Abstract
Elderly patients with multiple concomitant chronic diseases are the particularly vulnerable during the Coronavirus disease 2019 (COVID-19) epidemic, which accounts for a large number of COVID-19-related deaths. The purpose of the study was to investigate the impact of polypharmacy and potentially inappropriate medications (PIMs) on in-hospital mortality in a secondary hospital in China. A cross-sectional, retrospective study was conducted using electronic medical data collected from Shanghai Gonghui Hospital from April 2022 to June 2022. Two types of PIMs were adopted, involving the evaluation of the PIM status of older patients with COVID-19 (age ≥ 60 years) identified by AGS/Beers Criteria (PIM-Beers) and the PIM related to Covid-19 antiviral NMV/r (Nirmatrelvir/ritonavir) (PIM-NMV/r). Multivariate logistic regression was used to identify the risk factors associated with PIM use and in-hospital mortality. A total of 617 older COVID-19 inpatients were included in the study. The prevalence of polypharmacy and excessive polypharmacy were 24.6% and 19.1%, respectively. The prevalence of PIMs, PIM-Beers, and PIM-NMV/r were 25.8%, 22.5%, and 60.8%, respectively. Multivariate regression demonstrated that male (OR: 0.57 [95% CI: 0.33–0.98], p = 0.044), diabetes (OR: 2.05 [95% CI: 1.11–3.80], p = 0.023), the more number of medications (OR: 1.44 [95% CI: 1.20–1.72], p < 0.001) and given NMV/r (OR: 3.67 [95% CI: 1.48–9.10], p = 0.005) were influencing factors associated with PIM use. A multivariate logistic regression demonstrated that severe COVID-19 (OR: 6.56 [95% CI: 1.13–38.03], p = 0.036), polypharmacy (OR: 15.43 [95% CI: 3.20-74.29], p = 0.001), excessive polypharmacy (OR: 51.09 [95% CI: 5.23-499.52], p = 0.001), and long-term hospitalization (OR: 0.08 [95% CI: 0.02–0.32], p < 0.001) were influencing factors associated with in-hospital mortality in older COVID-19 inpatients. The polypharmacy and drug-drug interactions of NMV/r were observed in many older COVID-19 inpatients. Older patients with severe COVID-19, a higher number of medications and long-term hospitalization had a higher in-hospital mortality. This result highlights the importance of conducting clinical pharmacists-led medication reviews to identify PIMs of NMV/r and collaboratively working with the physicians to ensure medication appropriateness.
Subject terms: Diseases, Health care, Risk factors
Since the outbreak of Coronavirus disease 2019 (COVID-19) was first reported in China in December 2019, COVID-19 has swept across the world, bringing a huge impact on the livelihood of human society. In addition to the Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2) variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the novel Omicron (B.1. 1.529) variant, although causing reduced mortality, spreads very rapidly1. Sore throat, hoarse voice, and unusual joint and muscle pains are the obvious symptoms of the novel Omicron variant that distinguishes it from other types of variants2. Meta-analysis of retrospective cohort studies indicated that older age was associated with a greater risk of death in patients with COVID-193. Moreover, SARS-CoV-2 infection increased the 1-year mortality in both hospitalized and non-hospitalized elderly patients4. Since the outbreak in Shanghai, official report data from February to May 2022 retrieved by the Shanghai Municipal Commission of Health showed that the overall infection rate, severe/critical infection rate and mortality rate were 2.74/100 people, 6.34/100,000 people, and 2.42/100,000 people, respectively. The rate of serious/critical infections and mortality tended to increase with age5.
Polypharmacy, defined as five or more drugs prescribed6, has been associated with a higher risk of adverse drug events, drug-drug interactions, emergency department visits, hospitalizations, and even mortality7–9. The American Geriatrics Society (AGS)/Beers criteria was the first guideline that reached expert consensus on geriatric potentially inappropriate medication (PIM) now in their seventh iteration in 202310,11. Although successful development of the vaccine and widespread vaccination can prevent infection from SARS-CoV-2, the emergence of new variants of the virus diminishes the efficacy against infection. Many vaccinated people are still infected by the Omicron variant, which constantly prompts the demand to develop novel drugs, such as remdesivir12, molnupiravir13,14 to combat COVID-19. The COVID-19 antiviral Nirmatrelvir/ritonavir (NMV/r) (Paxlovid®), developed by Pfizer Inc., has been approved for the treatment of adults with mild to moderate COVID-19, who are at higher risk of progressing to severe COVID-19 in many countries15. The National Medical Products Administration in China also granted conditional approval for its import registration on February 11, 202216. However, the clinical use of NMV/r remained limited until the outbreak in Shanghai.
Several studies have reported the prevalence of polypharmacy and PIM use among elderly inpatients during different periods of the COVID-19 epidemic in different countries17,18. However, no data are available to assess polypharmacy, PIMs, drug-drug interactions with NMV/r, and in-hospital mortality in COVID-19 inpatients during the strict intervention policies were conducted in Shanghai. This study aimed to evaluate the prevalence of polypharmacy, PIMs, and drug-drug interactions with NMV/r in association with the outcomes of COVID-19 inpatients to provide more relevant evidence for further research and clinical practice.
Materials and methods
Patients
This was a cross-sectional, retrospective study in Shanghai Gonghui Hospital. The source population for this study was geriatric patients with COVID-19 as part of routine clinical care from 11 April to 30 June 2022 in Shanghai Gonghui Hospital. Geriatric patients who fulfilled the following criteria were eligible for inclusion in the study: ≥60 years of age, with at least one ICD-10 diagnosis code for COVID-19 (U071; disease diagnosis of COVID-19 confirmed by laboratory testing) listed as the primary or secondary reason for their inpatient hospital admission and with at least one medication. Patients with incomplete electronic medical records including diagnosis, sex, route of administration, medication dosage, and so on were excluded from the study.
Data collection
In this retrospective study, we included geriatric patients with COVID-19 in Shanghai Gonghui Hospital from 11 April to 30 June 2022. We obtained the following data from the electronic medical records: (1) patient characteristics (age, sex, diagnosis, comorbidities); (2) medication characteristics (generic name, dosage form, administration route, dosage, frequency of administration); (3) other information (length of stay, total hospital costs, medication costs, treatment outcomes). The criteria in the count of prescribed medications are as follows: (1) Chinese traditional herbal medications were not included; (2) Different routes of administration of the same drug count as one drug.
Evaluation criteria
Polypharmacy (PP) was defined as five or more concomitant medications but less than ten medications, while excessive polypharmacy (EPP) was defined as the concurrent use of ten or more medications, including prescription and nonprescription. For compound preparations, each component was calculated separately.
PIM-Beers was categorized based on the 2023 American Geriatrics Society (AGS)/Beers criteria, which is the screening tool used in clinical practice in the United States. In addition to PIM-Beers, we checked PIM use related to COVID-19 antiviral NMV/r. PIM-NMV/r, which was defined as the use of one of the following cases: (1) with renal insufficiency did not adjust the dose according to creatinine clearance rate; (2) potential drug-drug interactions (DDIs) identified on the basis of COVID-19 Drug Interactions website (www.covid19-druginteractions.org), developed by the University of Liverpool. We counted “Potential Weak Interaction” “Potential Interaction” and “Do Not Coadminister” as PIM-NMV/r. We added PIM-beers and PIM-NMV/r to count as PIMs.
Statistical analysis
Descriptive statistics was used to summarize baseline characteristics and calculated by using Fisher’s exact test. Categorical data are presented according to frequency and calculated by using Pearson’sχ2 test. Continuous data that were normally distributed are expressed as the mean ± standard deviation (SD), and continuous data that were not normally distributed are expressed as the median (M) and the interquartile range (IQR). Wilcoxon’s test for continuous variables, and the Kruskal-Wallis test for assessments between groups. The associations between the risk factors and PIM use or in-hospital mortality were assessed with a multivariate logistic regression analysis including all the variables simultaneously which have statistical significance (p < 0.05) in univariate analysis, and only variables with p-value < 0.05 in the multivariate analysis were identified as statistically significant influencing factors. All analyses were performed using R Statistical Software (R version 4.0.3).
Ethics approval
This study protocol was approved by the Shanghai Gonghui Hospital Research Ethics Board. All procedures performed in this study conformed to the standards of the 1964 Helsinki Declaration and subsequent relevant ethics.
Results
Basic patient characteristics
A total of 617 patients with COVID-19 from 11 April to 30 June 2022 were included in this study, of whom 58.0% (n = 358) were female. In all, 79 patients were given NMV/r and 33 patients were severe COVID-19. The median age was 79 (IQR: 68, 89) years old, ranging from 60 to 104, with the oldest (≥ 80 years of age) COVID-19 patients accounting for 49.4%. The mean length of stay was 14.2 days and the mean number of comorbidities was 2.7. The mean number of medications used by patients was 5.8. The average total hospital costs and total medication costs were RMB 4422.0 and RMB 1314.1, respectively. In this study, 50.6%, 18.5%, and 14.9% of the patients with COVID-19 had cardiovascular disease, diabetes, and cerebral infarction, respectively. The basic patient characteristics are provided in Table 1.
Table 1.
Patient demographic and clinical characteristics.
| Characteristics | Total (n = 617) |
Non-NMV/r (n = 538) |
NMV/r (n = 79) |
p |
|---|---|---|---|---|
| Sex, n (%) | 0.488 | |||
| Female | 358 (58.0) | 315 (58.6) | 43 (54.4) | |
| Male | 259 (42.0) | 223 (41.4) | 36 (45.6) | |
| Age, IQR | 79 (68, 89) | 78 (68, 89) | 82 (73, 88) | 0.13 |
| Length of stay, Mean ± SD | 14.2 ± 5.7 | 13.8 ± 5.5 | 17.1 ± 6.1 | < 0.001 |
| Comorbidities | ||||
| Cardiovascular disease, n (%) | 312 (50.6) | 256 (47.6) | 56 (70.9) | < 0.001 |
| Diabetes, n (%) | 114 (18.5) | 85 (15.8) | 29 (36.7) | < 0.001 |
| Cerebral infarction, n (%) | 92 (14.9) | 68 (12.6) | 24 (30.4) | < 0.001 |
| Previous history of tumors, n (%) | 33 ( 5.3) | 23 (4.3) | 10 (12.7) | 0.005 |
| Chronic kidney disease, n (%) | 18 ( 2.9) | 17 (3.2) | 1 (1.3) | 0.716 |
| Chronic obstructive pulmonary disease, n (%) | 18 ( 2.9) | 11 (2) | 7 (8.9) | 0.004 |
| Current medical history of the tumors, n (%) | 5 ( 0.8) | 4 (0.7) | 1 (1.3) | 0.497 |
| Autoimmune Disease, n (%) | 4 ( 0.6) | 4 (0.7) | 0 (0) | 1 |
| Diagnosis, Mean ± SD | 2.7 ± 1.8 | 2.6 ± 1.8 | 3.2 ± 1.8 | 0.008 |
| Medications, Mean ± SD | 5.8 ± 6.5 | 4.7 ± 5.2 | 13.2 ± 9.1 | < 0.001 |
| Any PP, n (%) | 270 (43.8) | 203 (37.7) | 67 (84.8) | < 0.001 |
| Number of drugs, n (%) | < 0.001 | |||
| 0–4 | 347 (56.2) | 335 (62.3) | 12 (15.2) | |
| 5–9 | 152 (24.6) | 128 (23.8) | 24 (30.4) | |
| ≥10 | 118 (19.1) | 75 (13.9) | 43 (54.4) | |
| Severe COVID-19 | 33(5.3) | 18 (3.3) | 15 (19.0) | < 0.001 |
| Total hospital costs, Mean ± SD | 4422.0 ± 6189.6 | 3208.9 ± 3778.4 | 12683.1 ± 11180.4 | < 0.001 |
| Total medications costs, Mean ± SD | 1314.1 ± 3507.5 | 601.6 ± 2036.2 | 6166.6 ± 6423.8 | < 0.001 |
The prevalence of PP, EPP
Polypharmacy and excessive polypharmacy were observed in 24.6% and 19.1% of the patients, respectively. In Table 2, patient characteristics statistically significantly associated with PP were increased age, increased number of diagnoses, increased PIM use, increased PIM-Beers, increased PIM-NMV/r, increased length of stay, increased total hospital costs, increased total medications costs, more severe COVID-19 patients, and fewer survivors.
Table 2.
Patient characteristics Associated with PP (n = 617).
| Characteristic | No PP (<5 medications) (n = 347) |
Any PP (≥ 5 medications) (n = 270) |
p | PP (≥ 5 and <10 medications) (n = 152) |
EPP (≥ 10 Medications (n = 118) |
p |
|---|---|---|---|---|---|---|
| Sex, n (%) | 0.17 | 0.434 | ||||
| Female | 193 (55.6) | 165 (61.1) | 96 (63.2) | 69 (58.5) | ||
| Male | 154 (44.4) | 105 (38.9) | 56 (36.8) | 49 (41.5) | ||
| Age, Median (IQR) | 74.0 (66.0, 87.0) | 84.0 (73.2, 90.0) | < 0.001 | 79.0 (71.0, 89.0) | 86.5 (78.2, 91.0) | < 0.001 |
| Age, n (%) | < 0.001 | < 0.001 | ||||
| 60–69 | 126 (36.3) | 42 (15.6) | 34 (22.4) | 8 (6.8) | ||
| 70–79 | 78 (22.5) | 66 (24.4) | 43 (28.3) | 23 (19.5) | ||
| 80–89 | 90 (25.9) | 92 (34.1) | 47 (30.9) | 45 (38.1) | ||
| ≥ 90 | 53 (15.3) | 70 (25.9) | 28 (18.4) | 42 (35.6) | ||
| Diagnosis, mean ± SD | 2.0 ± 1.4 | 3.7 ± 1.8 | < 0.001 | 3.3 ± 1.7 | 4.2 ± 1.9 | < 0.001 |
| Diagnosis, n (%) | < 0.001 | < 0.001 | ||||
| 1–2 | 255 (73.5) | 75 (27.8) | 55 (36.2) | 20 (16.9) | ||
| 3–4 | 71 (20.5) | 116 (43.0) | 63 (41.4) | 53 (44.9) | ||
| ≥ 5 | 21 (6.1) | 79 (29.3) | 34 (22.4) | 45 (38.1) | ||
| NMV/r, n (%) | 12 (3.5) | 67 (24.8) | < 0.001 | 24 (15.8) | 43 (36.4) | < 0.001 |
| PIM, n (%) | 17 (4.9) | 142 (52.6) | < 0.001 | 53 (34.9) | 89 (75.4) | < 0.001 |
| PIM-Beers, n (%) | 15 (4.3) | 124(45.9) | < 0.001 | 45 (29.6) | 79 (66.9) | < 0.001 |
| PIM-NMV/r, n (%) | 2 (0.6) | 46 (17.0) | < 0.001 | 10 (6.6) | 36 (30.5) | < 0.001 |
| Length of stay, mean ± SD | 12.8 ± 4.6 | 16.0 ± 6.4 | < 0.001 | 14.7 ± 5.3 | 17.5 ± 7.2 | < 0.001 |
| Length of stay, n (%) | < 0.001 | 0.002 | ||||
| 1–14 | 233 (67.1) | 118 (43.7) | 79 (52.0) | 39 (33.1) | ||
| ≥ 15 | 114 (32.9) | 152 (56.3) | 73 (48.0) | 79 (66.9) | ||
| Total hospital costs, Mean ± SD | 2098.3 ± 1174.1 | 7408.3 ± 8369.1 | < 0.001 | 4016.6 ± 2108.1 | 11777.2 ± 11004.8 | < 0.001 |
| Total medications costs, Mean ± SD | 175.2 ± 444.4 | 2777.8 ± 4908.7 | < 0.001 | 906.1 ± 1001.0 | 5188.8 ± 6610.0 | < 0.001 |
| Severe COVID-19 | 1(0.3) | 32 (11.9) | < 0.001 | 2 (1.3) | 30 (25.4) | < 0.001 |
| Survivors, n (%) | 344 (99.1) | 249 (92.2) | < 0.001 | 143 (94.1) | 106 (89.8) | 0.196 |
PIM, PIM-Beers and PIM-NMV/r
Among the 617 patients with COVID-19 in Shanghai Gonghui Hospital, 159 (25.8%) patients were identified to have at least one PIM and a total of 394 PIMs were detected according to the 2023 Beers criteria and PIM-NMV/r. Of the patients with PIM prescriptions, 44.7% received one PIM, 21.4% received two PIMs, and 34.0% received at least three PIMs according to the criteria. The prevalence of PIM-Beers and PIM-NMV/r was 22.5% (139/617) and 60.8% (48/79), respectively, and a total of 28 patients had both PIM-Beers and PIM-NMV/r. The most prevalent PIM-Beers (n = 294) are listed in Table 3, including diuretics (32.0%), antipsychotics (18.4%), antidepressants (5.4%), and gastrointestinal antispasmodics with strong anticholinergic activity (5.4%).
Table 3.
Prevalence of PIM-Beers by Pharmacologic Category (n = 294).
| Organ System/ Therapeutic Class | Pharmacologic Category | International Nonproprietary Name | No. | % |
|---|---|---|---|---|
| Cardiovascular | 137 | 46.6% | ||
| Diuretics | Furosemide, Antisterone | 94 | 32.0% | |
| Calcium channel blockers | Diltiazem, Nifedipine | 13 | 4.4% | |
| Anticoagulants | Rivaroxaban, Dabigatran | 12 | 4.1% | |
| Antithrombotics | Aspirin | 9 | 3.1% | |
| Antiarrhythmics | Amiodarone | 6 | 2.0% | |
| Antihypertensive | Telmisartan, losartan | 2 | 0.7% | |
| Cardiac glycosides | Digoxin | 1 | 0.3% | |
| Central nervous system | 88 | 29.9% | ||
| Antipsychotics | Olanzapine, Quetiapine | 54 | 18.4% | |
| Antidepressants | Sertraline, Venlafaxine | 19 | 6.5% | |
| Benzodiazepines | Alprazolam, Estazolam | 15 | 5.1% | |
| Endocrine | 29 | 9.9% | ||
| Sulfonamides | Glimepiride, Gliclazide | 13 | 4.4% | |
| Short-insulin | Insulin | 8 | 2.7% | |
| Sodium-dependent glucose transporters 2 inhibitors | Empagliflozin, Dapagliflozin | 8 | 2.7% | |
| Gastrointestinal | 26 | 8.8% | ||
|
GI antispasmodics with strong anticholinergic activity |
Atropine, Anisodamine | 16 | 5.4% | |
| Antiemetic | Metoclopramide | 10 | 3.4% | |
| Analgesics | 12 | 4.1% | ||
| Non-COX−2-selective NSAIDs | Ibuprofen | 11 | 3.7% | |
| Opioids | Tramadol | 1 | 0.3% | |
| Antihistamines | 2 | 0.7% | ||
| First-generation antihistamines | Promethazine | 2 | 0.7% |
Abbreviations: NSAIDs, Non-steroidal anti-inflammatory drugs.
A total of 79 out of 617 patients were given NMV/r, but 48 patients received at least 1 PIM-NMV/r, 46 patients were observed drug-drug interactions of NMV/r and 4 patients with renal insufficiency did not have NMV/r dose adjusted. The prevalence of PIM-NMV/r was 60.8%. The total number of drug-drug interactions of NMV/r was 96, with the highest number of drug-drug interactions of NMV/r in one patient being 7. The most prevalent drug-drug interactions of NMV/r (n = 96) were listed in Table 4, including amlodipine (22.8%), quetiapine (12.7%), flupentixol and melitracen (10.1%), clopidogrel (8.9%), rosuvastatin (7.6%) and aminophylline (7.6%).
Table 4.
Identified contra-indicated drug-drug interactions between Nirmatrelvir/Ritonavir and other medications.
| Drug | Effect on concentration | Possible effect | Drug Interactions | No. of patients (%) |
|---|---|---|---|---|
| Amlodipine | ↑Amlodipine | Increased risk of arrhythmias or other serious adverse reactions | Potential Interaction | 18(22.8%) |
| Quetiapine | ↑Quetiapine | Increased risk of coma | Do Not Coadminister | 10(12.7%) |
| Flupentixol and Melitracen | ↑Flupentixol | Increased risk of coma | Potential Interaction | 8(10.1%) |
| Clopidogrel | ↓Clopidogrel | Increased risk of atherothrombotic events |
Recently stented patients: Do not coadminister Others: Potential Interaction |
7(8.9%) |
| Rosuvastatin | ↑Rosuvastatin | Increased risk of myopathy | Potential Interaction | 6(7.6%) |
| Aminophylline | ↓Aminophylline | Reduced efficacy | Potential Weak Interaction | 6(7.6%) |
| Isosorbide Mononitrate | ↓Nitric Oxide | Reduced efficacy | Potential Weak Interaction (NMV/r 5days) | 4(5.1%) |
| Nifedipine | ↑Nifedipine | Increased risk of hypotension, flushing, and oedema | Potential Interaction | 4(5.1%) |
| Venlafaxine | ↑Venlafaxine | Not mentioned | Potential Weak Interaction | 3(3.8%) |
| Erythromycin | ↑Erythromycin | Increased risk of QT prolongation | Potential Interaction | 3(3.8%) |
| Levothyroxine | ↓Levothyroxine | Not mentioned | Potential Interaction | 2(6.7%) |
| Loratadine | ↑Loratadine | Not mentioned | Potential Weak Interaction | 2(2.5%) |
| Digoxin | ↑Digoxin | Not mentioned | Potential Interaction | 2(2.5%) |
| Fentanyl | ↑Fentanyl | Increased risk of serious, life-threatening, or fatal respiratory depression | Potential Interaction | 2(2.5%) |
| Lidocaine | ↑Lidocaine | Not mentioned | Potential Interaction | 2(2.5%) |
Risk factors for PIM-Beers and PIM use
The binary logistic regression model showed that the PIM-Beers was associated with diabetes (OR: 2.18 [95% CI: 1.19-4.00], p = 0.011), more prescriptions of medications (OR: 1.27 [95% CI: 1.17–1.45], p = 0.001) and polypharmacy (OR: 2.57 [95% CI: 1.10–5.98], p = 0.029) after adjusting for sex, age, the number of diseases, cardiovascular disease, cerebral infarction, previous history of tumors, given NMV/r, length of stay, total hospital costs, total medications costs and severe COVID-19 in Table 5.
Table 5.
Univariate and multivariate logistic regression analysis of factors for PIM-Beers.
| Univariate analysis | Multivariate analysis | ||||||
|---|---|---|---|---|---|---|---|
| Characteristics | OR | 95% CI | P value | Characteristics | OR | 95% CI | P value |
| Sex | 0.64 | 0.43–0.95 | 0.027 | Sex | 0.58 | 0.34−1.00 | 0.050 |
| Age | Age | ||||||
| 60–69 | Reference | 60–69 | Reference | ||||
| 70–79 | 2.56 | 1.41–4.65 | 0.002 | 70–79 | 1.27 | 0.59–2.76 | 0.541 |
| 80–89 | 2.29 | 1.28–4.08 | 0.005 | 80–89 | 0.51 | 0.23–1.15 | 0.105 |
| ≥ 90 | 3.44 | 1.88–6.27 | < 0.001 | ≥ 90 | 0.84 | 0.36–1.95 | 0.686 |
| Number of diseases | Number of diseases | ||||||
| 1–2 | Reference | 1–2 | Reference | ||||
| 3–4 | 4.90 | 3.01-8.00 | < 0.001 | 3–4 | 1.19 | 0.54–2.63 | 0.665 |
| ≥ 5 | 10.38 | 6.01–17.93 | < 0.001 | ≥ 5 | 1.56 | 0.60–4.04 | 0.362 |
| Cardiovascular disease | 4.04 | 2.64–6.20 | < 0.001 | Cardiovascular disease | 1.18 | 0.56–2.45 | 0.665 |
| Diabetes | 3.82 | 2.47–5.90 | < 0.001 | Diabetes | 2.18 | 1.19-4.00 | 0.011 |
| Cerebral infarction | 2.95 | 1.85–4.72 | < 0.001 | Cerebral infarction | 1.05 | 0.52–2.15 | 0.884 |
| Previous history of tumors | 3.09 | 1.51–6.31 | 0.002 | Previous history of tumors | 1.54 | 0.58–4.06 | 0.382 |
| Chronic kidney disease | 1.75 | 0.65–4.76 | 0.271 | Chronic kidney disease | - | - | - |
| Chronic obstructive pulmonary disease | 1.33 | 0.47–3.81 | 0.59 | Chronic obstructive pulmonary disease | - | - | - |
| Current medical history of the tumors | 5.25 | 0.87–31.74 | 0.071 | Current medical history of the tumors | - | - | - |
| Autoimmune Disease | 3.47 | 0.48–24.89 | 0.215 | Autoimmune Disease | - | - | - |
| Medications | 1.33 | 1.26–1.40 | < 0.001 | Medications | 1.27 | 1.17–1.45 | 0.001 |
| NMV/r | 3.32 | 2.03–5.43 | < 0.001 | NMV/r | 0.41 | 0.16–1.03 | 0.058 |
| Number of drugs | Number of drugs | ||||||
| 0–4 | Reference | 0–4 | Reference | ||||
| 5–9 | 9.31 | 4.99–17.37 | < 0.001 | 5–9 | 2.57 | 1.10–5.98 | 0.029 |
| ≥ 10 | 44.83 | 23.55–85.37 | < 0.001 | ≥ 10 | 2.60 | 0.61–11.14 | 0.197 |
| Length of stay | Length of stay | ||||||
| 1–14 | Reference | 1–14 | Reference | ||||
| ≥ 15 | 2.69 | 1.82–3.96 | < 0.001 | ≥ 15 | 1.36 | 0.75–2.45 | 0.309 |
| Total hospital costs | 1 | 1–1 | < 0.001 | Total hospital costs | 1 | 1–1 | 0.893 |
| Total medications costs | 1 | 1–1 | < 0.001 | Total medications costs | 1 | 1–1 | 0.883 |
| Severe COVID-19 | 12.88 | 5.66–29.31 | < 0.001 | Severe COVID-19 | 0.88 | 0.25–3.18 | 0.850 |
Multivariate regression showed that the PIM use was associated with male (OR: 0.57 [95% CI: 0.33–0.98], p = 0.044), diabetes (OR: 2.05 [95% CI: 1.11–3.80], p = 0.023), the more number of medications (OR: 1.44 [95% CI: 1.20–1.72], p < 0.001) and given NMV/r (OR: 3.67 [95% CI: 1.48–9.10], p = 0.005) after adjusting for age, the number of diseases, cardiovascular disease, cerebral infarction, previous history of tumors, any polypharmacy, length of stay, total hospital costs, total medications costs and severe COVID-19 in Table 6.
Table 6.
Univariate and multivariate logistic regression analysis of factors for PIM.
| Univariate analysis | Multivariate analysis | ||||||
|---|---|---|---|---|---|---|---|
| Characteristics | OR | 95% CI | P value | Characteristics | OR | 95% CI | P value |
| Sex | 0.66 | 0.45–0.96 | 0.029 | Sex | 0.57 | 0.33–0.98 | 0.044 |
| Age | Age | ||||||
| 60–69 | Reference | 60–69 | Reference | ||||
| 70–79 | 2.92 | 1.65–5.17 | < 0.001 | 70–79 | 1.20 | 0.55–2.64 | 0.641 |
| 80–89 | 2.73 | 1.57–4.73 | < 0.001 | 80–89 | 0.63 | 0.28–1.40 | 0.258 |
| ≥ 90 | 3.20 | 1.78–5.75 | < 0.001 | ≥ 90 | 0.81 | 0.35–1.90 | 0.631 |
| Number of diseases | Number of diseases | ||||||
| 1–2 | Reference | 1–2 | Reference | ||||
| 3–4 | 5.70 | 3.60–9.03 | < 0.001 | 3–4 | 1.33 | 0.61–2.93 | 0.474 |
| ≥ 5 | 9.06 | 5.34–15.38 | < 0.001 | ≥ 5 | 1.32 | 0.51–3.43 | 0.573 |
| Cardiovascular disease | 4.86 | 3.21–7.37 | < 0.001 | Cardiovascular disease | 1.52 | 0.73–3.14 | 0.260 |
| Diabetes | 4.32 | 2.82–6.63 | < 0.001 | Diabetes | 2.05 | 1.11–3.80 | 0.023 |
| Cerebral infarction | 3.09 | 1.96–4.89 | < 0.001 | Cerebral infarction | 0.96 | 0.46–2.01 | 0.924 |
| Previous history of tumors | 2.90 | 1.43–5.89 | 0.003 | Previous history of tumors | 1.09 | 0.39–3.05 | 0.857 |
| Chronic kidney disease | 1.46 | 0.54–3.95 | 0.459 | Chronic kidney disease | - | - | - |
| Chronic obstructive pulmonary disease | 1.87 | 0.71–4.91 | 0.203 | Chronic obstructive pulmonary disease | - | - | - |
| Current medical history of the tumors | 4.38 | 0.73–26.48 | 0.107 | Current medical history of the tumors | - | - | - |
| Autoimmune Disease | 2.90 | 0.41–20.79 | 0.288 | Autoimmune Disease | - | - | - |
| Medications | 1.43 | 1.34–1.52 | < 0.001 | Medications | 1.44 | 1.20–1.72 | < 0.001 |
| NMV/r | 9.56 | 5.66–16.17 | < 0.001 | NMV/r | 3.67 | 1.48–9.10 | 0.005 |
| Number of drugs | Number of drugs | ||||||
| 0–4 | Reference | 0–4 | Reference | ||||
| 5–9 | 10.39 | 5.76–18.76 | < 0.001 | 5–9 | 1.55 | 0.61–3.92 | 0.352 |
| ≥ 10 | 59.57 | 31.32–113.3 | < 0.001 | ≥ 10 | 1.07 | 0.20–5.76 | 0.939 |
| Length of stay | Length of stay | ||||||
| 1–14 | Reference | 1–14 | Reference | ||||
| ≥ 15 | 2.40 | 1.66–3.48 | < 0.001 | ≥ 15 | 1.09 | 0.59-2.00 | 0.785 |
| Total hospital costs | 1 | 1–1 | < 0.001 | Total hospital costs | 1 | 1–1 | 0.530 |
| Total medications costs | 1 | 1–1 | < 0.001 | Total medications costs | 1 | 1–1 | 0.948 |
| Severe COVID-19 | 15.41 | 6.23–38.11 | < 0.001 | Severe COVID-19 | 0.87 | 0.2–3.81 | 0.851 |
Risk factors for in-hospital mortality
The in-hospital mortality of the polypharmacy group (7.8%) was higher than the non-polypharmacy group (0.9%). Multivariate logistic regression showed that the in-hospital mortality was significantly associated with severe COVID-19 (OR: 6.56 [95% CI: 1.13–38.03], p = 0.036), polypharmacy (OR: 15.43 [95% CI: 3.20-74.29], p = 0.001), excessive polypharmacy (OR: 51.09 [95% CI: 5.23-499.52], p = 0.001) and long-term hospitalization (OR: 0.08 [95% CI: 0.02–0.32], p < 0.001) after adjusting for age, current medical history of tumors, the number of diseases, medications, and PIM in Table 7.
Table 7.
Univariate and multivariate logistic regression analysis of factors for in-hospital mortality.
| Univariate analysis | Multivariate analysis | ||||||
|---|---|---|---|---|---|---|---|
| Characteristics | OR | 95% CI | P value | Characteristics | OR | 95% CI | P value |
| Sex | 1.18 | 0.52–2.67 | 0.696 | Sex | - | - | - |
| Age | Age | ||||||
| 60–69 | Reference | 60–69 | Reference | ||||
| 70–79 | 2.37 | 0.43–13.14 | 0.323 | 70–79 | 1.51 | 0.24–9.44 | 0.660 |
| 80–89 | 4.32 | 0.92–20.28 | 0.064 | 80–89 | 2.71 | 0.50-14.75 | 0.249 |
| ≥ 90 | 6.55 | 1.39–30.89 | 0.017 | ≥ 90 | 4.34 | 0.82–22.85 | 0.083 |
| Number of diseases | Number of diseases | ||||||
| 1–2 | Reference | 1–2 | Reference | ||||
| 3–4 | 1.18 | 0.41–3.38 | 0.754 | 3–4 | 0.53 | 0.16–1.84 | 0.320 |
| ≥ 5 | 3.53 | 1.36–9.15 | 0.010 | ≥ 5 | 0.74 | 0.20–2.76 | 0.655 |
| Cardiovascular disease | 0.57 | 0.25–1.33 | 0.197 | Cardiovascular disease | - | - | - |
| Diabetes | 1.87 | 0.76–4.62 | 0.175 | Diabetes | - | - | - |
| Cerebral infarction | 1.53 | 0.56–4.21 | 0.409 | Cerebral infarction | - | - | - |
| Previous history of tumors | 0.76 | 0.10–5.82 | 0.794 | Previous history of tumors | - | - | - |
| Chronic kidney disease | 1.47 | 0.19–11.55 | 0.712 | Chronic kidney disease | - | - | - |
| Chronic obstructive pulmonary disease | 0 | 0-Inf | 0.988 | Chronic obstructive pulmonary disease | - | - | - |
| Current medical history of tumors | 17.88 | 2.84-112.48 | 0.002 | Current medical history of tumors | 8.36 | 0.81–86.08 | 0.074 |
| Autoimmune Disease | 0 | 0-Inf | 0.991 | Autoimmune Disease | - | - | - |
| Medications | 1.07 | 1.02–1.11 | 0.004 | Medications | 0.89 | 0.76–1.05 | 0.176 |
| NMV/r | 0 | 0-Inf | 0.989 | NMV/r | - | - | - |
| Number of drugs | Number of drugs | ||||||
| 0–4 | Reference | 0–4 | Reference | ||||
| 5–9 | 7.22 | 1.93–27.05 | 0.003 | 5–9 | 15.43 | 3.20-74.29 | 0.001 |
| ≥ 10 | 12.98 | 3.60-46.86 | < 0.001 | ≥ 10 | 51.09 | 5.23-499.52 | 0.001 |
| Length of stay | Length of stay | ||||||
| 1–14 | Reference | 1–14 | Reference | ||||
| ≥ 15 | 0.18 | 0.05–0.61 | 0.006 | ≥ 15 | 0.08 | 0.02–0.32 | < 0.001 |
| Total hospital costs | 1 | 1–1 | 0.404 | Total hospital costs | - | - | - |
| Total medications costs | 1 | 1–1 | 0.832 | Total medications costs | - | - | - |
| Severe COVID-19 | 6.99 | 2.57–19.02 | < 0.001 | Severe COVID-19 | 6.56 | 1.13–38.03 | 0.036 |
| PIM | 2.54 | 1.12–5.80 | 0.026 | PIM | 0.99 | 0.31–3.15 | 0.991 |
Discussion
To our best knowledge, this was the largest and comprehensive study using both 2023 AGS Beers Criteria and Covid-19 antiviral NMV/r instructions to evaluate the prevalence of PIM use among older adults with COVID-19, in an attempt to assess the impact on in-hospital mortality in older COVID-19 inpatients. Similar to other studies, about half of the patients had polypharmacy (≥ 5 medications), and nearly 20% of them had excessive polypharmacy (≥ 10 medications).
With the ravages of COVID-19, the average number of medications prescribed to older patients had increased significantly, from 9.4 in 2019 to 10.4 in 202119. While this increase was not just attributable to the COVID-19 epidemic, it was more likely that the growing population of elderly people over time has also raised the incidence of chronic disease. Thus it has led to an increase in the variety of drugs. However, our studies indicated that the average number of medications prescribed to older patients and the median number of diseases were 5.8 and 2.7, respectively, which was lower than Chen’s study with an average number of medications of 12 and the median number of diseases of 1218, possibly because our study was conducted in a secondary hospital and included patients with relatively mild conditions. A meta-analysis which included 14 studies with a sample size of 189,870 patients with COVID-19 showed that the prevalence of polypharmacy was 34.6%20, which was lower than our study of 43.8%. This may be related to the fact that older patients have a higher prevalence of chronic diseases, and tend to be more willing to accept medication. Based on the 2023 AGS Beers Criteria and NMV/r instructions, our study found that the prevalence of PIM use among older COVID-19 inpatients was 25.8%, which was lower than Malaysian with a prevalence of 32.7%17.The prevalence of PIM use was relatively low due to the fact that the patients included in the study were COVID-19 patients with less number of medications and disease in a secondary hospital.
Polypharmacy may increase hospitalization and mortality rates, especially in older patients, which is a marker that should not be ignored during the COVID-19 epidemic21. Our study showed that non-survivors of COVID-19 had more identified polypharmacy and excessive polypharmacy, indicating that strengthening the management of polypharmacy is indispensable. Likewise, we found that a higher percentage of patients with an existing tumor history among non-survivors than that in survivors, suggesting that oncology patients should be provided more protection during the COVID-19 epidemic.
According to our analysis, the most frequent PIM use was diuretics (32.0%), which was similar to other studies18. The use of diuretics in elderly patients may lead to deterioration or cause syndrome of inappropriate antidiuretic hormone, and electrolyte abnormalities. Some studies have shown diuretics were a risk factor for unplanned hospital admissions in elderly patients22. Univariate and multifactorial analyses showed that diuretics were associated with an increased incidence of severe COVID-1923. Elderly patients with COVID-19 may have edema due to hypoproteinemia or cachexia and require diuretics including hydrochlorothiazide, furosemide, or spironolactone. Therefore, electrolytes such as sodium and potassium should be monitored closely when starting diuretics or adjusting the dose to avoid adverse effects24.
Antipsychotics, such as olanzapine, and quetiapine, were the second commonly used class in hospitalized patients with COVID-19, which belong to the third part of the 2023 AGS Beers Criteria. With age, older adults may develop a manic disorder or delirium. Consequently, antipsychotics are commonly used by older patients. However, they are also associated with increased risks of stroke and a greater rate of cognitive decline and mortality. A retrospective study revealed that mental health drugs (primarily antidepressants) were associated with a higher risk of all-cause mortality than other medications in elderly patients with polypharmacy25. A population-based, retrospective cohort study showed that atypical antipsychotic was associated with an increased risk of mortality among older adults with dementia26. The findings were consistent during the COVID-19 epidemic27. Therefore, clinicians should minimize the use of antipsychotics.
In a phase II/III double-blind, randomized, clinical trial, Paxlovid has reduced the incidence of COVID-19-related hospitalization or death by day 28, the incidence of death in symptomatic, unvaccinated, non-hospitalized adults at high risk for progression to COVID-19 as compared with placebo. The incidence of adverse events was similar in the two groups during the treatment28. An observational study that included a total of 1,074,856 non-hospitalized patients with COVID-19, of whom 5,383 received molnupiravir and 6,464 received NMV/r revealed that early oral antiviral medication in elderly patients was associated with a reduced risk of mortality and hospitalisation29.
NMV/r is composed of 300 mg of nirmatrelvir and 100 mg of ritonavir. Nirmatrelvir is a SARS-CoV-2 main protease inhibitor, which has extensive coronavirus antiviral activity. The high oral bioavailability makes it suitable for oral administration30. Ritonavir is an inhibitor of cytochrome P450 (CYP) 3A4 isoenzymes and its maximum inhibitory effect is at a dose of 100 mg. It can inhibit nirmatrelvir, which is also a CYP 3A4 inhibitor substrate, by slowing down nirmatrelvir’s metabolism and increasing nirmatrelvir’s concentration, to exert the antiviral effect31. However, many drugs have drug-drug interactions with NMV/r due to the metabolism pathway through CYP 3A4. In addition, any drug which influences the activity of the CYP 3 A enzyme will change the metabolism of NMV/r, thus affecting the efficacy and safety. Our study found that the prevalence of drug-drug interactions with NMV/r among older COVID-19 inpatients was 58.2% (46/79), which was higher than Hangzhou in China with a prevalence of 14.6%32 and lower than COVID-19 inpatients in Canada with a prevalence of 67.9%33, respectively. Our results revealed the most frequent drug-drug interactions with NMV/r were amlodipine, the second-generation calcium channel antagonists, which belong to the COVID-19 Drug Interactions website. NMV/r is predicted to increase amlodipine exposure by 2-fold based on drug-drug interaction studies with amlodipine and paritaprevir/ritonavir leading to the recommendation to reduce amlodipine dosage by 50% or to take the dose every other day34. The second common co-medication contra-indicated during NMV/r treatment was quetiapine, which is mainly used for the treatment of type I or type II bipolar major depression. Quetiapine is metabolized mainly to inactive sulfoxide metabolites by CYP 3A4 in the liver, so the elevation of quetiapine concentrations may result in an increased risk of coma. Other frequently prescribed “Do Not Coadminister” medications in the study included estazolam, propafenone, amiodarone, clopidogrel, and rivaroxaban. Because of drug-drug interactions, many older patients with polypharmacy cannot safely receive NMV/r. Not all drug-drug interactions with NMV/r can be managed with dosage reduction or discontinuation of other medications, due to an abrupt discontinuation of other medications, resulting in adverse drug withdrawal events. Therefore, drug-drug interactions with NMV/r need to be evaluated by a physician or pharmacist before prescription to avoid adverse events.
A factor analysis of older COVID-19 inpatients revealed that severe COVID-19, polypharmacy, excessive polypharmacy, and long-term hospitalization were all strongly correlated with in-hospital mortality. We found a strong positive correlation between severe COVID-19, polypharmacy, and in-hospital mortality, which is consistent with other studies35. Polypharmacy increases the risk of drug-drug interactions, especially with NMV/r, and may even counteract the benefits of NMV/r for COVID-19. The findings further emphasize the importance of our study.
However, there are some limitations in our study. Firstly, the study was a retrospective study with no prior standardized documentation of medication history. Furthermore, we did not assess the adverse drug events in patients presenting with PIM use during the patient’s hospitalization and did not investigate the long-term effect of PIM use on adverse drug events and clinical outcomes. Finally, the interaction of herbal medicines taken by patients with NMV/r was not studied as they were taken by patients outside of the hospital and we did not have access to learn about the specific herbal species. Further studies with a larger population and long-term follow-up were warranted to evaluate PIM use with adverse drug reactions and prognosis.
Conclusions
This study provides important information on prescribing practices with COVID-19 patients in a secondary hospital in China. These results demonstrated that there was a higher level of prevalence of polypharmacy, excessive polypharmacy, PIM-NMV/r, and a relatively lower prevalence of PIM-Beers in older hospitalized patients with COVID-19. Older patients with severe COVID-19, a higher number of medications, and long-term hospitalization had a higher in-hospital mortality. Pharmacists should be more vigilant in reviewing inappropriate medications during the COVID pandemic.
Author contributions
Conception and design: MM. Administrative support: MM and SD. Provision of study materials or patients: XH, YX and QL. Collection and assembly of data: XH, YX RL and QL. Data analysis and interpretation: MM, XH and QZ. Manuscript writing: all authors. Final approval of manuscript: all authors. All authors declare that they have no conflicts of interest in this work.
Data availability
The data that support the findings of this study are available from Shanghai Gonghui Hospital, but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. The data are, however, available from the authors (Mian Mao or Xiaoli Hu) upon reasonable request and with the permission of Shanghai Gonghui Hospital.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Mian Mao and Shan Du have contributed equally to this work.
QiaoqiaoZhou and Xiaoli Hu have contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are available from Shanghai Gonghui Hospital, but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. The data are, however, available from the authors (Mian Mao or Xiaoli Hu) upon reasonable request and with the permission of Shanghai Gonghui Hospital.