Multi-Year Retrospective Analysis of Mortality and Readmissions Correlated with STOPP/START and Beers American Geriatric Society Criteria Applied to Calgary Hospital Admissions

1. Introduction

1.1. Background/Rationale

A key concern is whether medications assessed as “potentially inappropriate medications” (PIMs) or “potential medication omissions” (PPOs) contribute to seniors’ hospital admissions. The STOPP/START [1] and American Geriatric Society Beers [2] are the two main criteria used for assessment. Medline and Embase were searched to 17 January 2023 with the search terms patient readmission or hospitalization and (systematic reviews or meta-analyses) to identify frequency of readmissions of patients ≥ 65 and the role of medications and other risk factors.

A systematic review identified 62 studies (two RCTs and 60 non-randomised studies with 1,854,698 patients and the average percentages (weighted by study size) for thirty STOPP/START studies (1,245,974 patients) receiving one or more PIMs were 42.8% for 1,242,010 community patients and 51.8% for 3,964 hospitalised patients. For nineteen Beers studies (595,811 patients) the average percentages for one or more PIMs were 58% for 593,389 community patients and 55.5% for 2,422 hospitalised patients [3].

A systematic review of 30 RCTs (n = 11,693) assessed whether primary care could improve post-discharge care. In ten studies patient information was exchanged between health professionals at and after discharge and for the intervention group the relative risk of 30-day hospital readmission compared to control was (RR 0.68; 95%CI 0.56, 0.84) (16.5% vs. 30%) and for 6-month readmission was (RR 0.83; 0.75, 0.92) (35% vs. 38%) [4].

Three prospective studies of readmissions identified increasing numbers of readmissions per patient over time. For the PAERPA study 2015-2017 of 24,500 patients > 75 in the Hauts de France region the best predictor of readmission and death was the progressive numbers of readmissions. The relative risk of admission steadily increased after the first admission (RR 1.8; 1.7, 1.9) to after the fifth admission (RR 3.0; 2.6, 5.5) and the risk of death also steadily increased after the first admission (RR 1.1; 1.07, 1.11) to after the fifth (RR 1.3; 1.1, 1.5) [5].

During the DAMAGE study of 3,081 patients ≥ 75 in six acute French geriatric units again the best predictor of admission and death was prior admissions. The relative risk of readmissions steadily increased after the first admission (RR 1.31; 95%CI 1.08, 1.60) to the fifth readmission (RR 2.66; 1.44, 5.14) and the relative risk of death increased after the first admission (RR 1.61; 1.48, 1.76) to after the fifth readmission (RR 2.01; 123, 3.32). [6].

A prospective study of 625 patients ≥ 70 in two Belgian general teaching hospitals found the 30-day readmission rate was 12.3% and the risk factors were hospitalisation in the previous three months (OR 2.21; 1.24 to 3.87), longer prior stay (OR 2.79; 1.31 to 5.67) and discharge diagnoses for respiratory disease (OR 3.49; 1.88 to 6.42) or genito-urinary illness (OR 5.91; 2.24 to 14.54) [7].

There are two national retrospective cohort studies of readmissions and again the readmission rate increased over time. In New Zealand 2009-10 66,983 patients ≥ 65 had 95,318 admissions. On their first admission 13.3% were readmitted within 30 days and 23.8% within 90 days, and 4.6% died in hospital within 30 days and 8.5% within 90 days. On the second admission 26.1% were readmitted within 30 days and 40.9% within 90 days and 6% died in hospital and 15.4% within 90 days. [8].

In Switzerland, the Swiss Diagnosis-Related Groups reimbursement system was adopted nationwide in 2012 to provide financial incentives to reduce length of stay but there were, however, small increases in readmission rates. For 2,426,722 medical admissions 2009 through 2015 (median age 70, IQR 55-81) there was a small increase in the 30-day readmission rate (14.4% pre- and 15% post-DRG) and a decline in in-hospital mortality (4.9% pre- and 4.6% post-DRG). However, the readmission rates pre- and post-DRG for the 398,479 patients with the five main diagnoses increased for acute myocardial infarction (from 13.7% to 22%), COPD exacerbation (from 17% to 18.2%), acute heart failure (from 14.1% to 16.1%), community-acquired pneumonia (from 10.6% to 11.3%), and pulmonary embolism (from 7.8% to 9%) [9]. Retrospective studies also identified similar high readmission rates. [10,11,12,13].

Thus, there is an urgent need to identify why seniors have so many readmissions and how the readmission rate can be decreased.

1.2. Objectives

1. To measure in this retrospective database of 129,443 first admissions and 155,758 readmissions of Calgary seniors 2013-2021 the relative contributions of risk factors for mortality and readmissions identified in the literature review. 2. To identify cohorts at highest risk of mortality and readmission.

3. To identify the costs of readmissions and how much of these costs could be freed up for interventions by teams of family physicians, pharmacists and home visiting nurses to maintain patients as long as possible in their own homes and avoid readmissions.

3. Methods

3.1. Study Design

Retrosspective cohort database study of the Province of Alberta’s DIMR staff anonymised admission, hospital and discharge records (file DAD) of all individuals ≥ 65 years admitted to the four main acute care hospitals in Calgary with ICD-10 diagnoses and ICC procedure codes in the Province of Alberta DIMR database, and prescribing data in the Patient Information System (PIN).

3.2. Ethics Approval

Conjoint Health Research Ethics Board (CHREB), Research Services, University of Calgary (Project REB15-2163).

3.3. Setting

Retrospective database of 129,443 first admissions of Calgary seniors 2013-2021 and their 155,758 readmissions

3.4. Participants

All individuals ≥ 65 years admitted to the four main acute care hospitals in Calgary 2013-2021.

3.5. Variables

Demographics (age, sex); medical status [illnesses [Medical Council of Canada codes], number of illnesseses, number of medications, Charlson scores), PIMs, PPOs, corrected PPOs, readmissions, mortality.

3.6. Data sources/Measurement

The Province of Alberta’s DIMR staff anonymised admission, hospital and discharge records (file DAD) of all individuals ≥ 65 years admitted to the four main acute care hospitals in Calgary with ICD-10 diagnoses and ICC procedure codes in the Province of Alberta DIMR database, and prescribing data in the Patient Information System (PIN). The 2015 STOPP/START medications [1] and the 2019 AGS BEERS criteria [2] were linked to ATC codes.

3.7. Bias

All output data were independently checked by two authors

3.8. Study Size

All individuals ≥ 65 years admitted to the four main acute care hospitals in Calgary 2013-2021 (129,443 first admissions, 155,758 readmissions). No exclusions.

3.9. Quantitative Variables

The Alberta Health Services DAD and PIN data were combined using patient identifier and admission time windows and any duplicate records in the provided source files were eliminated in the course of processing.

3.10. Statistical Methods

There is no publicly available software for applying STOPP/START or AGS Beers criteria to electronic medical records. The processing and analysis codes developed in the course of this research project were built using a combination of Oracle Database 19c (PL/SQL), R and Excel, and statistical analyses (logistic regressions) were conducted within those databases. Patient outcomes were analysed by age, sex, number of medications on admission and discharge, number of comorbidities, Charlson Index, PIMs, PPOs and corrected PPOs

Missing data were not replaced because the dataset was anonymised by Alberta Health Services and we had no access to individual patient charts.

All patients admitted to the four Calgary acute care hospitals were entered in a unified data system. If patients went out of province and their records were not thereafter incorporated into the AHS database we had no way of knowing about any out of province medical events.

Logistic regressions adjusted raw outcome data for age on admission, sex and number of comorbidities

The DAD and PIN data were combined using patient identifier and admission time windows and any duplicate records in the provided source files were eliminated. The strategy utilised for joining these two data sets consisted of matching prescriptions for a given patient ID by date, identifying prescribed medications one month prior to admission and 180 days post admission, removing any prescriptions that fell into overlapping visit date windows as well as any duplication (Figure 1). The admission window is variable and not shown to scale.

Coupled with the six-month post discharge time window studied, the discharge supply (average 34 days, median 28) amounted to 9,342,962 post discharge prescription records for the 129,443 patients in the study. Subsequent processing excluded from this raw count any duplicate medications along with any medications counted as part of preadmission prescriptions. The prescriptions associated with a given visit were then categorised by type because of the wide variety of brand name and generic medications encountered over the cohort time period and also to simplify correlation with STOPP/START and AGS Beers criteria. The admission data also included diagnostic information for the visit, and this was used to calculate the criteria results for a given visit. Where required, laboratory results for eGFR (estimated glomerular filtration rate) were included to support calculations.

The results per visit for criteria are generally binary (medication is present or not). For AGS Beers they are ternary (values of 0,1,2), with 0 meaning no medication, 1 meaning diagnosis with no recommended medication and 2 meaning diagnosis with recommended medications.

There is no publicly available software for applying STOPP/START or AGS BEERS criteria to electronic medical records. The individual criteria were coded as Oracle database functions and applied to the raw data. This design allowed rapid processing and modularity for recalculation. The final results table was analysed using R Studio and Markdown. For calculations of Odds Ratios, the Generalized Linear Model (GLM) was used to apply logistic regressions (family parameter set to binomial). The processing and analysis code developed in the course of this research project was built using a combination of Oracle Database 19c (PL/SQL), R and Excel.

4. Results

4.1. Summary of the Characteristics of the Patients with a First Admission 2013-2021 Admitted to the Four Acute Care Hospitals in Calgary

In Table 1, descriptive statistics are shown for a total of 285,201 visits for 129,443 unique patients of both genders.

The number of first admissions for which data were available across all three Alberta Health Services databases (medical, surgical, and procedures; medications; and laboratory results) was 129,443 (median age 76 years). On admission they had a median of three medications and at discharge a median of nine (Table 1).

4.2. Numbers of Patient Admissions to the Four Acute care Calgary Hospitals 2013-2021

Data were obtained from three Alberta Health Services datasets and the total numbers reported in the tables vary according to how much information could be joined across all three datasets. The initial admission 2013-2021 was of 129,443 patients, the second of 64,441 of those patients, the third of 35,206, the fourth of 20,354, the fifth of 12,271 and the sixth of 7,577 patients. Another 14,951 patients had 7 to 15 admissions, and 843 had 16-25 admissions. A very small number of patients had more than 25 admissions during this period: the final 115 pa tients had 26-39 admissions. There were slightly more male (144,908) than female (140,293) admissions (Table 2).

4.3. Numbers of Admissions, Readmissions, Length of Stay, and Death in Hospital and in the Next Six Months

The percentage of patients who died during their first hospital admission was 3% and the percentage increased to 7.21% by the sixth admission and the average for the 7th through 39th admissions was 7.54% (Table 3). Of the 129,443 first admissions a large number (28,292; 21.9%) were readmitted within six months. The percentage of readmissions within six months rose steadily to the sixth readmission (45.2% and then averaged 53.5% for the 7th through 39th readmissions. The percentage who died within six months of an admission increased from 9.4% after the first admission to 24.9% after the sixth admission and then averaged 26% for the 7th through 39th admissions. The average stay rose from 9.8 days for the first admission to 13 days for the 6th and then averaged 11.7 days thereafter. The relative risk of readmission rose from 49.8% for a second after a first admission and to 61.8% for a 6th after a 5th readmission. For the smaller numbers of admissions after the 7th the relative risk varied between 65% and 100% with the numbers of readmissions becoming much smaller as the multiple readmissions progressed.

4.4. Principal Diagnoses for Admissions 1 through 39, and also Annually for Each Year 2013-2021

One method of visualising the large numbers of principal admitting diagnoses is to use “heat maps,“ which show larger numbers of patients in hot colours (e.g., red) and smaller numbers in cooler colours (pale yellow and green). The principal admitting diagnoses are shown in Figure 2 and Figure 3 as heat maps and the numerical data are also shown in the text below.

The most frequent principal admitting diagnoses (Figure 2) were for cardiac problems (34,277): heart failure (12,159); arrhythmias without cardiac catheterization (4,031); percutaneous coronary intervention with MI/Shock/Arrest/Heart Failure (3,706); Pacemaker implantation (3,555); Myocardial Infarction/Shock/Arrest without cardiac catheterization (2,608); Other cardiac disorders (2,326); Cardiac Valve Replacement (2,206); Percutaneous Coronary Intervention without MI/Shock/Arrest/Heart Failure (1,932); and Coronary Artery Bypass without Cardiac Catheterizations, without MI/Shock/Arrest, with/without pump (1,724).

The second most frequent principal admitting diagnoses were orthopedic procedures (25,610): Knee replacement (10,139); Hip replacement (5,924); Fixation/repair Hip/Femur (3,754); Spinal Vertebrae intervention (3,106); and Hip Replacement with trauma/complication of treatment (2,687).

The third most frequent principal admitting diagnoses were infections (22,364): Viral/Unspecified Pneumonia (6,019; Lower urinary tract infection (5,334); Other/Unspecified Septicemia (3,276); Enteritis (3,143); Severe Enteritis (2,402); and Aspiration pneumonia (2,190).

The fourth most frequent principal admitting diagnoses were pulmonary (15,637): Chronic Obstructive Pulmonary Disease (COPD) (10,995; Pulmonary Embolism (1,944); and Malignant Neoplasms of Respiratory System (1,698). The fifth most frequent principal admitting diagnoses were Central Nervous System (13,308): Ischemic Event Central Nervous System (4,884); Organic Mental Disorder (4,633); and Dementia (3,791). The sixth most frequent principal admitting diagnoses were Renal (8,890): Minor Intervention on Upper Urinary tract (3,572); Renal Failure (2,604); Non-Major Intervention on Lower Renal Tract (2,409), and Disorder Fluid/Electrolyte Balance (2,305). The seventh most frequent principal admitting diagnoses were Gastro-Intestinal (6,505): Gastrointestinal Hemorrhage (2,962); Symptom/Sign of Digestive Problem (1,848); and Laparoscopic Cholecystectomy (1,695). There were 5,897 operations for Partial Excision/Destruction of the Prostate.

This pattern of serious illness was also seen in each of the admissions in each of the years 2013-2021. Figure 2 lists the top 20 of the 40 most frequent diagnoses. he Medical Council of Canada diagnoses (MCC) (Figure 3) with the darker red colour highlight the anatomic systems with the most numerous primary admissions and show the persistence of admissions with these organ system problems.

4.5. The numbers of Medications Pre-Admission and on Discharge for all Admissions

On pre-admission there were 50,000 patients who had no medications and the numbers of medications declined steadily and sharply from two to 15 medications. However, there was a dramatic increase in the number of medications post-discharge with a median number of nine medications then tapering steadily to thirty medications (Figure 4).

4.6. Class and Numbers of Medications Prescribed: All Admissions 2013-2021

Of the top ten most frequently prescribed medications seven were for cardiac conditions: beta blockers (3,987,449 prescriptions), statins (2,706,992 prescriptions), calcium channel blockers (2,343,022 prescriptions), ACE inhibitors (1,786,653 prescriptions), antiplatelet agents (1,724,720 prescriptions) and ARBs (1,363,172 prescriptions) (Supplemental Table 1).

4.7. Costs of Admissions 2013-2021 Measured by Resource Intensity Weighting

The costs for the 1st to 19th admissions are shown (data are not shown beyond 19th readmission because of the small numbers). The Canadian Institute of Health Information (CIHI) designed the Resource Intensity Weighting (RIW) method to estimate costs of individual patient hospital admissions across Canada, with separate estimates for each province. The “cost per weighted case” (CPWC) 2022 measures a ratio for each province and for the whole of Canada. The ratio is a hospital’s total acute inpatient care expenses compared to the number of acute inpatient weighted cases. The weighted cases used are CMG+ 2022 (CIHI’s most recent case mix grouping methodology) [14,15]. Payments to physicians are in a separate confidential file we could not access. Resource Intensity Weights measure the intensity of resource use (i.e., relative cost) for the diagnostic, surgical, procedure and medical care of an individual. RIWs are assigned according to the case mix group to which an individual is assigned, and also include the patient’s age, health status, and discharge status. Micro-costing uses CIHI Complexity Overlay (CMG Plx™). We computed that the RIW data are skewed for each year 2013-2021 (due to some very expensive admissions). However, because this is a very large dataset comprising nine years of financial data, it is expected that the skewness will be a persistent feature of the RIW and the RIW methodology has been tested by CIHI so that they form the most reliable available data on costs. The average cost of a hospital stay in Alberta 2020-2021 was CAD$9149 and for Canada CAD$7619. In the Calgary dataset 2013-2021, the estimated RIW of a senior’s admission ranged from 1.527 to 11.727 times more expensive compared to the average cost of an admission (Figure 5). The heat map shows the consistently high costs (red colour) occurring in admissions for ear, nose mouth and throat diseases, dementia, rehabilitation, burns, circulatory system, cancer, and the respiratory system. RIWs tended to be very high when complex cancer surgery and reconstruction was involved (Figure 5). Using the RIW cost data available for the 285,201 patients for whom we had data from all three AHS databases, we extrapolated the costs for the full sample of 295,708 patients. Using Alberta costs the total cost of all seniors’ admissions to the four acute care hospitals in Calgary 2013-2021 was $7,477,391,068 and if average Canada costs were used $6,571,010,108. Using Alberta costs the cost of the 2nd through 39th admissions 2013-2021 was $2,762,343,130. If 10% could be saved from the 2nd through 39th admissions that would provide $276,234,313 for teams of family physicians, home visit nurses, pharmacists and other professionals to keep senior patients at home and avoid readmissions. If 20% could be saved then $552,468,626 would be available. Seniors’ admissions include many surgeries and there would be limited opportunity for cost saving from surgeries.

Source: Canadian Institute of Health Information, The Cost of a Standard Hospital Stay (also referred to as “cost per weighted case (CPWC)” 2022. measures a ratio for each province and for the whole of Canada. The ratio is a hospital’s total acute inpatient care expenses compared to the number of acute inpatient weighted cases. The weighted cases used are CMG+ 2022 (CIHI’s most recent case mix grouping methodology). Micro-costing uses Complexity Overlay (CMG Plx™) methodology [14,15].

4.8. Correlations with Readmissions and Mortality 2013-2021

For all admissions (Table 4) The (unadjusted) odds ratios for readmission were markedly elevated for START PPOs not prescribed (1.58; 1.55-1.61), STOPP PIMs (1.17; 1.17-1.17), and to a lesser extent AGS Beers PIMs (1.11; 1.11-1.12).

The most adverse unadjusted odds ratios for mortality within six months were for START PPO medications which were needed but not prescribed (2.16; 2.11-2.21). Patients with higher Charlson scores also had a high odds ratio of mortality (1.43; 1.42-1.43). Patients assessed as having any Resource Intensity Weighting above the average for all admissions also had higher odds ratios of mortality (ranging between 1.39 for RIW 1-2 to 2.86 for RIW >9). The number of comorbidities increased the odds ratio of mortality (1.19; 1.18-1.19) and also the number of medications assessed after admission (1.19; 1.18-1.19). The only (unadjusted) risk factor which had ORs including unity were START PPOs which were correctly prescribed with an odds ratio of mortality of (0.99; 0.97-1.01).

When odds ratios were corrected for age, gender and comorbidities the risk of mortality for STOPP PPOs not corrected remained elevated (1.56; 1.50-1.63) but when a prescription was given the ORs were markedly reversed (0.51; 0.49-0.53), and this was the only factor that provided improvement (and a major improvement) in the odds ratios of mortality.

For the first admission (Supplemental Table 2) the corrected odds ratio for readmission was elevated for START PPOs not prescribed (1.39; 1.35-1.42), for STOPP PIMs (1.16; 1.15-1.16), AGS Beers PIMS (1.11; 1.11-1.11) and number of medications (1.12; 1.12-1.12). Odds ratios were elevated for correctly prescribed STOPP PIMs (1.26; 1.23-1.30). Interpretations are that these patients received correct prescriptions but their illnesses needed time to improve, or that they received increased supervision resulting in readmissions

The ORs (adjusted for age at admission, gender and comorbidities) for mortality within six months were elevated for START PPOs not corrected by a prescription (1.56; 1.50-1.63) but mortality was markedly reduced if the PPOs were corrected by prescribing for the patient (0.51; 0.49-0.53).

The next result that was of key interest in this research project was to assess whether the odds ratios of further admissions) and mortality improved or worsened during subsequent admissions. The same pattern of a marked reduction in mortality if PPOs were correctly prescribed persisted in all subsequent admissions (Supplemental Tables S3-S8).

However, odds ratios of readmission were consistently higher if START PPOs were corrected: second admission (1.36; 1.31-1.41); third admission (1.42;1.36-1.49); fourth admission 1.50;1.41-1.59); fifth admission 1.49; 1.38-1.60); sixth admission (1.64; 1.49-1.81) and 7th through 39th admission (1.53; 1.41-1.65). It will be important to examine each patient’s illness and progress to identify the reasons for this apparently anomalous result. The possibilities are that the patient has other illnesses that cause admissions; prescribing to correct PPOs identified illnesses and their amelioration is still underway; the patients received increased monitoring and were more likely to be admitted for follow-up care; or the patient, carer and primary care team were concerned about deterioration from other illnesses and sought hospital care.

4.9. Destination after Discharge from the Four Calgary Acute Care Hospitals 2013-20221

After their admissions, only 44% of patients returned directly to their homes or to a lodge (residences in Canada in which patients are expected to be able to look after themselves with minimal supervision) (Figure 6). An important focus of this project was to identify the experiences of the Calgary seniors during their multiple admissions. Increasing the numbers of discharges direct to home with continued long-term residence in their own homes supported by a team of family physicians, pharmacists and home visit nurses would be an outstanding benefit.

5. Discussion

5.1. Key Results

There was a dramatic decrease in mortality over all admissions when PPOs were corrected with a prescription (OR 0.51; 95%CI 0.48-0.53; p <.001) but a marked increase in mortality if PPOs were not corrected with a prescription (OR 1.56; 1.50-1.63; p <.001). There was an increase in mortality if patients received a Beers AGS PIM (OR 1.08; 1.07-1.08; p < .001) but not a STOPP PIM (OR 0.99; 0.96-1.00; p <.001) (all data corrected for age, gender and comorbidities).

There was an increase in readmissions if START PPOs were not corrected (OR 1.39; 1.35-1.42; p <.001) but also if they were corrected (OR 1.26; 1.23-1.30; P<.001) and the possible reasons for this unexpected finding need to be investigated. The possibilities are that the patient acquired other illnesses, prescribing to correct PPOs was underway but had not ameliorated the illnesses, or patients received increased monitoring including readmissions. STOPP PIMs also correlated with readmissions (OR 1.16; 1.16-1.16; p <.001) as did AGS Beers PIMs (1.11; 1.11-1.11; p<.001).

5.2. Admission Costs

When hospital costs for the province of Alberta are applied to the Calgary sample the total cost of all seniors’ admissions to the four acute care hospitals in Calgary 2013-2021 was $7,477,391,068 and when average Canadian costs are utilised the costs were $6,571,010,108. For the 2nd through 39th admissions 2013-2021 the Alberta cost was $2,762,343,130. If 10% were saved by reducing the numbers of 2nd through 39th admissions this would provide $276,234,313 for home care and a 20% saving $552,468,626.

5.3. Previous Studies of Interventions to Reduce PIMs, PPOs and Admission Costs

Two key systematic reviews emphasised the importance of comprehensive patient-centred rather than system-centred interventions. A systematic review and meta-analysis of 42 RCTs of the complexity of interventions to prevent readmissions of those ≥65 rated most studies on the Cochrane Risk of Bias tool at low risk with the most frequent problem being no reliable method of dealing with missing data. The relative risk of 30-day readmission was lower in the intervention arms (0.82; 0.73, 0.91), and was lower when the intervention augmented patient self-care capacity (RR 0.68; 0.53, 0.86) compared to when it did not (RR 0.88; 0.80, 0.97); had five unique components (RR 0.63; 0.53, 0.76) compared to when it did not (RR 0.91; 0.81, 1.01); and when at least two individuals were involved in intervention delivery (RR 0.69; 0.57, 0.84) compared to when they were not (0.87; 0.66. 0.98). Compared to Category 1 interventions (which provided no comprehensive support) Category 3 interventions provide a consistent and comprehensive strategy that emphasises the assessment and treatment of factors of patient context and self-care (caregiver contributions, functional status, impact of comorbidities, patient and caregiver goals for care and potential for self-management) and socioeconomic factors. Studies with Category 3 interventions had a lower relative risk of admission of 0.63 (0.43, 0.91) [16].

A systematic review of 15 systematic reviews of integrated care assessed the average AMSTAR 1 score of the reviews as seven and identified whether the 219 included studies incorporated all the elements in the WHO Integrated care for Older People approach (WHO 2016, WHO 2016). All the interventions included case management and multidisciplinary planning and/or care delivery and multiple care providers (nurses in 12 studies, physiotherapists in 10, GPs in nine and social workers in nine). However, the outcomes in most reviews were service or system centered and not focused on patient care experiences, whereas the key WHO focus is for interventions to be patient-reported outcome measures (PROMs) and patient-reported experiences (PREMs) [17]

Three systematic reviews of specific interventions (physician-pharmacist collaboration, multidisciplinary teams, pharmacist counselling) assessed the evidence as low quality and thus conclusions about these interventions cannot be made. A systematic review of physician and pharmacist collaboration in primary care to reduce readmissions of adult patients included 16 RCTs. The Cochrane Risk of Bias tool assessed 16/16 studies as low risk for randomisation, 11/16 for allocation concealment, 0/16 blinding of participants and personnel, 15/15 blinding of outcome assessors, 15/16 completeness of outcome data, and 16/16 no selective reporting but the GRADE study quality was low. Despite face-to-face communication between pharmacists and primary care physicians in all studies, there was no significant effect on readmissions (RR 0.92; 0.80-1.06) and low certainty of evidence [18]. A systematic review of 11 RCTs (n = 7,496 ≥ 65) of care in acute geriatric units on the Cochrane Risk of Bias tool assessed four at high risk of bias for randomisation and two for allocation concealment and only five performed an intention-to treat analysis. The multidisciplinary team included a geriatrician and/or primary care physician and a nurse with geriatric training and there was only a small difference in the relative risk of living at home after three months (RR 1.06; 0.99, 1.13) [19].

A systematic review identified 62 RCTs of pharmacists counselling patients and for the 29 RCTs of pharmacists counselling patients to adhere to their prescribed medication adherence increased (RR 1.30; 1.19, 1.43) but the GRADE assessment of quality was low to very low. For 11 RCTs which assessed effects on 30-day readmission rates the RR was 0.76 (0.58, 0.99) [20]. Another systematic review of 18 RCTs of pharmacist medication reviews rated 10/18 as high risk on the Cochrane risk of bias tool and readmission rates did not differ between the experimental and control groups (RR .97; 0.89, 1.05) [21].

Two small studies identified that polypharmacy and PIMs predicted readmissions. A study of 647 patients in acute geriatric wards in southern Italy in 2013 found that polypharmacy predicted 3-month readmissions (OR 2.72; 1.48-4.99) but STOPP (1.60; 0.85, 3.01) and AGS BEERS criteria (0.85; 0.46-1.56) did not [22]. In a study of 259 patients discharged from a general medicine unit in Scotland during the 41.5-month follow-up period: 50% died and a PIM was associated with three or more readmissions (OR 2.43; 1.19-4.48) and with mortality (OR 2.51; 1.20, 5.28) and a PPO with mortality (OR 1.88; 1.09, 3.27 [23].

Thus, key concepts in planning interventions to reduce readmissions are presented in Leppin’s [16] systematic review which found that reductions in readmission rates were most likely if interventions improved self-care capacity, had five or more unique components and involved two of more intervenors.

5.4. Strengths

This database is the largest of first admissions (129,443) and of readmissions (155,758) over the longest period of nine years (2013-2021) in the literature.

5.5. Weaknesses

We were not able to obtain the costs that physicians billed for hospital care as this was in a separate confidential file. We did not have access to individual patient files and were unable to discover why physicians corrected some PPOs with a prescription and other PPOs not or the reasons why additional medications were started during readmissions (some were PIMs). We were unable to identify adverse drug reactions because during the time of the study there was no specific category for ADRs in the hospital records. We would have preferred to use Cox proportional hazards models to accurately assess survival but the non-proportional hazard assumption was not met and so we used logistic regression.

5.5. Generalisability

The study is generalisable to the Calgary population as a whole because the numbers of patients by age group and gender admitted to the four Calgary acute care hospitals are representative of the Calgary population as measured by the 2022 Canadian Census (Supplemental Table S9).

The study population is also similar to the five-year age groupings of Alberta, Canada and the US and could be generalised to those jurisdictions with appropriate caution for differences in ethnic composition, access to medical care and prescribed medications.

However, generalisability could be reduced for populations with a substantially different ethnic composition. Twelve key P450 isoforms metabolise more than 70% of medications. Many medications also either inhibit or enhance metabolic rates in these isoforms, thus affecting how other medications are metabolised. Ethnic differences within populations are particularly important because of major differences between ethnic groups in their P450 enzymes and the alleles they inherit from each parent and how these affect drug metabolism, drug-drug and drug-gene interactions. For example, two thirds of anti-depressants are metabolised by two isoforms, CYP2C19 and CYP2D6. Patients with the CYP2C19 Null/Null genotype have 0% metabolic activity for medications which are metabolised by the CYP2C19 P450 isoform, whereas those with CYP2C19*17/*17 have two ultrarapid metabolising alleles and they metabolise at 120% of the normal rate, and for the CYP2D6WtX3 genotype (which has two ultrarapid alleles) the metabolic rate is 150% [24,25].