, 295 (19.2%) from Reykjavik, 285 (18.5%) from Aberdeen, 205 (13.3%) from Ghent, 190 (12.4%) from Madrid and 157 (10.2%) from Ancona. The mean age of the total population was 78.2 ± 7.4 years with 52.8% being male. Of 1537 people included in our analysis, 540 (35.1%) participants had diabetes. The mean age of people with diabetes was 77.4 ± 7.3 years and 78.6 ± 7.5 years for people without diabetes (p = 0.002). In the group with diabetes, 316 (58.5%) were male compared to 496 males (49.7%) in the group without diabetes (p = 0.001). The median number of medications was significantly greater in people with diabetes (10 [ 8 , 12 ] versus 9 [ 6 , 11 ]; p < 0.001), as was the number of co-morbidities (12 [ 8 , 16 ] versus 9 [ 7 , 13 ], p < 0.001). In addition, both the CIRS-G score (16.5 ± 5.8 vs. 14.4 ± 5.7, p < 0.001) and Barthel index score (18 (14,20) vs. 18 (14,20), p = 0.004) were significantly different between the two groups, with the diabetes group having greater burden of morbidity and disability. Participant characteristics are presented in Table 1 . Table 1 Participant characteristics of people with and without diabetes. Variable Total population (n = 1537) n (%) People with diabetes (n = 540) n (%) People without diabetes (n = 997) n (%) p-value Recruiting centre < 0.001 Aberdeen 285 (18.5%) 120 (42.1%) 165 (57.9%) Ancona 157 (10.2%) 56 (35.7%) 101 (64.3%) Cork 405 (26.4%) 104 (25.7%) 301(74.3%) Ghent 205 (13.3%) 85 (41.2%) 120 (58.8%) Madrid 190 (12.4%) 85 (44.7%) 105 (45.3%) Reykjavik 295 (19.2%) 90 (30.5%) 205 (69.5%) Age years (mean ± SD) 78.2 ± 7.4 77.4 ± 7.3 78.6 ± 7.5 0.002 Sex 0.001 Male 812 (52.8%) 316 (58.5%) 496 (49.7%) Female 725 (47.2%) 224 (41.5%) 501(50.3%) Smoking status 0.41 Yes 108 (7%) 34 (6.3%) 74 (7.4%) No 1,429 (93%) 506 (93.7%) 923 (92.6%) Alcohol 0.06 Yes 432 (28.1%) 136 (25.2%) 296 (29.7%) No 1,105 (71.9%) 404 (74.8%) 701 (70.3%) Education 0.74 No schooling 37 (2.4%) 12 (2.2%) 25 (2.5%) Primary school education only 561 (36.5%) 198 (36.7%) 363 (36.4%) Some secondary education 281 (18.3%) 89 (16.5%) 192 (19.3%) Complete secondary education 448 (29.1%) 165 (30.6%) 283 (28.4%) Some third-level education 55 (3.6%) 22 (4.1%) 33 (3.3%) Complete third-level education 155 (10.1%) 54 (10%) 101 (10.1%) Previous documented ADR(s) 669 (43.5%) 224 (41.5%) 445 (44.6%) 0.23 Total number of medications median [IQR] 9 [ 7 , 11 ] 10 [ 8 , 12 ] 9 [ 6 , 11 ] < 0.001 Number of medical conditions median [IQR] 10 [ 7 , 14 ] 12 [ 8 , 16 ] 9 [ 7 , 13 ] < 0.001 Admitted ward 0.20 Medical 1299 (84.5%) 465 (86.1%) 834 (83.7%) Surgical 238 (15.5%) 75 (13.9%) 163 (16.3%) Patients who experienced a prevalent ADR 0.56 Yes 563 (36.6%) 203 (37.6%) 360 (36.1%) No 974 (63.4%) 337 (62.4%) 637 (63.9%) Patients who experienced an incident ADR 0.17 Yes 551 (35.8%) 206 (38.1%) 345 (34.6%) No 986 (64.2%) 334 (61.9%) 652 (65.4%) CIRS-G score (mean ± SD) 15.1 ± 5.9 16.5 ± 5.8 14.4 ± 5.7 < 0.001 Barthel Index ADL (median [IQR]) 18 [ 14 , 20 ] 18 [ 14 , 20 ] 18 [ 14 , 20 ] 0.004 MMSE score (median [IQR]) * 27 [ 23 , 29 ] 27 [ 23 , 29 ] 27 [ 24 , 29 ] 0.70 SD: Standard Deviation; IQR: Interquartile Range; ADRs: Adverse Drug Reactions; MMSE: Mini-Mental State Examination; CIRS-G: Cumulative Illness Rating Scale -Geriatric (CIRS-G) score; Barthel Index ADL: Barthel Index of Activities of Daily Living. *Total population included in the analysis was 1503 for MMSE score. 3.2. Primary outcomes 3.2.1. Prevalence and incidence of ADRs in the SENATOR data A total of 3247 unadjudicated putative ADRs were reported in the SENATOR trial; 886 putative ADRs were excluded as no culprit drugs were identified. Of the remaining 2361 putative ADRs, there were 1080 (45.7%) prevalent and 1281 (54.3%) incident ADRs. Of 1080 unadjudicated prevalent ADRs, 17 ADRs with missing data for causality and severity were removed and 1063 were sent for adjudication. After adjudication, 290 were determined as being non-eligible (i.e. not an ADR) leaving 773 eligible prevalent ADRs (see Supplementary Fig. 1 ). Of the 1281 unadjudicated incident ADRs, 232 ADRs with missing data for causality and severity were excluded prior to adjudication and 1049 were sent to the adjudication committee. Following adjudication, 221 were considered non-eligible leaving 828 eligible incident ADRs (see Supplementary Fig. 2 ). 3.2.2. Comparison of prevalent ADRs between people with and without diabetes Of 773 prevalent ADRs, 284 (36.7%) were observed in people with diabetes versus 489 (63.3%) ADRs in people without diabetes (see Table 2 ). Of 1537 participants, 203 (37.6%) participants with diabetes versus 360 (36.1%) participants without diabetes experienced a prevalent ADR, p = 0.56 (see Table 1 ). Table 2 Comparison of severity of prevalent ADRs between people with and without diabetes. Prevalent ADRs Total no. of prevalent ADRs (n = 773) n (%) Prevalent ADRs in people with diabetes (n = 284) n (%) Prevalent ADRs in people without diabetes (n = 489) n (%) p-value Mild Moderate/severe Mild Moderate/severe Acute bleeding 80 (10.3%) 4 (1.4%) 16 (5.6%) 27 (5.5%) 33 (6.7%) 0.05 Acute diarrhoea 30 (3.9%) 6 (2.1%) 8 (2.8%) 9 (1.8%) 7 (1.4%) 0.18 New onset constipation 42 (5.4%) 2 (0.7%) 9 (3.2%) 5 (1%) 26 (5.3%) 0.22 Acute dyspepsia/nausea/vomiting 59 (7.6%) 4 (1.4%) 13 (4.6%) 13 (2.7%) 29 (5.9%) 0.30 Acute kidney injury (AKI) 82 (10.6%) 20 (7%) 13 (4.6%) 26 (5.3%) 23 (4.7%) 0.32 Symptomatic hypoglycaemia (SH) 14 (1.8%) 1 (0.4%) 12 (4.2%) 0 1 (0.2%) < 0.001 New onset fall/S 110 (14.2%) 13 (4.6%) 23 (8.1%) 17 (3.5%) 57 (11.7%) 0.58 Delirium 39 (5.0%) 4 (1.4%) 10 (3.5%) 10 (2%) 15 (3.1%) 0.92 Major serum electrolyte disturbance 176 (22.8%) 32 (11.3%) 37 (13%) 34 (7%) 73 (14.9%) 0.23 Symptomatic bradycardia 25 (3.2%) 1 (0.4%) 5 (1.8%) 7 (1.4%) 12 (2.5%) 0.24 Symptomatic orthostatic hypotension 17 (2.2%) 3 (1.1%) 4 (1.4%) 4 (0.8%) 6 (1.2%) 0.60 New onset unsteady gait 19 (2.5%) 4 (1.4%) 5 (1.8%) 5 (1%) 5 (1%) 0.26 Unspecified adverse event 80 (10.3%) 15 (5.3%) 20 (7%) 19 (3.9%) 26 (5.3%) 0.10 ADRs: Adverse Drug Reactions Among 12 different pre-specified ADRs, SH was the only prevalent ADR with a statistically significant difference between the groups, with 13 events in people with diabetes compared to 1 SH event in people without diabetes (p < 0.001). Of the 13 SH, one SH was mild and 12 were moderate/severe compared to only one moderate/severe SH in people without diabetes. Instances of mild AKI, falls and electrolyte disturbance, and moderate/severe delirium were more common in people with diabetes, but these differences did not reach statistical significance. Interestingly, the percentage of both mild and moderate/severe unspecified ADRs was higher in people with versus people without diabetes, (p = 0.006). In terms of causality, only SH showed a statistically significant difference between the groups, 12 were probable/certain, and one was indeterminate/unlikely/possible in people with diabetes compared to just one indeterminate/unlikely/possible event in people without diabetes. People with diabetes had more indeterminate/unlikely/possible AKI and electrolyte disturbance as well as a higher proportion of probable/certain electrolyte disturbance, though none of these differences reached statistical significance. The severity and causality of prevalent ADRs are presented in Table 2 and Supplementary Table 2 respectively. 3.2.3. Comparison of incident ADRs between people with and without diabetes Of 828 incident ADRs, 334 (40.3%) were observed in people with diabetes and 494 (59.7%) in those without diabetes (see Table 3 ). Of 1537 participants, 206 (38.1%) participants with diabetes suffered incident ADRs compared to 345 (34.6%) participants without diabetes (p = 0.17) (see Table 1 ). Table 3 Comparison of severity of incident ADRs between people with and without diabetes. Incident ADRs Total no. of incident ADRs (n = 828) n (%) Incident ADRs in people with diabetes (n = 334) n (%) Incident ADRs in people without diabetes (n = 494) n (%) p-value Mild Moderate/severe Mild Moderate/severe Acute bleeding 68 (8.2%) 17 (5.1%) 9 (2.7%) 23 (4.7%) 19 (3.8%) 0.58 Acute diarrhoea 65 (7.9%) 22 (6.6%) 5 (1.5%) 28 (5.7%) 10 (2%) 0.27 New onset constipation 138 (16.7%) 7 (2.1%) 34 (10.2%) 18 (3.6%) 79 (16%) 0.16 Acute dyspepsia/nausea/vomiting 68 (8.2%) 6 (1.8%) 21 (6.3%) 17 (3.4%) 24 (4.9%) 0.42 Acute kidney injury (AKI) 99 (11.9%) 20 (6%) 27 (8.1%) 24 (4.9%) 28 (5.7%) 0.008 Symptomatic hypoglycaemia (SH) 14 (1.7%) 2 (0.6%) 12 (3.6%) 0 0 < 0.001 New onset fall/S 22 (2.7%) 6 (1.8%) 4 (1.2%) 9 (1.8%) 3 (0.6%) 0.31 Delirium 44 (5.3%) 11 (3.3%) 8 (2.4%) 17 (3.4%) 8 (1.6%) 0.26 Major serum electrolyte disturbance 163 (19.7%) 24 (7.2%) 31 (9.3%) 43 (8.7%) 65 (13.2%) 0.69 Symptomatic bradycardia 11 (1.3%) 1 (0.3%) 1 (0.3%) 4 (0.8%) 5 (1%) 0.24 Symptomatic orthostatic hypotension 16 (1.9%) 2 (0.6%) 4 (1.2%) 8 (1.6%) 2 (0.4%) 0.84 New onset unsteady gait 3 (0.4%) 1 (0.3%) 0 1 (0.2%) 1 (0.2%) 1.00 Unspecified adverse event 117 (14.1%) 28 (8.4%) 31 (9.3%) 24 (4.9%) 34 (6.9%) 0.006 ADRs: Adverse Drug Reactions There were 20 (6%) mild AKI and 27 (8.1%) moderate/severe AKI that occurred in patients with diabetes compared to 24 (4.9%) mild and 28 (5.7%) moderate/severe AKI in patients without diabetes (p = 0.008). The culprit medications are listed in Table 4 . Additionally, two mild and 12 moderate/severe SH were identified in people with diabetes compared to zero SH in those without diabetes (p < 0.001). Mild bleeding and mild diarrhoea were more common in people with diabetes. Similarly, people with diabetes had a greater percentage of moderate/severe dyspepsia/nausea/vomiting, falls, delirium and orthostatic hypotension although the difference was not statistically significant. Table 4 Number of instances of certain and probable acute kidney injury by medication Causative medications Acute kidney injury Certain and probable adverse drug reactions Prevalent Incident People with diabetes (n = 33) People without diabetes (n = 49) People with diabetes (n = 47) People without diabetes (n = 52) Furosemide 6 11 20 21 Spironolactone 2 0 7 4 Bumetanide 1 2 4 1 Metolazone 1 0 2 3 Flucloxacillin 1 0 - - Gentamicin 0 1 0 2 Co-trimoxazole 0 1 1 1 Ramipril 0 1 3 0 Potassium canrenoate 1 2 1 3 Perindopril 1 0 - - Piroxicam 0 1 - - Dexketoprofen 0 1 - - Torsemide 0 1 - - Enalapril 2 0 0 2 Etoricoxib 0 1 - - Vancomycin 0 1 - - Radiology contrast - - 3 1 Metformin - - 1 0 Clindamycin - - 0 1 Piperacillin + Tazobactam - - 1 1 Co-amoxiclav - - 0 1 Canrenone - - 0 2 Mefenamic acid - - 0 1 Naproxen - - 1 1 Amlodipine - - 0 1 Hydrochlorothiazide - - 0 1 In terms of ADR causality, 20 AKI events were unlikely/indeterminate/possible and 27 AKI events were probable/certain in people with diabetes compared to 26 AKI events that were unlikely/indeterminate/possible and 26 AKI events that were probable/certain in people without diabetes. All 14 SH in people with diabetes were probable/certain (p < 0.001). In addition, a higher percentage of people with diabetes suffered indeterminate/unlikely/possible bleeding and constipation, and probable/certain diarrhoea, falls, and delirium in comparison to those without diabetes. Nevertheless, causality was not significantly different between the groups for any ADRs except SH. The severity and causality of incident ADRs are presented in Table 3 and Supplementary Table 3 . 3.3. Impact of ADRs in diabetic patients on secondary outcomes All-cause mortality at 12 weeks was higher in people with diabetes, 9.1% (46/505) compared to people without diabetes, 6.3% (59/944), p = 0.045. The number of people re-hospitalised at 12 weeks was 164 (36%) among those that had diabetes compared to 310 (35.4%) in people without diabetes (p = 0.84). LOS was the same in both the groups (6 [ 3 , 11 ] vs 6 [ 3 , 10 ], p = 0.36). The results for secondary outcomes are presented in Table 5 . Table 5 Secondary outcomes: comparison of mortality, rehospitalisation and length of stay between people with and without diabetes Variable Total population (n) Total (n) People with diabetes (n = 505) People without diabetes (n = 944) p-value Mortality (all-cause) at 12 weeks 1449 105 (7.2%) 46 (9.1%) 59 (6.3%) 0.045 People with diabetes (n = 456) People without diabetes (n = 876) Re-hospitalisation (all-cause) at 12 weeks 1332 474 (35.6%) 164 (36%) 310 (35.4%) 0.84 People with diabetes (n = 534) People without diabetes (n = 984) Length of stay (median [IQR]) 1518 6 [ 3 , 11 ] 6 [ 3 , 11 ] 6 [ 3 , 10 ] 0.36 IQR: Interquartile Range The unadjusted model showed a statistically significant association between having diabetes and mortality (OR 1.50, 95% CI 1.01–2.25, p = 0.047). Multiple logistic regression showed a significant association remained when adjusted for age, sex and Barthel Index score (OR 1.55, 95% CI 1.02–2.34 and p = 0.039). The association did not reach statistical significance after adjustment for the burden of comorbidities. Furthermore, rehospitalisation and LOS did not show a significant association with having diabetes both in the unadjusted and multiple logistic regression models. The results for multiple logistic regression are shown in Table 6 . Table 6 Multiple logistic regression analysis showing the association between the presence of diabetes and mortality, rehospitalisation and length of stay. Model Variables Outcomes Mortality Rehospitalisation Length of stay OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value unadjusted Diabetes – Yes 1.50 1.01–2.25 0.047 0.98 0.77–1.24 0.84 0.99 0.80–1.22 0.90 A Age 1.64 1.09–2.47 0.017 0.99 0.78–1.26 0.94 0.98 0.79–1.21 0.83 B Age, Sex 1.63 1.08–2.45 0.019 1.02 0.80–1.30 0.87 0.97 0.78–1.20 0.75 C Age, Sex, Barthel Index 1.55 1.02–2.34 0.039 1.05 0.83–1.33 0.70 1.02 0.82–1.26 0.87 D Age, Sex, Barthel Index, CIRSG - score 1.39 0.91–2.12 0.13 1.08 0.85–1.38 0.52 0.99 0.79–1.23 0.91 E Age, Sex, Barthel Index, CIRSG – score, smoking 1.38 0.90–2.10 0.14 1.08 0.85–1.38 0.54 0.99 0.79–1.23 0.91 F Age, Sex, Barthel Index, CIRSG – score, smoking, alcohol 1.36 0.89–2.07 0.16 1.08 0.84–1.38 0.56 0.98 0.79–1.22 0.86 G Age, Sex, Barthel Index, CIRSG – score, smoking, alcohol, no. of meds 1.38 0.90–2.12 0.14 1.14 0.89–1.46 0.30 1.01 0.81–1.27 0.91 H Age, Sex, Barthel Index, CIRSG – score, smoking, alcohol, no of meds, no. of co-morbidities 1.33 0.86–2.06 0.19 1.20 0.93–1.55 0.16 0.96 0.76–1.20 0.71 I Age, Sex, Barthel Index, CIRSG – score, smoking, alcohol, no. of meds, no. of co-morbidities, AKI (incident) 1.27 0.82–1.97 0.29 1.21 0.94–1.56 0.14 1.00 0.79–1.26 0.97 J Age, Sex, Barthel Index, CIRSG – score, smoking, alcohol, no of meds, no. of co-morbidities, AKI (prevalent) 1.32 0.85–2.04 0.22 1.20 0.93–1.55 0.16 0.96 0.76–1.20 0.71 K Age, Sex, Barthel Index, CIRSG – score, smoking, alcohol, no of meds, no. of co-morbidities, SH (incident) 1.34 0.86–2.07 0.19 1.19 0.92–1.54 0.18 0.99 0.79–1.24 0.91 L Age, Sex, Barthel Index, CIRSG – score, smoking, alcohol, no of meds, no. of co-morbidities, SH (prevalent) 1.35 0.87–2.09 0.18 1.21 0.94–1.56 0.15 0.96 0.76–1.20 0.70 CIRS-G: Cumulative Illness Rating Scale-Geriatric; AKI: Acute Kidney Injury; SH: Symptomatic Hypoglycaemia; OR: Odds Ratio; CI: Confidence Interval. 3.3.1. Mediation analysis When testing the mediator role of ADRs in the relationship between having diabetes and mortality, in the first regression step, presence of diabetes was significantly associated with mortality (B = 0.41, p = 0.047). In the second step, presence of diabetes was positively associated with incident AKI (B = 0.55, p = 0.008). Finally, in the third step, when presence of diabetes and AKI were simultaneously included in the equation, having diabetes and incident AKI was significantly associated with mortality (OR = 1.43, Sobel test p = 0.048); the results are shown in Supplementary Fig. 3 and Supplementary Table 4 . Similarly, eGFR fully mediated the association between presence of diabetes and mortality. Supplementary Fig. 4 and Supplementary Table 4 illustrate the findings. It was not possible to carry out mediation analysis for other outcomes i.e., rehospitalisation and LOS as the results were insignificant at Step 1. Furthermore, the other 12 ADRs and serum albumin concentration were not significant at Step 2 and hence could not be analysed further. 4. Discussion To the best of our knowledge, the present study is the first that specifically reports the burden of ADRs in an older hospitalized patient population with diabetes with specific details of both prevalent and incident ADRs in the hospital care setting. We found a higher rate of ADRs and an increased risk of all-cause mortality at 12 weeks in those with diabetes. The higher mortality rate was mediated by medication-associated AKI and lower eGFR. Diuretics were frequently implicated as the cause of medication-associated AKI. Out of 1537 participants in the SENATOR trial, more than a third of people with diabetes experienced an ADR. An observational study that used data from 2257 hospitalized type 2 diabetes mellitus patients enrolled in the Gruppo Italiano di Farmacovigilanza nell’Anziano study, conducted in community and university hospitals across Italy from 1993 to 1998 reported that 10.2% of all patients had an ADR during the hospital stay. However, that study reported the incidence of ADRs due to hydrosoluble drugs in undiagnosed renal failure patients with diabetes and is now a considerably older study [ 31 ]. In the present study, although some particular ADRs occurred more frequently in people with diabetes, only AKI and SH reached statistical significance. Similarly, only SH showed significance amongst the prevalent ADRs, though this is not surprising. The unadjusted regression analysis showed a significant association between diabetes and mortality. Additionally, multiple logistic regression analysis showed this association remained significant when adjusted for age, sex and Barthel Index score. After adjustment for comorbidity burden, the association between mortality and diabetes remained but no longer reached statistical significance. This is probably because this association with death is partly driven by the accumulation of comorbidities, but most of these are themselves strongly associated with, or directly caused by, diabetes. Furthermore, mediation analysis confirmed that the mortality rates were significantly higher in patients with diabetes experiencing AKI (incident ADR) accounting for the difference in outcomes. However, diabetes does not appear to influence the likelihood of re-hospitalisation or duration of inpatient stay. The higher risk of SH can very likely be attributed to a combination of tight glycaemic control, undernutrition and polypharmacy with drug-drug interactions with antidiabetic medication. Multiple studies [ 32 , 33 ] identify hypoglycaemia as the most commonly observed ADR in people with diabetes but our study, with its highly detailed ADR ascertainment processes, shows that other ADRs are more prevalent. People with diabetes are also at an increased risk of experiencing an AKI during hospitalization. This can be explained by the fact that diabetic patients incur variable degrees of kidney damage over time, exacerbated by nephrotoxic drugs. This is particularly significant in older people, as kidney function normally declines with age [ 31 ]. Our findings show how important it is to understand the burden of potentially avoidable ADRs and iatrogenic injury in the growing population of older people with diabetes. Our study is novel in that it specifically examines the relationship between diabetes and ADRs and the impact of ADRs on outcomes in a multimorbid older population, as well as its large sample size with participants from six centres across Europe. The method of ascertainment of ADRs was substantially more detailed and rigorous compared to that used in most other studies that rely on routinely collected clinical data. This study has some limitations. We could not reliably distinguish between Type 1 and Type 2 diabetes from the trial dataset, which might have provided additional insights into ADRs in hospitalised older diabetic patients. Additionally, we lacked sufficient data on the duration and control of diabetes, both factors which could influence the degree of ADR risk especially as some patients could simply be maintained on diet control and may not be on any anti-diabetic medication. Finally, incident ADRs may theoretically have been affected by the trial intervention, which sought to minimise ADRs. However, few recommendations from the trial intervention were adopted by clinicians looking after participants and the trial results showed no impact on ADRs [ 24 ] so this is unlikely to be significant. Nevertheless, further research addressing the above-mentioned limitations would help confirm and build on our findings. 5. Conclusions In summary, hospitalised multimorbid older people with diabetes are at a significantly higher susceptibility for developing specific ADRs, especially AKI, which increases their risk of mortality. Along with diabetic control for preventing vascular complications including renal damage, efforts to reduce polypharmacy, regular medication review and deprescribing of nephrotoxic medications and more cautious use of diuretics are recommended to reduce the AKI ADR rates and improve outcomes in this growing population. Declarations Funding The SENATOR trial was funded by the European Union Framework Programme 7 (FP7/2007–2013 grant number 305930) and AC received the Innes Will Endowed Scholarship under the University of Aberdeen Summer Research Scholarship Programme to undertake the present study. Conflict of interest statement: On behalf of all authors, the corresponding author states that there is no conflict of interest. References Edwards IR, Aronson JK. Adverse drug reactions: definitions, diagnosis, and management. Lancet 2000;356(9237):1255-1259. Trivalle C, Burlaud A, Ducimetière P, IMEPAG Group. Risk factors for adverse drug events in hospitalized elderly patients: a geriatric score. Eur Geriatr Med 2011;2(5):284-289. Zazzara MB, Palmer K, Vetrano DL, Carfì A, Graziano O. Adverse drug reactions in older adults: a narrative review of the literature. Eur Geriatr Med 2021;12(3):463-473. Wiffen P, Gill M, Edwards J, Moore A. Adverse drug reactions in hospital patients-A systematic review of the prospective and retrospective studies. Bandolier 2002;June:1-14. Sandoval T, Martínez M, Miranda F, Jirón M. Incident adverse drug reactions and their effect on the length of hospital stay in older inpatients. Int J Clin Pharm 2021;43(4):839-846. Jennings EL, Murphy KD, Gallagher P, O’Mahony D. In-hospital adverse drug reactions in older adults; prevalence, presentation and associated drugs—a systematic review and meta-analysis. Age Ageing 2020;49(6):948-958. Yadesa TM, Kitutu FE, Deyno S, Ogwang PE, Tamukong R, Alele PE. Prevalence, characteristics and predicting risk factors of adverse drug reactions among hospitalized older adults: a systematic review and meta-analysis. SAGE Open Medicine 2021;9:1-14. Formica D, Sultana J, Cutroneo P, Lucchesi S, Angelica R, Crisafulli S, et al. The economic burden of preventable adverse drug reactions: a systematic review of observational studies. Exp Opinion Drug Safety 2018;17(7):681-695. Doucet J, Verny C, Bordier L, Rekik A, Zulfiqar AA, Bezerra CB, Bauduceau B. Evolution in geriatric syndromes and association with survival over 5 years in the GERODIAB cohort of older French diabetic patients. Eur Geriatr Med 2021 Jun;12(3):619-625. Kirkman MS, Briscoe VJ, Clark N, Florez H, Haas LB, Halter JB, et al. Diabetes in older adults. Diabetes Care 2012;35(12):2650-2664. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 2022;183:109119. Sinclair A, Saeedi P, Kaundal A, Karuranga S, Malanda B, Williams R. Diabetes and global ageing among 65–99-year-old adults: Findings from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2020 April 2020;162:108078. Mata-Cases M, Franch-Nadal J, Real J, Cedenilla M, Mauricio D. Prevalence and coprevalence of chronic comorbid conditions in patients with type 2 diabetes in Catalonia: a population-based cross-sectional study. BMJ Open 2019 Oct 28;9(10):e031281-2019-031281. Remelli F, Ceresini MG, Trevisan C, Noale M, Volpato S. Prevalence and impact of polypharmacy in older patients with type 2 diabetes. Aging Clin Exp Res 2022:1-15. Iglay K, Hannachi H, Joseph Howie P, Xu J, Li X, Engel SS, et al. Prevalence and co-prevalence of comorbidities among patients with type 2 diabetes mellitus. Curr Med Res Opin 2016;32(7):1243-1252. Davies E, O'Mahony M. Adverse drug reactions in special populations–the elderly. Br J Clin Pharmacol 2015;80(4):796-807. Cruz-Jentoft AJ, Carpena-Ruiz M, Montero-Errasquín B, Sánchez-Castellano C, Sánchez-García E. Exclusion of older adults from ongoing clinical trials about type 2 diabetes mellitus. J Am Geriatr Soc. 2013 May;61(5):734-8. Denig P, van Puijenbroek EP, Soliman N, Mol PG, de Vries ST. Adverse drug event patterns experienced by patients with diabetes: a diary study in primary care. Pharmacoepidemiol Drug Saf 2019;28(9):1175-1179. Singh A, Dwivedi S. Study of adverse drug reactions in patients with diabetes attending a tertiary care hospital in New Delhi, India. Indian J Med Res 2017 Feb;145(2):247-249. Elangwe A, Katte J, Tchapmi D, Figueras A, Mbanya JC. Adverse drug reactions to anti-diabetic drugs are commonest in patients whose treatment do not adhere to diabetes management clinical guidelines: cross-sectional study in a tertiary care service in sub-Saharan Africa. Eur J Clin Pharmacol 2020;76(11):1601-1605. Ogbonna B, Ezenduka C, Opara C, Ahara L. Drug therapy problems in patients with Type-2 Diabetes in a tertiary hospital in Nigeria. Int J Innov Res Dev 2014;3(1):494-502. Koyra HC, Tuka SB, Tufa EG. Epidemiology and predictors of drug therapy problems among type 2 diabetic patients at Wolaita Soddo University Teaching Hospital, Southern Ethiopia. Am J Pharmacol Sci 2017;5(2):40-48. Deb T, Chakrabarty A, Ghosh A. Adverse drug reactions in Type 2 diabetes mellitus patients on oral antidiabetic drugs in a diabetes outpatient department of a tertiary care teaching hospital in the Eastern India. Int J Med Sci Public Health 2017;6(3):554-558. Lavan AH, O’Mahony D, Gallagher P, Fordham R, Flanagan E, Dahly D, et al. The effect of SENATOR (Software ENgine for the Assessment and optimisation of drug and non-drug Therapy in Older peRsons) on incident adverse drug reactions (ADRs) in an older hospital cohort–Trial Protocol. BMC Geriatrics 2019;19(1):1-12. O'Mahony D, Gudmundsson A, Soiza RL, Petrovic M, Cruz-Jentoft AJ, Cherubini A, et al. Prevention of adverse drug reactions in hospitalized older patients with multi-morbidity and polypharmacy: the SENATOR* randomized controlled clinical trial. Age Ageing 2020;49(4):605-614. World Health Organization (WHO), The use of the WHO-UMC system for standardised case causality assessment. Available at: https://www.who.int/publications/m/item/WHO-causality-assessment, 14 Oct 2022. Hartwig SC, Siegel J, Schneider PJ. Preventability and severity assessment in reporting adverse drug reactions. Am J Hosp Pharm 1992;49(9):2229-2232. Kamangar F. Confounding variables in epidemiologic studies: basics and beyond. Arch Iran Med 2012 Aug;15(8):508-516. MacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol. 2007;58:593-614. Baron RM, Kenny DA. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51(6):1173-1182. Corsonello A, Pedone C, Corica F, Mazzei B, Di Iorio A, Carbonin P, et al. Concealed renal failure and adverse drug reactions in older patients with type 2 diabetes mellitus. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 2005;60(9):1147-1151. Monteiro C, Silvestre S, Duarte AP, Alves G. Assessment of suspected adverse drug reactions in elderly patients with diabetes mellitus based on a Portuguese spontaneous reporting database: analysis of reporting from 2008 to 2018 . Expert Opinon on Drug Safety 2021;20(7):845–53. Singh A, Dwivedi S. Study of adverse drug reactions in patients with diabetes attending a tertiary care hospital in New Delhi, India. Indian J Med Res. 2017;145(2):247-249. Supplementary Files AdditionalMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 01 Oct, 2023 Reviewers invited by journal 01 Oct, 2023 Editor invited by journal 29 Sep, 2023 Editor assigned by journal 27 Sep, 2023 First submitted to journal 25 Sep, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3377254","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":237152404,"identity":"ef386fd0-ecaa-4021-bb9e-6e89dbfeb32c","order_by":0,"name":"Anagha Chinmayee","email":"","orcid":"","institution":"University of Aberdeen Institute of Applied Health Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anagha","middleName":"","lastName":"Chinmayee","suffix":""},{"id":237152405,"identity":"c1cae9dd-50ba-4f76-bcee-b7f87a7f3102","order_by":1,"name":"Selvaranai 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14:35:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3377254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3377254/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":44181659,"identity":"fb82d389-f24f-4bf4-9702-8fce0dd89767","added_by":"auto","created_at":"2023-10-06 09:52:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":744362,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3377254/v1/320bcbcd-2659-485b-a586-9e522f6fa3b5.pdf"},{"id":44181281,"identity":"b6d552c5-ebf9-424d-b1ba-88b9c3bde54e","added_by":"auto","created_at":"2023-10-06 09:44:24","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":60319,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3377254/v1/07cb96342ba599d913290dc6.docx"}],"financialInterests":"","formattedTitle":"Diabetes mellitus increases risk of adverse drug reactions and death in hospitalised older people: the SENATOR trial","fulltext":[{"header":"Key findings","content":"\u003cp\u003eAim:\u0026nbsp;To compare severity and type of ADRs in hospitalised, multimorbid older people with and without diabetes and to assess the impact of ADRs on mortality, rehospitalisation and length of stay.\u003c/p\u003e\n\u003cp\u003eFindings: Patients with diabetes had significantly more ADRs causing hypoglycaemia and acute kidney injury (AKI), with higher mortality that was mediated by drug-related AKI.\u003c/p\u003e\n\u003cp\u003eMessage: Clinicians should be especially aware of ADRs in people with diabetes, especially diuretics causing AKI.\u0026nbsp;\u003c/p\u003e"},{"header":"1. Background","content":"\u003cp\u003eAdverse drug reactions (ADRs) are defined as \u0026ldquo;an appreciably harmful or unpleasant reaction, resulting from an intervention related to the use of a medicinal product, which predicts hazard from future administration and warrants prevention or specific treatment, or alteration of the dose regimen or withdrawal of the product\u0026rdquo; [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The most common risk factors for ADRs are age, multimorbidity and polypharmacy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Aside from being a major cause of morbidity and mortality, ADRs can have a significant impact on an individual\u0026rsquo;s quality of life and place an increased burden on healthcare systems due to higher patient care costs and prolonged hospital stays [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Two systematic reviews reported a pooled ADR prevalence of 16% (26 studies including 20,153 patients) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and 22% (18 studies, involving 80,695 patients) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] respectively in hospitalised older people and individual study prevalence ranged from 6.3\u0026ndash;64.4%. A systematic review of 18 observational studies from European countries and the USA reported that costs due to preventable ADRs in an inpatient setting ranged from \u0026euro;2,851 to \u0026euro;9,015 per hospitalisation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiabetes mellitus is a chronic condition with a rising prevalence globally and is associated with greater mortality, decreased functional status, and increased hospitalisation among older people [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to 2021 data, it is estimated that 536.6\u0026nbsp;million people worldwide have diabetes, accounting for 10.5% of the global population with the highest prevalence of 19.3% seen in older people aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. According to recently reported data, nearly one in five older people has diabetes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Older people with diabetes often have multiple co-morbidities, and tend to be prescribed numerous medications [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As such, the risk of ADRs in this population is higher, not only due to anti-diabetic medications, but also due to the physiological changes that occur with old age and the micro- and macro-vascular complications of the condition [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] plus under-representation of older people from drug trials that would allow accurate estimation of the real risk of ADRs [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA cohort study using diary data conducted by Denig et al. in a primary care setting reported that out of 78 patients with diabetes mellitus almost half (46%) reported at least one ADR. Of the 80 ADRs reported, 71 (90%) were known ADRs based on the summary of product characteristics (SmPC) and no formal causality was assessed [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA few studies from low-and-middle-income countries have reported ADRs in the diabetic population in the hospital setting [\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, those studies were focused mainly on ADRs due to anti-diabetic medications or reported ADRs as part of drug-therapy problems (DTPs) in diabetes and were methodologically weak with most being observational studies. More importantly, these studies have not been carried out in the older population, despite diabetes being a condition that affects mostly older adults. To our knowledge, there is no study that has evaluated the risk of ADRs in hospitalised older people with diabetes.\u003c/p\u003e \u003cp\u003eThe primary objective of this study was to compare the incidence, prevalence, severity, and type of ADR in hospitalised multimorbid older people with and without diabetes using data from the SENATOR trial. The secondary objectives were to assess the impact of ADRs in people with diabetes on mortality, rehospitalisation, and length of stay (LOS) using logistic regression and mediation analysis.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eAnalyses were carried out using data from the SENATOR (Software Engine for the Assessment and optimization of drug and non-drug Therapy in Older peRsons) trial, clinical trial registration number NCT02097654 (clinicaltrials.gov). The study was approved by each participating institution\u0026rsquo;s research ethics committee. The SENATOR protocol has been published previously [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In brief, SENATOR was a pragmatic, multi-national, parallel-arm prospective randomized open-label, blinded endpoint (PROBE) controlled trial that tested the impact of the SENATOR software tool in reducing in-hospital ADRs in multimorbid older people with polypharmacy. The study was conducted between July 2016 to February 2018 and included a diverse population from six academic teaching hospitals across Europe (Belgium, Iceland, Ireland, Italy, Spain and United Kingdom) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study population\u003c/h2\u003e \u003cp\u003eThe trial included older people aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years with \u0026ge;\u0026thinsp;three chronic medical conditions requiring pharmacotherapy who were admitted to the hospital with acute medical or surgical illness. The main exclusion criteria were i) elective admission, ii) admission to geriatric medicine, clinical pharmacology, clinical oncology, haematology, psychiatry, palliative medicine, emergency medicine, intensive care units, iii) acute liver failure, iv) renal failure requiring dialysis, v) solid organ transplant graft, vi) non-accidental overdose/self-harm, vii) estimated life expectancy less than three months; viii) anticipated discharge/hospital transfer within 48 hours of admission and ix) admitted to hospital\u0026thinsp;\u0026gt;\u0026thinsp;60 hours at the time of planned enrolment [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Definition and adjudication of ADRs\u003c/h2\u003e \u003cp\u003eSENATOR gathered data on both prevalent and incident ADRs in the trial. Prevalent ADRs were defined as the ADR events/processes that were either the primary cause or partly the cause of hospitalization or which occurred in the emergency department or other locations in the hospital up to the time of randomization. Incident ADRs were defined as ADR events/processes that occurred between randomization and the index hospital discharge or day 14 post-randomisation, whichever occurred first. The ADRs were reported using a study-specific endpoint form based on a review of all the available documentation within the medical record including medical, nursing and allied health professional case note entries, laboratory values, radiology reports, electrocardiograms and other investigations [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe SENATOR trial defined 12 pre-specified ADRs that represent approximately 80% of all ADRs commonly reported in hospitalised multimorbid older people. The 12 pre-specified ADRs were acute bleeding, acute diarrhoea, new-onset constipation, acute dyspepsia/nausea/vomiting, acute kidney injury (AKI), symptomatic hypoglycaemia (SH), new-onset fall/s, delirium, major serum electrolyte disturbance, symptomatic bradycardia, symptomatic orthostatic hypotension and new-onset unsteady gait. Any non-pre-specified ADRs were documented as unspecified adverse events [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, a total of 13 types of ADRs were reported in the SENATOR trial. The definitions of the pre-specified ADRs are provided in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe ADRs reported using the study-specific endpoint assessment form (ADR form) were adjudicated by a blinded Potential Endpoints Adjudication Committee which ascertained the causality and severity of the ADR. The committee consisted of six blinded expert members who reviewed ADRs independently. If the first blinded endpoint committee reviewer agreed with the site principal investigator\u0026rsquo;s record of causality and severity, the decision was accepted. If not, the ADR forms were reviewed by the second reviewer. If there was no agreement after the second review, the ADR forms were reviewed by the third reviewer and a consensus was reached. Otherwise, the ADR form was adjudicated by consensus at a full committee meeting excluding the site Principal Investigator [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Causality of ADRs was assessed using the WHO-UMC causality assessment system which categorized ADRs as certain, probable, possible, unlikely and indeterminate [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and ADR severity was graded as mild, moderate and severe according to the modified Hartwig \u0026amp; Siegel severity assessment scale [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Data handling\u003c/h2\u003e \u003cp\u003eThe SENATOR trial included data from three assessments i.e., baseline, discharge or day 14 whichever occurred first and a 12-week follow-up visit. For this study purpose, patients were categorised based on the presence or absence of diabetes from their medical history (ICD-10 classification). We included patients with both Type 1 and Type 2 diabetes mellitus. The variables of interest included demographic variables (recruiting centre, age, sex, smoking and alcohol use and level of education); clinical variables (admitting ward, the total number of medications, number of co-morbidities, incidence, prevalence, severity and causality of ADRs and laboratory values such as eGFR and albumin; functional, comorbidity status and cognitive rating scale status (Barthel Index of activities of daily living, Cumulative Illness Rating Scale-Geriatric (CIRS-G) and Mini-Mental State Examination score (MMSE)); and secondary outcome variables (mortality, rehospitalisation and length of stay (LOS)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Compilation of ADRs data\u003c/h2\u003e \u003cp\u003eThe primary outcome variables included severity and causality of prevalent and incident ADRs. From the total list of unadjudicated ADRs collected in the trial, the SENATOR team excluded those suspected ADRs where no culprit drug was identified and the ADRs with no data for severity and causality before adjudication. After adjudication, the ADRs were deemed non-eligible if the severity rating was missing or graded zero and only eligible adjudicated ADRs were included in our analyses. Although the SENATOR trial data had five categories for causality, to ensure an adequate study power for our analysis, we categorised ADRs into two groups for causality i.e., indeterminate/unlikely/possible and probable/certain. This was done because the numbers of indeterminate and unlikely events were small. Similarly, the severity was grouped into two categories, mild and moderate/severe.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Potential confounders and mediators\u003c/h2\u003e \u003cp\u003eUsing standard criteria to identify confounders [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], age, sex, Barthel Index score, CIRS-G score, smoking, alcohol, number of medications and number of co-morbidities were included as confounders. Of the 12 pre-specified ADRs, only those that were clinically relevant to diabetes and those that showed significance between people with and without diabetes (AKI and SH) were considered confounders in the logistic regression analysis.\u003c/p\u003e \u003cp\u003eA mediator is a variable that is in a causal sequence between two variables, the independent and dependent variables [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. We considered the presence/absence of diabetes as our independent variable and mortality at 12 weeks, rehospitalisation at 12 weeks of discharge and LOS as our dependent variables. Based on this definition, the variables chosen as mediators were eGFR, serum albumin concentration and the 12 pre-specified ADRs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Statistical analysis\u003c/h2\u003e \u003cp\u003eIBM SPSS Statistics version 27.0 was used for data analysis. Depending on the data distribution, continuous data were presented as median (interquartile range, IQR) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). In addition to visual inspection of the distribution, skewness of minus one to plus one was considered normal distribution. Categorical data were presented as frequencies and proportions. Group differences between people with and without diabetes for continuous variables were analysed using parametric (unpaired t-test) and nonparametric (Mann-Whitney \u003cem\u003eU\u003c/em\u003e test) tests respectively for normal and skewed data distribution. Categorical data were compared using Chi-square or Fisher\u0026rsquo;s exact tests.\u003c/p\u003e \u003cp\u003eLogistic regression analysis was used to examine the relationship between having diabetes and the impact of ADRs on outcomes i.e., mortality, rehospitalisation and LOS. Models were built with a stepwise approach using multivariable logistic regression, adjusting for confounders one at a time and adding ADRs (AKI and SH) in the final model. LOS was dichotomised as \u0026le;/\u0026gt;6 as 6 days was the median LOS. Odds ratios with 95% confidence intervals and p-values were reported.\u003c/p\u003e \u003cp\u003eMediation analysis was performed according to the framework proposed by Baron and Kenny [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to assess whether mediators explain differences in outcomes in people with and without diabetes. The criteria for analysis included: Step 1) the independent variable must be significantly related to the dependent variable; Step 2) the independent variable must be significantly related to the mediator, and Step 3) the association between the independent and dependent variable must be attenuated when the mediator is included in the regression model [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The indirect effect was calculated as a*b and c is the direct effect. Sobel\u0026rsquo;s test was used to determine the significance of the effect. For all analyses, a two-sided p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Participant characteristics\u003c/h2\u003e \u003cp\u003eA total of 1537 participants were recruited to the SENATOR trial; 405 (26.4%) from Cork, 295 (19.2%) from Reykjavik, 285 (18.5%) from Aberdeen, 205 (13.3%) from Ghent, 190 (12.4%) from Madrid and 157 (10.2%) from Ancona. The mean age of the total population was 78.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4 years with 52.8% being male.\u003c/p\u003e \u003cp\u003eOf 1537 people included in our analysis, 540 (35.1%) participants had diabetes. The mean age of people with diabetes was 77.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3 years and 78.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5 years for people without diabetes (p\u0026thinsp;=\u0026thinsp;0.002). In the group with diabetes, 316 (58.5%) were male compared to 496 males (49.7%) in the group without diabetes (p\u0026thinsp;=\u0026thinsp;0.001). The median number of medications was significantly greater in people with diabetes (10 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] versus 9 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as was the number of co-morbidities (12 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] versus 9 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, both the CIRS-G score (16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8 vs. 14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Barthel index score (18 (14,20) vs. 18 (14,20), p\u0026thinsp;=\u0026thinsp;0.004) were significantly different between the two groups, with the diabetes group having greater burden of morbidity and disability. Participant characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant characteristics of people with and without diabetes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1537)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeople with diabetes\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;540)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeople without diabetes\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;997)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eRecruiting centre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAberdeen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e285 (18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAncona\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (35.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (64.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCork\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e405 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e301(74.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMadrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (44.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReykjavik\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e295 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e205 (69.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e812 (52.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316 (58.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e496 (49.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e725 (47.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e501(50.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,429 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e506 (93.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e923 (92.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e432 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e296 (29.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,105 (71.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e404 (74.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e701 (70.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school education only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e561 (36.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363 (36.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome secondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e192 (19.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete secondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e448 (29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome third-level education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (4.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplete third-level education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious documented ADR(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e669 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e224 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e445 (44.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of medications\u003c/p\u003e \u003cp\u003emedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of medical conditions\u003c/p\u003e \u003cp\u003emedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAdmitted ward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1299 (84.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e465 (86.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e834 (83.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238 (15.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePatients who experienced a prevalent ADR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e563 (36.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e360 (36.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e974 (63.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e337 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e637 (63.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePatients who experienced an incident ADR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e551 (35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e345 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e986 (64.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e652 (65.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIRS-G score (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBarthel Index ADL (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE score (median [IQR]) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSD: Standard Deviation; IQR: Interquartile Range; ADRs: Adverse Drug Reactions; MMSE: Mini-Mental State Examination; CIRS-G: Cumulative Illness Rating Scale -Geriatric (CIRS-G) score; Barthel Index ADL: Barthel Index of Activities of Daily Living.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Total population included in the analysis was 1503 for MMSE score.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Primary outcomes\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Prevalence and incidence of ADRs in the SENATOR data\u003c/h2\u003e \u003cp\u003eA total of 3247 unadjudicated putative ADRs were reported in the SENATOR trial; 886 putative ADRs were excluded as no culprit drugs were identified. Of the remaining 2361 putative ADRs, there were 1080 (45.7%) prevalent and 1281 (54.3%) incident ADRs. Of 1080 unadjudicated prevalent ADRs, 17 ADRs with missing data for causality and severity were removed and 1063 were sent for adjudication. After adjudication, 290 were determined as being non-eligible (i.e. not an ADR) leaving 773 eligible prevalent ADRs (see \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). Of the 1281 unadjudicated incident ADRs, 232 ADRs with missing data for causality and severity were excluded prior to adjudication and 1049 were sent to the adjudication committee. Following adjudication, 221 were considered non-eligible leaving 828 eligible incident ADRs (see \u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Comparison of prevalent ADRs between people with and without diabetes\u003c/h2\u003e \u003cp\u003eOf 773 prevalent ADRs, 284 (36.7%) were observed in people with diabetes versus 489 (63.3%) ADRs in people without diabetes (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of 1537 participants, 203 (37.6%) participants with diabetes versus 360 (36.1%) participants without diabetes experienced a prevalent ADR, p\u0026thinsp;=\u0026thinsp;0.56 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of severity of prevalent ADRs between people with and without diabetes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevalent ADRs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal no. of prevalent ADRs\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;773)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePrevalent ADRs in people with diabetes\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;284)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ePrevalent ADRs in people without diabetes\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;489)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate/severe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModerate/severe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute bleeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e33 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute diarrhoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew onset constipation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e26 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute dyspepsia/nausea/vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e29 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute kidney injury (AKI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e23 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptomatic hypoglycaemia (SH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew onset fall/S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e57 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelirium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e15 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor serum electrolyte disturbance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (22.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e73 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptomatic bradycardia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e12 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptomatic orthostatic hypotension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew onset unsteady gait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnspecified adverse event\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (3.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e26 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eADRs:\u003c/strong\u003e Adverse Drug Reactions\u003c/p\u003e \u003cp\u003eAmong 12 different pre-specified ADRs, SH was the only prevalent ADR with a statistically significant difference between the groups, with 13 events in people with diabetes compared to 1 SH event in people without diabetes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Of the 13 SH, one SH was mild and 12 were moderate/severe compared to only one moderate/severe SH in people without diabetes. Instances of mild AKI, falls and electrolyte disturbance, and moderate/severe delirium were more common in people with diabetes, but these differences did not reach statistical significance. Interestingly, the percentage of both mild and moderate/severe unspecified ADRs was higher in people with versus people without diabetes, (p\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e \u003cp\u003eIn terms of causality, only SH showed a statistically significant difference between the groups, 12 were probable/certain, and one was indeterminate/unlikely/possible in people with diabetes compared to just one indeterminate/unlikely/possible event in people without diabetes. People with diabetes had more indeterminate/unlikely/possible AKI and electrolyte disturbance as well as a higher proportion of probable/certain electrolyte disturbance, though none of these differences reached statistical significance. The severity and causality of prevalent ADRs are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Comparison of incident ADRs between people with and without diabetes\u003c/h2\u003e \u003cp\u003eOf 828 incident ADRs, 334 (40.3%) were observed in people with diabetes and 494 (59.7%) in those without diabetes (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Of 1537 participants, 206 (38.1%) participants with diabetes suffered incident ADRs compared to 345 (34.6%) participants without diabetes (p\u0026thinsp;=\u0026thinsp;0.17) (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of severity of incident ADRs between people with and without diabetes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncident ADRs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal no. of incident ADRs\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;828)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eIncident ADRs in people with diabetes\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;334)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eIncident ADRs in people without diabetes\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;494)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate/severe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModerate/severe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute bleeding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (5.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute diarrhoea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew onset constipation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute dyspepsia/nausea/vomiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute kidney injury (AKI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (11.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptomatic hypoglycaemia (SH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew onset fall/S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelirium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor serum electrolyte disturbance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163 (19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptomatic bradycardia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptomatic orthostatic hypotension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew onset unsteady gait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnspecified adverse event\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117 (14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eADRs:\u0026nbsp;\u003c/strong\u003eAdverse Drug Reactions\u003c/p\u003e \u003cp\u003eThere were 20 (6%) mild AKI and 27 (8.1%) moderate/severe AKI that occurred in patients with diabetes compared to 24 (4.9%) mild and 28 (5.7%) moderate/severe AKI in patients without diabetes (p\u0026thinsp;=\u0026thinsp;0.008). The culprit medications are listed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Additionally, two mild and 12 moderate/severe SH were identified in people with diabetes compared to zero SH in those without diabetes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mild bleeding and mild diarrhoea were more common in people with diabetes. Similarly, people with diabetes had a greater percentage of moderate/severe dyspepsia/nausea/vomiting, falls, delirium and orthostatic hypotension although the difference was not statistically significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of instances of certain and probable acute kidney injury by medication\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCausative medications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAcute kidney injury\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCertain and probable adverse drug reactions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePrevalent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eIncident\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePeople with diabetes (n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeople without diabetes (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeople with diabetes (n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeople without diabetes (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFurosemide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpironolactone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBumetanide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetolazone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlucloxacillin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGentamicin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-trimoxazole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRamipril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium canrenoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerindopril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePiroxicam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDexketoprofen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTorsemide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnalapril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEtoricoxib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVancomycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiology contrast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClindamycin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePiperacillin\u0026thinsp;+\u0026thinsp;Tazobactam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-amoxiclav\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanrenone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMefenamic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaproxen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmlodipine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydrochlorothiazide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn terms of ADR causality, 20 AKI events were unlikely/indeterminate/possible and 27 AKI events were probable/certain in people with diabetes compared to 26 AKI events that were unlikely/indeterminate/possible and 26 AKI events that were probable/certain in people without diabetes. All 14 SH in people with diabetes were probable/certain (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, a higher percentage of people with diabetes suffered indeterminate/unlikely/possible bleeding and constipation, and probable/certain diarrhoea, falls, and delirium in comparison to those without diabetes. Nevertheless, causality was not significantly different between the groups for any ADRs except SH. The severity and causality of incident ADRs are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Impact of ADRs in diabetic patients on secondary outcomes\u003c/h2\u003e \u003cp\u003eAll-cause mortality at 12 weeks was higher in people with diabetes, 9.1% (46/505) compared to people without diabetes, 6.3% (59/944), p\u0026thinsp;=\u0026thinsp;0.045. The number of people re-hospitalised at 12 weeks was 164 (36%) among those that had diabetes compared to 310 (35.4%) in people without diabetes (p\u0026thinsp;=\u0026thinsp;0.84). LOS was the same in both the groups (6 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] vs 6 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], p\u0026thinsp;=\u0026thinsp;0.36). The results for secondary outcomes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSecondary outcomes: comparison of mortality, rehospitalisation and length of stay between people with and without diabetes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeople with diabetes\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;505)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeople without diabetes\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;944)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality (all-cause) at 12 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePeople with diabetes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;456)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePeople without diabetes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;876)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRe-hospitalisation (all-cause) at 12 weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e474 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e164 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e310 (35.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePeople with diabetes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;534)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePeople without diabetes\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;984)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay\u003c/p\u003e \u003cp\u003e(median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eIQR: Interquartile Range\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe unadjusted model showed a statistically significant association between having diabetes and mortality (OR 1.50, 95% CI 1.01\u0026ndash;2.25, p\u0026thinsp;=\u0026thinsp;0.047). Multiple logistic regression showed a significant association remained when adjusted for age, sex and Barthel Index score (OR 1.55, 95% CI 1.02\u0026ndash;2.34 and p\u0026thinsp;=\u0026thinsp;0.039). The association did not reach statistical significance after adjustment for the burden of comorbidities. Furthermore, rehospitalisation and LOS did not show a significant association with having diabetes both in the unadjusted and multiple logistic regression models. The results for multiple logistic regression are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple logistic regression analysis showing the association between the presence of diabetes and mortality, rehospitalisation and length of stay.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRehospitalisation\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e\u003cb\u003eLength of stay\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes \u0026ndash; Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u0026ndash;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.77\u0026ndash;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.80\u0026ndash;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09\u0026ndash;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u0026ndash;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79\u0026ndash;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u0026ndash;2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80\u0026ndash;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.78\u0026ndash;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Sex, Barthel Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02\u0026ndash;2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83\u0026ndash;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82\u0026ndash;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Sex, Barthel Index, CIRSG - score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u0026ndash;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u0026ndash;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Sex, Barthel Index, CIRSG \u0026ndash; score, smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u0026ndash;2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u0026ndash;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Sex, Barthel Index, CIRSG \u0026ndash; score, smoking, alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u0026ndash;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.84\u0026ndash;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79\u0026ndash;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Sex, Barthel Index, CIRSG \u0026ndash; score, smoking, alcohol, no. of meds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u0026ndash;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u0026ndash;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.81\u0026ndash;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Sex, Barthel Index, CIRSG \u0026ndash; score, smoking, alcohol, no of meds, no. of co-morbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u0026ndash;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.76\u0026ndash;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Sex, Barthel Index, CIRSG \u0026ndash; score, smoking, alcohol, no. of meds, no. of co-morbidities, AKI (incident)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u0026ndash;1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u0026ndash;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79\u0026ndash;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Sex, Barthel Index, CIRSG \u0026ndash; score, smoking, alcohol, no of meds, no. of co-morbidities, AKI (prevalent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u0026ndash;2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93\u0026ndash;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.76\u0026ndash;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Sex, Barthel Index, CIRSG \u0026ndash; score, smoking, alcohol, no of meds, no. of co-morbidities, SH\u003c/p\u003e \u003cp\u003e(incident)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u0026ndash;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.79\u0026ndash;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Sex, Barthel Index, CIRSG \u0026ndash; score, smoking, alcohol, no of meds, no. of co-morbidities, SH\u003c/p\u003e \u003cp\u003e(prevalent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u0026ndash;2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.94\u0026ndash;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.76\u0026ndash;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eCIRS-G: Cumulative Illness Rating Scale-Geriatric; AKI: Acute Kidney Injury; SH: Symptomatic Hypoglycaemia; OR: Odds Ratio; CI: Confidence Interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Mediation analysis\u003c/h2\u003e \u003cp\u003eWhen testing the mediator role of ADRs in the relationship between having diabetes and mortality, in the first regression step, presence of diabetes was significantly associated with mortality (B\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;=\u0026thinsp;0.047). In the second step, presence of diabetes was positively associated with incident AKI (B\u0026thinsp;=\u0026thinsp;0.55, p\u0026thinsp;=\u0026thinsp;0.008). Finally, in the third step, when presence of diabetes and AKI were simultaneously included in the equation, having diabetes and incident AKI was significantly associated with mortality (OR\u0026thinsp;=\u0026thinsp;1.43, Sobel test p\u0026thinsp;=\u0026thinsp;0.048); the results are shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e. Similarly, eGFR fully mediated the association between presence of diabetes and mortality. \u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e illustrate the findings. It was not possible to carry out mediation analysis for other outcomes i.e., rehospitalisation and LOS as the results were insignificant at Step 1. Furthermore, the other 12 ADRs and serum albumin concentration were not significant at Step 2 and hence could not be analysed further.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo the best of our knowledge, the present study is the first that specifically reports the burden of ADRs in an older hospitalized patient population with diabetes with specific details of both prevalent and incident ADRs in the hospital care setting. We found a higher rate of ADRs and an increased risk of all-cause mortality at 12 weeks in those with diabetes. The higher mortality rate was mediated by medication-associated AKI and lower eGFR. Diuretics were frequently implicated as the cause of medication-associated AKI.\u003c/p\u003e \u003cp\u003eOut of 1537 participants in the SENATOR trial, more than a third of people with diabetes experienced an ADR. An observational study that used data from 2257 hospitalized type 2 diabetes mellitus patients enrolled in the Gruppo Italiano di Farmacovigilanza nell\u0026rsquo;Anziano study, conducted in community and university hospitals across Italy from 1993 to 1998 reported that 10.2% of all patients had an ADR during the hospital stay. However, that study reported the incidence of ADRs due to hydrosoluble drugs in undiagnosed renal failure patients with diabetes and is now a considerably older study [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, although some particular ADRs occurred more frequently in people with diabetes, only AKI and SH reached statistical significance. Similarly, only SH showed significance amongst the prevalent ADRs, though this is not surprising. The unadjusted regression analysis showed a significant association between diabetes and mortality. Additionally, multiple logistic regression analysis showed this association remained significant when adjusted for age, sex and Barthel Index score. After adjustment for comorbidity burden, the association between mortality and diabetes remained but no longer reached statistical significance. This is probably because this association with death is partly driven by the accumulation of comorbidities, but most of these are themselves strongly associated with, or directly caused by, diabetes. Furthermore, mediation analysis confirmed that the mortality rates were significantly higher in patients with diabetes experiencing AKI (incident ADR) accounting for the difference in outcomes. However, diabetes does not appear to influence the likelihood of re-hospitalisation or duration of inpatient stay.\u003c/p\u003e \u003cp\u003eThe higher risk of SH can very likely be attributed to a combination of tight glycaemic control, undernutrition and polypharmacy with drug-drug interactions with antidiabetic medication. Multiple studies [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] identify hypoglycaemia as the most commonly observed ADR in people with diabetes but our study, with its highly detailed ADR ascertainment processes, shows that other ADRs are more prevalent.\u003c/p\u003e \u003cp\u003ePeople with diabetes are also at an increased risk of experiencing an AKI during hospitalization. This can be explained by the fact that diabetic patients incur variable degrees of kidney damage over time, exacerbated by nephrotoxic drugs. This is particularly significant in older people, as kidney function normally declines with age [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings show how important it is to understand the burden of potentially avoidable ADRs and iatrogenic injury in the growing population of older people with diabetes. Our study is novel in that it specifically examines the relationship between diabetes and ADRs and the impact of ADRs on outcomes in a multimorbid older population, as well as its large sample size with participants from six centres across Europe. The method of ascertainment of ADRs was substantially more detailed and rigorous compared to that used in most other studies that rely on routinely collected clinical data.\u003c/p\u003e \u003cp\u003eThis study has some limitations. We could not reliably distinguish between Type 1 and Type 2 diabetes from the trial dataset, which might have provided additional insights into ADRs in hospitalised older diabetic patients. Additionally, we lacked sufficient data on the duration and control of diabetes, both factors which could influence the degree of ADR risk especially as some patients could simply be maintained on diet control and may not be on any anti-diabetic medication. Finally, incident ADRs may theoretically have been affected by the trial intervention, which sought to minimise ADRs. However, few recommendations from the trial intervention were adopted by clinicians looking after participants and the trial results showed no impact on ADRs [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] so this is unlikely to be significant. Nevertheless, further research addressing the above-mentioned limitations would help confirm and build on our findings.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, hospitalised multimorbid older people with diabetes are at a significantly higher susceptibility for developing specific ADRs, especially AKI, which increases their risk of mortality. Along with diabetic control for preventing vascular complications including renal damage, efforts to reduce polypharmacy, regular medication review and deprescribing of nephrotoxic medications and more cautious use of diuretics are recommended to reduce the AKI ADR rates and improve outcomes in this growing population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe SENATOR trial was funded by the European Union Framework Programme 7\u0026nbsp;(FP7/2007\u0026ndash;2013 grant number 305930)\u0026nbsp;and AC received the Innes Will Endowed Scholarship under the University of Aberdeen Summer Research Scholarship Programme to undertake the present study.\u003c/p\u003e\n\u003cp\u003eConflict of interest statement: On behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEdwards IR, Aronson JK. Adverse drug reactions: definitions, diagnosis, and management. Lancet 2000;356(9237):1255-1259.\u003c/li\u003e\n\u003cli\u003eTrivalle C, Burlaud A, Ducimeti\u0026egrave;re P, IMEPAG Group. Risk factors for adverse drug events in hospitalized elderly patients: a geriatric score. Eur Geriatr Med 2011;2(5):284-289.\u003c/li\u003e\n\u003cli\u003eZazzara MB, Palmer K, Vetrano DL, Carf\u0026igrave; A, Graziano O. Adverse drug reactions in older adults: a narrative review of the literature. Eur Geriatr Med 2021;12(3):463-473.\u003c/li\u003e\n\u003cli\u003eWiffen P, Gill M, Edwards J, Moore A. Adverse drug reactions in hospital patients-A systematic review of the prospective and retrospective studies. Bandolier 2002;June:1-14.\u003c/li\u003e\n\u003cli\u003eSandoval T, Mart\u0026iacute;nez M, Miranda F, Jir\u0026oacute;n M. Incident adverse drug reactions and their effect on the length of hospital stay in older inpatients. Int J Clin Pharm 2021;43(4):839-846.\u003c/li\u003e\n\u003cli\u003eJennings EL, Murphy KD, Gallagher P, O\u0026rsquo;Mahony D. In-hospital adverse drug reactions in older adults; prevalence, presentation and associated drugs\u0026mdash;a systematic review and meta-analysis. Age Ageing 2020;49(6):948-958.\u003c/li\u003e\n\u003cli\u003eYadesa TM, Kitutu FE, Deyno S, Ogwang PE, Tamukong R, Alele PE. Prevalence, characteristics and predicting risk factors of adverse drug reactions among hospitalized older adults: a systematic review and meta-analysis. SAGE Open Medicine 2021;9:1-14.\u003c/li\u003e\n\u003cli\u003eFormica D, Sultana J, Cutroneo P, Lucchesi S, Angelica R, Crisafulli S, et al. The economic burden of preventable adverse drug reactions: a systematic review of observational studies. Exp Opinion Drug Safety 2018;17(7):681-695.\u003c/li\u003e\n\u003cli\u003eDoucet J, Verny C, Bordier L, Rekik A, Zulfiqar AA, Bezerra CB, Bauduceau B. Evolution in geriatric syndromes and association with survival over 5 years in the GERODIAB cohort of older French diabetic patients. Eur Geriatr Med 2021 Jun;12(3):619-625.\u003c/li\u003e\n\u003cli\u003eKirkman MS, Briscoe VJ, Clark N, Florez H, Haas LB, Halter JB, et al. Diabetes in older adults. Diabetes Care 2012;35(12):2650-2664.\u003c/li\u003e\n\u003cli\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 2022;183:109119.\u003c/li\u003e\n\u003cli\u003eSinclair A, Saeedi P, Kaundal A, Karuranga S, Malanda B, Williams R. Diabetes and global ageing among 65\u0026ndash;99-year-old adults: Findings from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2020 April 2020;162:108078.\u003c/li\u003e\n\u003cli\u003eMata-Cases M, Franch-Nadal J, Real J, Cedenilla M, Mauricio D. Prevalence and coprevalence of chronic comorbid conditions in patients with type 2 diabetes in Catalonia: a population-based cross-sectional study. BMJ Open 2019 Oct 28;9(10):e031281-2019-031281.\u003c/li\u003e\n\u003cli\u003eRemelli F, Ceresini MG, Trevisan C, Noale M, Volpato S. Prevalence and impact of polypharmacy in older patients with type 2 diabetes. Aging Clin Exp Res 2022:1-15.\u003c/li\u003e\n\u003cli\u003eIglay K, Hannachi H, Joseph Howie P, Xu J, Li X, Engel SS, et al. Prevalence and co-prevalence of comorbidities among patients with type 2 diabetes mellitus. Curr Med Res Opin 2016;32(7):1243-1252.\u003c/li\u003e\n\u003cli\u003eDavies E, O\u0026apos;Mahony M. Adverse drug reactions in special populations\u0026ndash;the elderly. Br J Clin Pharmacol 2015;80(4):796-807.\u003c/li\u003e\n\u003cli\u003eCruz-Jentoft AJ, Carpena-Ruiz M, Montero-Errasqu\u0026iacute;n B, S\u0026aacute;nchez-Castellano C, S\u0026aacute;nchez-Garc\u0026iacute;a E. Exclusion of older adults from ongoing clinical trials about type 2 diabetes mellitus. J Am Geriatr Soc. 2013 May;61(5):734-8.\u003c/li\u003e\n\u003cli\u003eDenig P, van Puijenbroek EP, Soliman N, Mol PG, de Vries ST. Adverse drug event patterns experienced by patients with diabetes: a diary study in primary care. Pharmacoepidemiol Drug Saf 2019;28(9):1175-1179.\u003c/li\u003e\n\u003cli\u003eSingh A, Dwivedi S. Study of adverse drug reactions in patients with diabetes attending a tertiary care hospital in New Delhi, India. Indian J Med Res 2017 Feb;145(2):247-249.\u003c/li\u003e\n\u003cli\u003eElangwe A, Katte J, Tchapmi D, Figueras A, Mbanya JC. Adverse drug reactions to anti-diabetic drugs are commonest in patients whose treatment do not adhere to diabetes management clinical guidelines: cross-sectional study in a tertiary care service in sub-Saharan Africa. Eur J Clin Pharmacol 2020;76(11):1601-1605.\u003c/li\u003e\n\u003cli\u003eOgbonna B, Ezenduka C, Opara C, Ahara L. Drug therapy problems in patients with Type-2 Diabetes in a tertiary hospital in Nigeria. Int J Innov Res Dev 2014;3(1):494-502.\u003c/li\u003e\n\u003cli\u003eKoyra HC, Tuka SB, Tufa EG. Epidemiology and predictors of drug therapy problems among type 2 diabetic patients at Wolaita Soddo University Teaching Hospital, Southern Ethiopia. Am J Pharmacol Sci 2017;5(2):40-48.\u003c/li\u003e\n\u003cli\u003eDeb T, Chakrabarty A, Ghosh A. Adverse drug reactions in Type 2 diabetes mellitus patients on oral antidiabetic drugs in a diabetes outpatient department of a tertiary care teaching hospital in the Eastern India. Int J Med Sci Public Health 2017;6(3):554-558.\u003c/li\u003e\n\u003cli\u003eLavan AH, O\u0026rsquo;Mahony D, Gallagher P, Fordham R, Flanagan E, Dahly D, et al. The effect of SENATOR (Software ENgine for the Assessment and optimisation of drug and non-drug Therapy in Older peRsons) on incident adverse drug reactions (ADRs) in an older hospital cohort\u0026ndash;Trial Protocol. BMC Geriatrics 2019;19(1):1-12.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Mahony D, Gudmundsson A, Soiza RL, Petrovic M, Cruz-Jentoft AJ, Cherubini A, et al. Prevention of adverse drug reactions in hospitalized older patients with multi-morbidity and polypharmacy: the SENATOR* randomized controlled clinical trial. Age Ageing 2020;49(4):605-614.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO), The use of the WHO-UMC system for standardised case causality assessment. Available at: https://www.who.int/publications/m/item/WHO-causality-assessment, 14 Oct 2022.\u003c/li\u003e\n\u003cli\u003eHartwig SC, Siegel J, Schneider PJ. Preventability and severity assessment in reporting adverse drug reactions. Am J Hosp Pharm 1992;49(9):2229-2232.\u003c/li\u003e\n\u003cli\u003eKamangar F. Confounding variables in epidemiologic studies: basics and beyond. Arch Iran Med 2012 Aug;15(8):508-516.\u003c/li\u003e\n\u003cli\u003eMacKinnon DP, Fairchild AJ, Fritz MS. Mediation analysis. Annu Rev Psychol. 2007;58:593-614.\u003c/li\u003e\n\u003cli\u003eBaron RM, Kenny DA. The moderator\u0026ndash;mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Pers Soc Psychol 1986;51(6):1173-1182.\u003c/li\u003e\n\u003cli\u003eCorsonello A, Pedone C, Corica F, Mazzei B, Di Iorio A, Carbonin P, et al. Concealed renal failure and adverse drug reactions in older patients with type 2 diabetes mellitus. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 2005;60(9):1147-1151.\u003c/li\u003e\n\u003cli\u003eMonteiro C, Silvestre S, Duarte AP, Alves G. Assessment of suspected adverse drug reactions in elderly patients with diabetes mellitus based on a Portuguese spontaneous reporting database: analysis of reporting from 2008 to 2018 . Expert Opinon on Drug Safety 2021;20(7):845\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eSingh A, Dwivedi S. Study of adverse drug reactions in patients with diabetes attending a tertiary care hospital in New Delhi, India. Indian J Med Res. 2017;145(2):247-249.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-geriatric-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"EGEM","sideBox":"Learn more about [European Geriatric Medicine](https://www.springer.com/journal/41999)","snPcode":"41999","submissionUrl":"https://www.editorialmanager.com/egem/default2.aspx","title":"European Geriatric Medicine","twitterHandle":"","acdcEnabled":false,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Adverse drug reactions, diabetes mellitus, older people, multimorbidity, polypharmacy","lastPublishedDoi":"10.21203/rs.3.rs-3377254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3377254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose: Adverse drug reactions (ADRs) are a major cause of morbidity and mortality, especially in older people. Older people with diabetes mellitus may be at especially high risk of ADRs but this risk has not been well studied. This study aimed to compare severity and type of ADRs in hospitalised, multimorbid older people with and without diabetes and secondly to assess the impact of ADRs on mortality, rehospitalisation and length of stay.\u003c/p\u003e\n\u003cp\u003eMethods: Participants in the SENATOR (Software Engine for the Assessment and optimization of drug and non-drug Therapy in Older peRsons) trial were assessed for 12 common and ‘other’ prevalent and incident adverse drug reactions using a blinded end-point adjudication process. Descriptive analyses, logistic regression and mediation analyses were undertaken.\u003c/p\u003e\n\u003cp\u003eResults: Of 1537 people in the SENATOR trial, 540 (35.1%) had diabetes mellitus (mean age 77.4 ± 7.3 years, 58.5% male). In the total population, 773 prevalent and 828 incident ADRs were reported. Both prevalent and incident symptomatic hypoglycaemia and incident acute kidney injury (AKI) were significantly more common in people with diabetes (p\u0026lt;0.05). Patients with diabetes had higher all-cause mortality at 12 weeks than those without (9.1% vs 6.3%, p=0.04). Mediation analysis revealed that mortality was significantly higher (OR = 1.43, Sobel test p=0.048) in people with diabetes and ADRs causing AKI.\u003c/p\u003e\n\u003cp\u003eConclusions: Older multimorbid people with diabetes presenting to hospital with acute illness have significantly more ADRs than those without, and a significantly higher mortality that is mediated by medication-associated AKI and poorer renal function.\u003c/p\u003e","manuscriptTitle":"Diabetes mellitus increases risk of adverse drug reactions and death in hospitalised older people: the SENATOR trial","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-10-06 09:44:19","doi":"10.21203/rs.3.rs-3377254/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2023-10-01T10:30:31+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-10-01T08:29:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"European Geriatric Medicine","date":"2023-09-29T10:38:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-09-27T22:32:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Geriatric Medicine","date":"2023-09-25T06:02:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-geriatric-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"EGEM","sideBox":"Learn more about [European Geriatric Medicine](https://www.springer.com/journal/41999)","snPcode":"41999","submissionUrl":"https://www.editorialmanager.com/egem/default2.aspx","title":"European Geriatric Medicine","twitterHandle":"","acdcEnabled":false,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"fa4ede62-fd1b-4437-9d84-cb3639100919","owner":[],"postedDate":"October 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2023-11-16T20:19:37+00:00","versionOfRecord":[],"versionCreatedAt":"2023-10-06 09:44:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3377254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3377254","identity":"rs-3377254","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}