Development and validation of a nomogram to predict the risk of potentially inappropriate medication use in older depression outpatients
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Abstract Background: Potentially inappropriate medication (PIM) for the elderly is a serious public health problem associated with increased adverse drug events. PIM refers to medications where the adverse risks outweigh the potential benefits. Identifying the risk factors for PIM is essential for optimizing prescription practices and improving patient safety. Objective: To establish a risk prediction model for potentially inappropriate medications (PIMs) in older patients with depression, providing guidance to optimize medication plans, reduce adverse drug reactions, and improve treatment outcomes and quality of life. Methods: The prescription of depression patients in all hospitals in the Chengdu area was taken as an example. A significant factor influencing PIM risk was identified through univariate and multivariate logistic regression analyses, and a nomogram was constructed. The discrimination and calibration of the model were evaluated via receiver operating characteristic (ROC) curves. Results: Data from the Chengdu area (n=4629) were divided into a training set (n=3548) and an internal validation set (n=1081), with Zhengzhou data (n=1620) used as the external validation set. ROC curve analysis revealed that the area under the curve (AUC) for the training set was 0.721, that for the internal validation set was 0.668, and that for the external validation set was 0.663. Conclusion: The prediction model based on these factors has good predictive value for PIM occurrence in older patients with depression.
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Development and validation of a nomogram to predict the risk of potentially inappropriate medication use in older depression outpatients | 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 Development and validation of a nomogram to predict the risk of potentially inappropriate medication use in older depression outpatients Baihui Wu, Zhaoyan Chen, Fangyuan Tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7351975/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 18 You are reading this latest preprint version Abstract Background : Potentially inappropriate medication (PIM) for the elderly is a serious public health problem associated with increased adverse drug events. PIM refers to medications where the adverse risks outweigh the potential benefits. Identifying the risk factors for PIM is essential for optimizing prescription practices and improving patient safety. Objective: To establish a risk prediction model for potentially inappropriate medications (PIMs) in older patients with depression, providing guidance to optimize medication plans, reduce adverse drug reactions, and improve treatment outcomes and quality of life. Methods: The prescription of depression patients in all hospitals in the Chengdu area was taken as an example. A significant factor influencing PIM risk was identified through univariate and multivariate logistic regression analyses, and a nomogram was constructed. The discrimination and calibration of the model were evaluated via receiver operating characteristic (ROC) curves. Results: Data from the Chengdu area (n=4629) were divided into a training set (n=3548) and an internal validation set (n=1081), with Zhengzhou data (n=1620) used as the external validation set. ROC curve analysis revealed that the area under the curve (AUC) for the training set was 0.721, that for the internal validation set was 0.668, and that for the external validation set was 0.663. Conclusion: The prediction model based on these factors has good predictive value for PIM occurrence in older patients with depression. Older depression potentially inappropriate medication nomogram Figures Figure 1 Figure 2 Figure 3 1. Introduction According to data provided by the World Health Organization (WHO), the global prevalence of late-life depression ranges from 10–20%, and in certain regions or specific populations, this proportion may be even greater [ 1 ] . In recent years, with the intensification of global population aging, the number of people suffering from late-life depression has been increasing annually. In China, late-life depression is particularly severe, with a depression incidence rate of approximately 10.5% [ 2 ] . Moreover, it is common for older individuals to suffer from multiple diseases and use multiple medications. The interactions between various drugs and the long-term use of medications due to chronic comorbidities make drug interactions complex [ 3 – 5 ] , which can affect drug efficacy and increase the risk of adverse reactions. This may lead to the occurrence of potentially inappropriate medication (PIM) use. PIM refers to the situation where the potential adverse risks of using such medications may outweigh the expected clinical benefits [ 6 ] . Although late-life depression is a common mental disorder, there is currently a lack of effective clinical treatment methods, and pharmacotherapy remains the primary intervention. However, many older patients with depression have limited long-term therapeutic effects from drug treatment, poor tolerance to pharmacotherapy, and are prone to adverse reactions. Many of these drugs are PIMs, making older patients with depression a key focus group for PIM use. In evidence-based medicine and clinical practice, PIM is commonly used to evaluate the quality of drug therapy, including issues such as drug overdose, drug‒drug interactions, repeated dosing, and improper drug dosage [ 7 ] . One of the important factors that leads to a decrease in the safety of drug therapy is that the drugs prescribed are PIMs. This not only wastes medical resources but also may increase the risk of medication use for patients, reduce their quality of life, and lead to adverse outcomes such as fractures, falls, rehospitalization, and death [ 8 ] . Therefore, it is essential to pay close attention to the PIM issue in older patients with depression to reduce the medication risk in this population. Therefore, this study selects the prescriptions of older outpatients with depression as the research object and analyses the PIM situation and influencing factors of older outpatients in China. These findings provide guidance for future efforts to reduce medication risks and improve the medication environment for older patients with depression. The development of the Chinese PIM standard not only integrates multicenter monitoring data and clinical medication data from China but also involves multiple rounds of expert deliberation and validation in drug classification and risk assessment. It fully considers the disease spectrum characteristics and medication behavior features of older individuals in China, especially the PIM criteria under disease conditions, to ensure the scientific and clinical applicability of the standard [ 9 ] . Directly applying foreign PIM standards for assessment may lead to inaccurate results. Therefore, this study uses the Chinese PIM standard for data screening. In recent years, with the accumulation of medical big data and the advancement of artificial intelligence, machine learning has achieved significant success in the medical field, especially in disease prediction, diagnosis, and personalized treatment [ 10 ] , demonstrating great potential. It has been widely applied in various medical scenarios, such as intelligent diagnosis, medication consultation, and personalized medication, effectively improving the accuracy of clinical decision-making and the efficiency of diagnosis and treatment and promoting the transformation of medical models toward intelligent development. Since the national data are from a multicenter sample, there may be too many influencing factors due to differences in local medical levels and lifestyle habits such as diet, which may affect the model fit. Considering the availability of data, model applicability, and flexibility of the research design, data from the Chengdu area were selected for model construction. The first quarter includes the Spring Festival holiday, which may lead to abnormal medical visit patterns and medication behaviors. Data from the 2nd to 4th quarters cover spring, summer, and autumn and can more comprehensively reflect the disease spectrum and medication characteristics under different climatic conditions. This makes it easier to identify the influencing factors of PIM use in elderly patients with depression. Therefore, a PIM model for older patients with depression was constructed via logistic regression on the basis of data from the 2nd to 4th quarters of the Chengdu area, with the first quarter data of Chengdu used for internal validation. Additionally, a large discrepancy in the number of prescriptions between the internal and external validation sets may lead to model performance distortion due to data distribution bias. To ensure the scientific rigor of the model, an external validation was conducted using data from Zhengzhou with a similar number of prescriptions. This selection can better control variables, improve the accuracy and reliability of the model, achieve better model fit, and make full use of existing resources and data advantages to explore the model's goodness of fit and calibration degree, providing references for further model optimization. 2. Materials and methods 2.1 Data sources The data for this study were derived from the “Hospital Prescription Analysis Collaboration Project” of the Chinese Pharmaceutical Association. Initially, a cluster sampling method was employed to select the prescription medication data of older patients with depression from January to December 2021 across six major regions in China (North China, Central South China, Northeast China, Central China, East China, and Southwest China). The study included seven cities: Chengdu, Beijing, Guangzhou, Shanghai, Tianjin, Zhengzhou, and Hangzhou. The data extracted included patient personal information (age and sex), medication information (drug name and cost), and diagnostic information. Specifically, this study focused on the prescription data of older patients with depression from the Chengdu and Zhengzhou regions from January to December 2021. 2.2 Inclusion and Exclusion Criteria The inclusion criteria were as follows: (1) aged ≥ 65 years. (2) Outpatient data. (3) Clear diagnosis of depression, which is based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) and the International Classification of Diseases (10th Edition) (ICD-10). The exclusion criteria were as follows: (1) unclear diagnosis or incomplete information records. (2) No pharmacotherapy was used. 2.3 Data Cleaning Data cleaning involved the removal of records where the patient's age was less than 65 years, incomplete or missing prescription information, or where the prescribed medication was a topical preparation or solvent. Additionally, prescription records from the same case number and date were merged and counted as a single case. According to the inclusion and exclusion criteria, a total of 3 prescriptions were removed by data cleaning. 2.4 Prescription identification and statistics After data cleaning, prescriptions were identified, and their quantities were counted via “prescription codes”. Each prescription code corresponds to a single prescription record and is unique, covering all medication records from a patient's single visit. When determining and calculating the frequency of PIM use, the following principles were applied: (1) If a prescribed medication met any single criterion of the PIM evaluation standard, it was counted as one PIM occurrence. (2) If a prescribed medication met multiple criteria of the PIM evaluation standard, the occurrences were cumulatively tallied. 2.5 Evaluation criteria A national multicenter cross-sectional survey was conducted to use the Chinese PIM standard to identify and analyze PIM usage among older patients with depression in China. The Chinese PIM standard consists of two parts. The first part includes 13 major categories of 72 drugs/classes of drugs, such as drugs for the nervous system, cardiovascular system, and antiallergic system. Each drug/class of drugs is accompanied by 1 to 6 risk points for drug use. These risk points focus on severe, frequent, and common risks. Some drugs also include additional points of concern and medication suggestions, which help clinicians and pharmacists accurately and quickly identify the risks of each drug, making them more practical and referable. On the basis of the average score of expert-evaluated indicators, drugs are classified by risk intensity: (1) High-risk medications should be avoided by older individuals. (2) Low-risk medications should be used with caution by older patients. Part 2 includes 44 types/classes of drugs across 27 disease states, requiring joint evaluation of both the disease and the drug to determine whether the medication is a PIM. On the basis of medication frequency (total annual consumption of a drug divided by its defined daily dose), the 44 types/classes of drugs are categorized into Class A and Class B alert medications: (1) Class A warning drugs with a usage frequency ≥ 3000, such as the use of nonsteroidal anti-inflammatory drugs in heart failure. (2) Class B warning drugs with a usage frequency < 3000, such as the use of triazolam for insomnia. Organizing medications on the basis of disease state facilitates the search for whether a PIM is being used, making this standard more clinically instructive. 2.6 Statistical analysis In this study, the construction of a PIM risk prediction model for older patients with depression is a typical binary classification task. Therefore, this study employed logistic regression to construct a risk prediction model. Logistic regression is suitable for situations where the dependent variable is categorical, especially when there is no linear relationship between the independent and dependent variables, allowing for more effective model building and analysis. During the logistic regression analysis, all included variables were normalized and encoded, a process that runs through key stages such as data preprocessing, model training, and model saving. In this study, all variables were transformed into categorical variables for analysis. For continuous variables, such as age, a discretization preprocessing method was used. On the basis of previous literature or medical reference value ranges, these variables were converted into categorical variables and encoded according to different age groups. For unordered categorical variables, such as the number of diseases, number of medications, and reimbursement type, a binary or multicategory approach was used. The precision of the model is closely related to the factors included. Different factors have varying degrees of impact on the outcomes. Including all factors indiscriminately may reduce the computational efficiency of the model and even lead to overfitting. Therefore, it is essential to select factors that significantly influence the model. In this study, binary logistic regression was used for univariate analysis to screen for factors with P < 0.05. These factors were then included in the multivariate logistic regression analysis. The results are presented as odds ratios (ORs) and 95% confidence intervals (CIs). P < 0.05 was considered statistically significant. Variables with statistical significance ( P < 0.05) in the multivariate logistic regression analysis were used to construct a nomogram via R Studio version 4.2.0. A nomogram is a graphical tool for visualizing prediction models. It converts the values of each predictive variable into scores, sums these scores to obtain a total score, and then locates the corresponding PIM prediction probability on the nomogram on the basis of the total score. The nomogram can intuitively display the results of logistic regression [ 11 ] . The model was constructed on the basis of the selected factors. The prescriptions of older patients with depression in Chengdu in 2021 were divided by quarter. The data from the last three quarters were used as the training set, whereas the data from the first quarter were used as the internal validation set. The prescription data of older patients with depression in Zhengzhou, China, in 2021 were used as the external validation set. The goodness of fit of the model constructed from the training set was verified via both internal and external validation sets. When evaluating the goodness of fit of a constructed model, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are commonly used. The closer the AUC value is to 1, the stronger the model's discriminatory ability. An AUC > 0.7 is generally considered to indicate good discriminatory power [ 12 ] . The Hosmer–Lemeshow test is used to assess the calibration ability of the model, that is, the consistency between the predicted probabilities of the model and the actual observed results. P < 0.05 in the test is generally considered to indicate good calibration of the model [ 13 ] . 3. Results 3.1 Descriptive Statistics This study included a total of 6,249 prescriptions for older patients with depression. In the training set (n = 3,548), there were 1,547 prescriptions (45.5%) for older patients with depression involving PIM. In the internal validation set (n = 1,081), 497 prescriptions (46.0%) involving PIMs were included. In the external validation set (n = 1,620), there were 1,015 prescriptions (62.7%) involving PIMs. The patient selection process is depicted in Fig. 1 . Prescriptions for older patients with depression aged ≥ 80 years accounted for 24.2% (n = 1,512) of the sample. Among the prescriptions for older patients with depression, 78.16% (n = 4,884) had multiple chronic conditions, and 16.96% (n = 1,060) included ≥ 5 medications. Additionally, anxiety symptoms were present in 57.67% (n = 3,604) of the prescriptions, and insomnia symptoms were present in 28.9% (n = 1,806) of the prescriptions. The basic characteristics of the patients are shown in Table 1 . Table 1 Basic characteristics of the training, internal validation, and external validation methods Characteristics Training (%) Internal Validation (%) External Validation (%) PIM Yes 1615(45.5%) 497(46.0%) 1015(62.7%) No 1933(54.5%) 584(54.0%) 605(37.3%) Hospital level 2nd 68(1.9%) 31(2.9%) 73(4.5%) 3nd 3480(98.1%) 1050(97.1%) 1547(95.5%) Department Other 2657(74.9%) 808(74.7%) 1241(76.6%) Psychiatry 743(20.9%) 233(21.6%) 228(14.1%) Geriatrics 148(4.2%) 40(3.7%) 151(9.3%) Sex Female 2379(67.1%) 705(65.2%) 991(61.2%) Male 1169(32.9%) 376(34.8%) 629(38.8%) Age group 65–79 2890(81.5%) 853(78.9%) 994(61.4%) ≥ 80 658(18.5%) 228(21.1%) 626(38.6%) No. of diseases 1 783(22.1%) 234(21.6%) 348(21.5%) 2–4 1758(49.5%) 571(52.8%) 833(51.4%) ≥ 5 1007(28.4%) 276(25.5%) 439(27.1%) No. of medications 1–4 2969(83.7%) 924(85.5%) 1296(80.0%) ≥ 5 579(16.3%) 157(14.5%) 324(20.0%) Payment Free 1218(34.3%) 417(38.6%) 169(10.4%) Partial Fee 1368(38.6%) 352(32.6%) 447(27.6%) Full fee 962(27.1%) 312(28.9%) 1004(62.0%) Disease Anxiety Yes 1971(55.6%) 588(54.4%) 1045(64.5%) No 1577(44.4%) 493(45.6%) 575(35.5%) Sleep disorder Yes 1012(28.5%) 315(29.1%) 479(29.6%) No 2536(71.5%) 766(70.9%) 1141(70.4%) Dementia Yes 78(2.2%) 15(1.4%) 85(5.2%) No 3470(97.8%) 1066(98.6%) 1535(94.8%) Hypertension Yes 836(23.6%) 251(23.2%) 383(23.6%) No 2712(76.4%) 830(76.8%) 1237(76.4%) Coronary heart disease Yes 324(9.1%) 101(9.3%) 325(20.1%) No 3224(90.9%) 980(90.7%) 1295(79.9%) Diabetes Yes 389(11.0%) 104(9.6%) 202(12.5%) No 3159(89.0%) 977(90.4%) 1418(87.5%) Arthritis Yes 54(1.5%) 13(1.2%) 35(2.2%) No 3494(98.5%) 1068(98.8%) 1585(97.8%) Hyperlipidemia Yes 188(5.3%) 57(5.3%) 96(5.9%) No 3360(94.7%) 1024(94.7%) 1524(94.1%) Osteoporosis Yes 119(3.4%) 20(1.9%) 38(2.3%) No 3429(96.6%) 1061(98.1%) 1582(97.7%) Cerebrovascular disease Yes 542(15.3%) 154(14.2%) 136(8.4%) No 3006(84.7%) 927(85.8%) 1484(91.6%) 3.2 Influencing Factors The results of the univariate analysis for older patients with depression revealed that the number of comorbid diseases, type of reimbursement, department name, hospital level, anxiety disorder, sleep disorder, diabetes, hyperlipidemia, osteoporosis, and cerebrovascular disease were significantly associated with the occurrence of PIMs in older patients with depression ( P < 0.05). The 10 variables with P < 0.05 from the univariate analysis were subsequently included in the multivariate logistic regression analysis. The results indicated that the number of comorbid diseases, type of reimbursement, department name, hospital level, anxiety disorder, sleep disorder, diabetes, hyperlipidemia, and cerebrovascular disease were significant factors influencing the occurrence of PIM use in older patients with depression ( P < 0.05). The detailed results are shown in Table 2 . Table 2 Results of univariate and multivariate logistic regression analyses Univariate Analysis P Multivariate Analysis P Characteristics OR (95% CI) Characteristics OR (95% CI) Hospital level Hospital level 2nd Reference 2nd Reference 3nd 0.251(0.138–0.431) <0.001 3nd 0.193(0.104–0.341) <0.001 Department Department Other Reference Other Reference Psychiatry 2.563(2.167–3.038) <0.001 Psychiatry 2.812(2.325–3.406) <0.001 Geriatrics 0.357(0.233–0.530) <0.001 Geriatrics 0.483(0.310–0.732) <0.001 Sex Sex - Female Reference Female Male 0.949(0.825–1.093) 0.468 Male Age group Age group 65–79 Reference 65–79 Reference ≥ 80 1.201(1.014–1.423) 0.038 ≥ 80 1.414(1.168–1.714) <0.001 No. of diseases No. of diseases 1 Reference 1 Reference 2–4 1.316(1.110–1.560) 0.002 2–4 1.444(1.165–1.792) <0.001 ≥ 5 1.020(0.844–1.233) 0.837 ≥ 5 1.273(0.979–1.658) 0.072 No.of medications No. of medications 1–4 Reference 1–4 Reference ≥ 5 0.835(0.697–0.999) 0.049 ≥ 5 0.814(0.650–1.017) 0.071 Payment Payment Free Reference Free Reference Partial Fee 0.306(0.260–0.360) <0.001 Partial Fee 0.386(0.322–0.461) <0.001 Full fee 0.843(0.711–0.999) 0.049 Full fee 0.874(0.710–1.075) 0.201 Disease Disease Anxiety Anxiety No Reference No Reference Yes 0.839(0.734–0.958) 0.01 Yes 0.784(0.674–0.913) 0.002 Sleep disorder Sleep disorder No Reference No Reference Yes 2.421(2.086–2.813) <0.001 Yes 2.422(2.032–2.890) <0.001 Dementia Dementia - No Reference No Yes 0.974(0.617–1.526) 0.908 Yes Hypertension Hypertension No Reference No Reference Yes 0.813(0.695–0.951) 0.01 Yes 0.816(0.669–0.994) 0.044 Coronary heart disease Coronary heart disease - No Reference No Yes 0.796(0.630–1.003) 0.054 Yes Diabetes Diabetes No Reference No Reference Yes 0.497(0.395–0.621) <0.001 Yes 0.495(0.379–0.645) <0.001 Arthritis Arthritis - No Reference Yes Yes 0.700(0.395–1.210) 0.209 No Hyperlipidemia Hyperlipidemia - No Reference No Yes 0.766(0.566–1.032) 0.082 Yes Osteoporosis Osteoporosis No Reference No Reference Yes 0.488(0.323–0.721) <0.001 Yes 0.708(0.451–1.091) 0.124 Cerebrovascular disease Cerebrovascular disease No Reference No Reference Yes 1.205(1.003–1.447) 0.046 Yes 1.267(1.024–1.567) 0.029 3.3 A nomogram for predicting PIM Using multivariate logistic regression, the following variables were selected to construct the prediction model: department name, reimbursement type, hospital level, age, number of diseases, sleep disorders, hypertension, and cerebrovascular disease. A nomogram was then developed on the basis of these variables, and the results are shown in Fig. 2 . The AUC value of the training set for the PIM model in older patients with depression was 0.72, indicating good discriminatory ability. The Hosmer–Lemeshow test value was 0.29, suggesting that the model had good calibration. The model was applied to the internal validation set for testing. The internal validation results revealed that the AUC value of the logistic regression model was 0.668, indicating acceptable discriminatory ability. The Hosmer–Lemeshow test value was less than 0.05, suggesting that the model calibration was not ideal. The model was applied to the external validation set to evaluate its generalizability and extrapolation capability. The external validation results revealed that the AUC value of the logistic regression model was 0.663, indicating moderate discriminatory ability. The Hosmer–Lemeshow test value was less than 0.05, suggesting that the model calibration was not ideal. The ROC curve is shown in Fig. 2 , and the calibration curve is shown in Fig. 2 . For PIM in older patients with depression, DCA revealed that the nomogram has a greater net benefit than strategies based on considering either all patients or no patients for intervention at risk thresholds up to approximately 70% (Fig. 3 ). 4. Discussion To the best of our knowledge, this study is the first attempt to assess the influencing factors of PIM in Chinese elderly outpatients. On the basis of the Chinese PIM standard, we calculated the detection proportion of PIMs in outpatient prescriptions for older patients with depression and identified influencing factors to construct a PIM risk prediction model for older patients with depression in Chengdu. The predictor analysis results revealed that age, number of diseases, and polypharmacy are closely related to the occurrence of PIM use in older patients. According to a study by Tian et al. [ 14 ] , PIM use is associated with sex, polypharmacy, and multiple chronic diseases. Research by other scholars [ 15 ] has revealed that polypharmacy is positively correlated with the proportion of PIM prescriptions detected, which is consistent with the results of this study. Owing to the physiological decline caused by aging and the presence of chronic diseases [ 16 ] , older patients are highly susceptible to polypharmacy. This discrepancy may be due to differences in sample size. PIMs are also related to the patient's disease status and department. Alhawassi et al. [ 17 ] showed that polypharmacy and the presence of certain chronic comorbidities are associated with a greater risk of PIM use in older patients. Similar conclusions have been drawn by other scholars [ 18 – 20 ] . Therefore, age and the number of diseases affect not only the types and quantities of medications used but also the factors influencing PIM use in older patients. Clinically, it is important to focus on older patients with multiple diseases, especially those who are older, and to strengthen the assessment and monitoring of medication use. Different PIM evaluation criteria are formulated on the basis of national conditions and medication use in various countries, and there are differences in content, number of items, and target populations. A comparison of the 2019 Beers criteria and the 2014 STOPP criteria with the Chinese PIM standard revealed that the 2019 Beers and 2014 STOPP criteria apply to patients aged ≥ 65 years, whereas the Chinese PIM standard applies to patients aged ≥ 60 years. The 2019 beer criteria list a total of 99 PIMs, the 2014 STOPP criteria contain 81 PIMs, and the Chinese PIM standard includes 106 PIMs. The 2019 Beers criteria cover five major categories: medications that older adults should avoid, medications to be used with caution in certain disease states, medications to be avoided in renal impairment, drug–drug interactions, and drug–disease interactions. The 2014 STOPP criteria are classified by physiological systems and involve 13 categories, including respiratory and central nervous systems, focusing mainly on PIMs in specific disease states, whereas the Chinese PIM standards are divided into two categories: medications to be avoided and PIMs in disease states. Therefore, the Chinese PIM fully considers the disease spectrum and medication habits of older individuals in China. It includes criteria for judging PIMs under disease conditions and has undergone multiple rounds of expert deliberation and validation in drug classification and risk assessment, ensuring the scientific and practical nature of the PIM items. Owing to cultural differences and medical backgrounds, standards developed for other populations may not be suitable for the older population in China. Moreover, standards developed for other populations may not be comprehensive in their updates and revisions. Therefore, this study used the Chinese PIM standard for analysis. Machine learning models are now widely applied in various medical fields, including disease diagnosis, medical decision-making, and predicting the risk of adverse outcomes after medication use. Previous scholars have also constructed machine learning models to predict PIMs. Tian et al. [ 21 ] Logistic regression was used to construct a nomogram to predict the risk of PIM use in older patients with lung cancer, and the nomogram was constructed using six influencing factors: payment, charge, disease, polypharmacy, insomnia, and pain. The results of the ROC curve analysis were satisfactory, and both the internal and external validation results were good. The nomogram demonstrated a high net benefit in DCA. This finding indicates that the use of a nomogram for analyzing influencing factors is valuable. When similar methods are used to study patients with other diseases, attention should be given to the selection of influencing factors, and a reasonable judgment should be made on whether the use of multiple medications by elderly patients with multimorbidity increases the risk of PIM. These studies focused on patients with lung cancer, and there are no reports on the construction of PIM risk prediction models for older patients with depression. Moreover, most of the above studies were single-center studies, while this study is based on multicenter data and uses the Chinese PIM standard, which is more suitable for domestic use. Therefore, we conclude that a PIM risk prediction model tailored to specific populations is essential. With a large, multicenter sample covering seven Chinese cities, our findings are robust. Using this nomogram, clinicians can identify PIMs in older patients with depression more accurately and rapidly, improving review efficiency, saving time and costs, and markedly reducing manual-review error rates. This tool promotes rational prescribing, enables early recognition of high-risk individuals, optimizes medication plans, diminishes adverse reactions, and enhances therapeutic outcomes, thereby providing a valuable reference for clinical practice. This study has several limitations that warrant attention. First, only the Chinese PIM criteria were applied; no comparison was made with the STOPP or 2019 Beers criteria, so global benchmarks for PIM are unavailable. Second, given the constraints of the dataset, the model may be overfit; techniques such as regularization and cross-validation were not employed for further optimization. Third, the data pertain to older outpatients with depression, and because follow-up in outpatient settings is limited, the subsequent real-world clinical impact remains uncertain. 5. Conclusion On the basis of real-world prescription data, this study used logistic regression to construct a PIM risk prediction model for older patients with depression according to the Chinese PIM standard. This model provides scientific support for doctors and clinical pharmacists and offers reliable evidence for the medication safety of older patients with depression. The results show that the prediction model can assist medical staff in different regions and different levels of medical institutions in prescribing for older patients with depression, effectively reducing the risk of PIM use and improving medication safety. This research is highly clinically important for improving rational medication use guidelines for older patients with depression in China. Declarations Conflict of interest The authors declare that they have no competing interests. Data sharing statement All data generated or analyzed during this study inquiries can be obtained from the corresponding author. Clinical trial number Not applicable. Ethics statement This study was approved by the Sichuan University West China Hospital Research Ethics Board (2024/810). All procedures performed in this study conformed to the standards of the 1964 Helsinki Declaration and subsequent relevant ethics. Due to the requirement for data to be anonymized, the individual patients could not be asked for consent to participate; therefore, we applied for an exception to the requirement of informed consent, and the West China Hospital Research Ethics Board ethics committee approved our request. Author contributions Study concept and design: Baihui Wu. Acquisition of data: Fangyuan Tian, Zhaoyan Chen. Analysis and interpretation of data: Baihui Wu. Drafting of the manuscript: Baihui Wu. Critical revision of the manuscript for important intellectual content: Fangyuan Tian Funding This work was supported by National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University (Z2024YY003), Research project on high quality development of Hospital pharmacy, National Institute of Hospital Administration, NHC, China (NIHAYSZX2531), 1·3·5 project for disciplines of excellence-Clinical Research Fund, West China Hospital, Sichuan University (2024HXFH015), "Qimingxing" Research Fund for Young Talents (HXQMX0065), Research Project established by Chinese Pharmaceutical Association Hospital Phamacy Department (CPA-Z05-ZC-2024002). Acknowledgments The corresponding author had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Role of the Funder/Sponsor statement The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. References Kaufmann C P, Tremp R, Hersberger K E, et al. Inappropriate prescribing: a systematic overview of published assessment tools[J]. European Journal of Clinical Pharmacology, 2014, 70(1): 1-11. Ren X, Yu S, Dong W, et al. Burden of depression in China, 1990-2017: Findings from the global burden of disease study 2017[J]. Journal of Affective Disorders, 2020, 268: 95-101. Wang Y, Yang Z, Yao Q. Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning[J]. Communications Medicine, 2024, 4(1): 1-12. Pichini S, Di Trana A, García-Algar O, et al. Editorial: Drug-drug interactions in pharmacology[J]. Frontiers in Pharmacology, 2023, 14: 1155738. Doos L, Roberts E O, Corp N, et al. Multi-drug therapy in chronic condition multimorbidity: a systematic review[J]. Family Practice, 2014, 31(6): 654-663. Tian F, Chen Z, Zeng Y, Feng Q, Chen X. Prevalence of Use of Potentially Inappropriate Medications Among Older Adults Worldwide: A Systematic Review and Meta-Analysis. JAMA Netw Open. 2023 Aug 1;6(8):e2326910. Alshammari H, Al-Saeed E, Ahmed Z, et al. Reviewing Potentially Inappropriate Medication in Hospitalized Patients Over 65 Using Explicit Criteria: A Systematic Literature Review[J]. Drug, Healthcare and Patient Safety, 2021, 13: 183-210. Sichieri K, Trevisan D D, Barbosa R L, et al. Potentially inappropriate medications with older people in intensive care and associated factors: a historic cohort study[J]. São Paulo Medical Journal, 142(1): e2022666. Rational Drug Use Branch of Chinese Association of Geriatric. Criteria of potentially inappropriate medications for older adults in China. Adverse Drug Reactions Journal. 2018;20(1):2–8. Patel J, Ladani A, Sambamoorthi N, et al. A Machine Learning Approach to Identify Predictors of Potentially Inappropriate Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) Use in Older Adults with Osteoarthritis[J]. International Journal of Environmental Research and Public Health, 2020, 18(1): 155. Iasonos A, Schrag D, Raj G V, et al. How to build and interpret a nomogram for cancer prognosis[J]. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 2008, 26(8): 1364-1370. Pencina M J, D’Agostino R B. Evaluating Discrimination of Risk Prediction Models: The C Statistic[J]. JAMA, 2015, 314(10): 1063-1064. Harrell F E, Lee K L, Califf R M, et al. Regression modelling strategies for improved prognostic prediction[J]. Statistics in Medicine, 1984, 3(2): 143-152. Tian F, Liao S, Chen Z, et al. The prevalence and risk factors of potentially inappropriate medication use in older Chinese inpatients with multimorbidity and polypharmacy: a cross-sectional study[J]. Annals of Translational Medicine, 2021, 9(18): 1483. Fadare J O, Desalu O O, Obimakinde A M, et al. Prevalence of inappropriate medication prescription in the elderly in Nigeria: A comparison of Beers and STOPP criteria[J]. The International Journal of Risk & Safety in Medicine, 2015, 27(4): 177-189. Drenth-van Maanen A C, Wilting I, Jansen P A F. Prescribing medicines to older people-How to consider the impact of ageing on human organ and body functions[J]. British Journal of Clinical Pharmacology, 2020, 86(10): 1921-1930. Alhawassi T M, Alatawi W, Alwhaibi M. Prevalence of potentially inappropriate medications use among older adults and risk factors using the 2015 American Geriatrics Society Beers criteria[J]. BMC geriatrics, 2019, 19(1): 154. Sanghai S, Wong C, Wang Z, et al. Rates of Potentially Inappropriate Dosing of Direct-Acting Oral Anticoagulants and Associations With Geriatric Conditions Among Older Patients With Atrial Fibrillation: The SAGE-AF Study[J]. Journal of the American Heart Association, 2020, 9(6): e014108. Muhlack D C, Hoppe L K, Saum K U, et al. Investigation of a possible association of potentially inappropriate medication for older adults and frailty in a prospective cohort study from Germany[J]. Age and Ageing, 2019, 49(1): 20-25. Delgado J, Jones L, Bradley M C, et al. Potentially inappropriate prescribing in dementia, multi-morbidity and incidence of adverse health outcomes[J]. Age and Ageing, 2021, 50(2): 457-464. Tian F, Chen Z, Wu B. Development and validation of a nomogram to predict the risk of potentially inappropriate medication use in older lung cancer outpatients with multimorbidity[J]. Expert Opinion on Drug Safety, 2023, 22(8): 725-732. Additional Declarations No competing interests reported. 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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-7351975","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513383916,"identity":"da8dd66c-17db-4b24-b9a8-6d61cd5bc76a","order_by":0,"name":"Baihui Wu","email":"","orcid":"","institution":"National Clinical Research Center for Geriatrics, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Baihui","middleName":"","lastName":"Wu","suffix":""},{"id":513383917,"identity":"c4ae1814-72e4-406e-9886-7ba8ca5a420d","order_by":1,"name":"Zhaoyan Chen","email":"","orcid":"","institution":"National Clinical Research Center for Geriatrics, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Zhaoyan","middleName":"","lastName":"Chen","suffix":""},{"id":513383918,"identity":"1ba85529-5b9f-49a9-99b9-285430090b45","order_by":2,"name":"Fangyuan Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYDACZiCugHESKiTk+JmZDz/Ap4MHpOUMjPfhjI2xZDtbmgFeLQxIWhhntqQlbjjPoyCBT4s9O/OzBwdq7thtuJH87DFvw2Fj48M8DAYMNTbRuB3GZm5w4Niz5A030syNeXccljM7zHvgAcOxtNwG3H4xk/7AdjjZ4EaCmTTvmcPGZof5EgwYGw7j0cL+TeLAP5CW9G/SvG2HEzc38xhI4NfCYyZxsO2wncGNHDPJmW1A7zMT0nKYp0ziYN/hBMkzb8okQIEscRgYyAl4/MLef3ybxIFvh+35jqdvkwBHZf/hww8+1Njg1AIDiQsOIHMTCCgHAXt5QoaOglEwCkbByAUAPthgLfAXawYAAAAASUVORK5CYII=","orcid":"","institution":"National Clinical Research Center for Geriatrics, Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Fangyuan","middleName":"","lastName":"Tian","suffix":""}],"badges":[],"createdAt":"2025-08-12 06:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7351975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7351975/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91505427,"identity":"12ff3bbe-23d6-4850-8e0e-a8080132b099","added_by":"auto","created_at":"2025-09-17 08:17:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139145,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of older depressed patients identified in the study.\u003c/p\u003e\n\u003cp\u003eROC, receiver operating characteristic; DCA, decision curve analysis\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7351975/v1/ba4480e3939cb2e6057a28b8.png"},{"id":91505430,"identity":"0f79d20b-f710-4e16-89fa-2d642248fc8b","added_by":"auto","created_at":"2025-09-17 08:17:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":281149,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for PIM in older patients with depression in Chengdu (a); ROC curve for the training (b) and validation (c, d) cohorts for PIM; calibration curve for the training (e) and validation (f, g) cohorts for PIM.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7351975/v1/f5860dc4af66e3368340bc0c.png"},{"id":91505426,"identity":"458dbcd9-9c0e-436a-8037-12943daf2705","added_by":"auto","created_at":"2025-09-17 08:17:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13084,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for PIM in older patients with depression\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7351975/v1/c392a5b44f5ee127ead82dc4.png"},{"id":91511428,"identity":"cfd1210b-a28c-4463-92ed-8f213ad2914e","added_by":"auto","created_at":"2025-09-17 08:49:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1445361,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7351975/v1/3ea709c1-0684-4abd-be05-e999462479b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a nomogram to predict the risk of potentially inappropriate medication use in older depression outpatients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccording to data provided by the World Health Organization (WHO), the global prevalence of late-life depression ranges from 10\u0026ndash;20%, and in certain regions or specific populations, this proportion may be even greater\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In recent years, with the intensification of global population aging, the number of people suffering from late-life depression has been increasing annually. In China, late-life depression is particularly severe, with a depression incidence rate of approximately 10.5%\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Moreover, it is common for older individuals to suffer from multiple diseases and use multiple medications. The interactions between various drugs and the long-term use of medications due to chronic comorbidities make drug interactions complex\u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, which can affect drug efficacy and increase the risk of adverse reactions. This may lead to the occurrence of potentially inappropriate medication (PIM) use. PIM refers to the situation where the potential adverse risks of using such medications may outweigh the expected clinical benefits\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough late-life depression is a common mental disorder, there is currently a lack of effective clinical treatment methods, and pharmacotherapy remains the primary intervention. However, many older patients with depression have limited long-term therapeutic effects from drug treatment, poor tolerance to pharmacotherapy, and are prone to adverse reactions. Many of these drugs are PIMs, making older patients with depression a key focus group for PIM use.\u003c/p\u003e\u003cp\u003eIn evidence-based medicine and clinical practice, PIM is commonly used to evaluate the quality of drug therapy, including issues such as drug overdose, drug‒drug interactions, repeated dosing, and improper drug dosage\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. One of the important factors that leads to a decrease in the safety of drug therapy is that the drugs prescribed are PIMs. This not only wastes medical resources but also may increase the risk of medication use for patients, reduce their quality of life, and lead to adverse outcomes such as fractures, falls, rehospitalization, and death\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is essential to pay close attention to the PIM issue in older patients with depression to reduce the medication risk in this population. Therefore, this study selects the prescriptions of older outpatients with depression as the research object and analyses the PIM situation and influencing factors of older outpatients in China. These findings provide guidance for future efforts to reduce medication risks and improve the medication environment for older patients with depression.\u003c/p\u003e\u003cp\u003eThe development of the Chinese PIM standard not only integrates multicenter monitoring data and clinical medication data from China but also involves multiple rounds of expert deliberation and validation in drug classification and risk assessment. It fully considers the disease spectrum characteristics and medication behavior features of older individuals in China, especially the PIM criteria under disease conditions, to ensure the scientific and clinical applicability of the standard\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Directly applying foreign PIM standards for assessment may lead to inaccurate results. Therefore, this study uses the Chinese PIM standard for data screening.\u003c/p\u003e\u003cp\u003eIn recent years, with the accumulation of medical big data and the advancement of artificial intelligence, machine learning has achieved significant success in the medical field, especially in disease prediction, diagnosis, and personalized treatment\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, demonstrating great potential. It has been widely applied in various medical scenarios, such as intelligent diagnosis, medication consultation, and personalized medication, effectively improving the accuracy of clinical decision-making and the efficiency of diagnosis and treatment and promoting the transformation of medical models toward intelligent development.\u003c/p\u003e\u003cp\u003eSince the national data are from a multicenter sample, there may be too many influencing factors due to differences in local medical levels and lifestyle habits such as diet, which may affect the model fit. Considering the availability of data, model applicability, and flexibility of the research design, data from the Chengdu area were selected for model construction. The first quarter includes the Spring Festival holiday, which may lead to abnormal medical visit patterns and medication behaviors. Data from the 2nd to 4th quarters cover spring, summer, and autumn and can more comprehensively reflect the disease spectrum and medication characteristics under different climatic conditions. This makes it easier to identify the influencing factors of PIM use in elderly patients with depression. Therefore, a PIM model for older patients with depression was constructed via logistic regression on the basis of data from the 2nd to 4th quarters of the Chengdu area, with the first quarter data of Chengdu used for internal validation. Additionally, a large discrepancy in the number of prescriptions between the internal and external validation sets may lead to model performance distortion due to data distribution bias. To ensure the scientific rigor of the model, an external validation was conducted using data from Zhengzhou with a similar number of prescriptions. This selection can better control variables, improve the accuracy and reliability of the model, achieve better model fit, and make full use of existing resources and data advantages to explore the model's goodness of fit and calibration degree, providing references for further model optimization.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data sources\u003c/h2\u003e\u003cp\u003eThe data for this study were derived from the \u0026ldquo;Hospital Prescription Analysis Collaboration Project\u0026rdquo; of the Chinese Pharmaceutical Association. Initially, a cluster sampling method was employed to select the prescription medication data of older patients with depression from January to December 2021 across six major regions in China (North China, Central South China, Northeast China, Central China, East China, and Southwest China). The study included seven cities: Chengdu, Beijing, Guangzhou, Shanghai, Tianjin, Zhengzhou, and Hangzhou. The data extracted included patient personal information (age and sex), medication information (drug name and cost), and diagnostic information. Specifically, this study focused on the prescription data of older patients with depression from the Chengdu and Zhengzhou regions from January to December 2021.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Inclusion and Exclusion Criteria\u003c/h2\u003e\u003cp\u003eThe inclusion criteria were as follows: (1) aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. (2) Outpatient data. (3) Clear diagnosis of depression, which is based on the \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition\u003c/em\u003e (DSM-5) and the \u003cem\u003eInternational Classification of Diseases (10th Edition)\u003c/em\u003e (ICD-10).\u003c/p\u003e\u003cp\u003eThe exclusion criteria were as follows: (1) unclear diagnosis or incomplete information records. (2) No pharmacotherapy was used.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Data Cleaning\u003c/h2\u003e\u003cp\u003eData cleaning involved the removal of records where the patient's age was less than 65 years, incomplete or missing prescription information, or where the prescribed medication was a topical preparation or solvent. Additionally, prescription records from the same case number and date were merged and counted as a single case. According to the inclusion and exclusion criteria, a total of 3 prescriptions were removed by data cleaning.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Prescription identification and statistics\u003c/h2\u003e\u003cp\u003eAfter data cleaning, prescriptions were identified, and their quantities were counted via \u0026ldquo;prescription codes\u0026rdquo;. Each prescription code corresponds to a single prescription record and is unique, covering all medication records from a patient's single visit.\u003c/p\u003e\u003cp\u003eWhen determining and calculating the frequency of PIM use, the following principles were applied: (1) If a prescribed medication met any single criterion of the PIM evaluation standard, it was counted as one PIM occurrence. (2) If a prescribed medication met multiple criteria of the PIM evaluation standard, the occurrences were cumulatively tallied.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Evaluation criteria\u003c/h2\u003e\u003cp\u003eA national multicenter cross-sectional survey was conducted to use the Chinese PIM standard to identify and analyze PIM usage among older patients with depression in China.\u003c/p\u003e\u003cp\u003eThe Chinese PIM standard consists of two parts. The first part includes 13 major categories of 72 drugs/classes of drugs, such as drugs for the nervous system, cardiovascular system, and antiallergic system. Each drug/class of drugs is accompanied by 1 to 6 risk points for drug use. These risk points focus on severe, frequent, and common risks. Some drugs also include additional points of concern and medication suggestions, which help clinicians and pharmacists accurately and quickly identify the risks of each drug, making them more practical and referable. On the basis of the average score of expert-evaluated indicators, drugs are classified by risk intensity: (1) High-risk medications should be avoided by older individuals. (2) Low-risk medications should be used with caution by older patients.\u003c/p\u003e\u003cp\u003ePart 2 includes 44 types/classes of drugs across 27 disease states, requiring joint evaluation of both the disease and the drug to determine whether the medication is a PIM. On the basis of medication frequency (total annual consumption of a drug divided by its defined daily dose), the 44 types/classes of drugs are categorized into Class A and Class B alert medications: (1) Class A warning drugs with a usage frequency\u0026thinsp;\u0026ge;\u0026thinsp;3000, such as the use of nonsteroidal anti-inflammatory drugs in heart failure. (2) Class B warning drugs with a usage frequency\u0026thinsp;\u0026lt;\u0026thinsp;3000, such as the use of triazolam for insomnia. Organizing medications on the basis of disease state facilitates the search for whether a PIM is being used, making this standard more clinically instructive.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\u003cp\u003eIn this study, the construction of a PIM risk prediction model for older patients with depression is a typical binary classification task. Therefore, this study employed logistic regression to construct a risk prediction model. Logistic regression is suitable for situations where the dependent variable is categorical, especially when there is no linear relationship between the independent and dependent variables, allowing for more effective model building and analysis.\u003c/p\u003e\u003cp\u003eDuring the logistic regression analysis, all included variables were normalized and encoded, a process that runs through key stages such as data preprocessing, model training, and model saving. In this study, all variables were transformed into categorical variables for analysis. For continuous variables, such as age, a discretization preprocessing method was used. On the basis of previous literature or medical reference value ranges, these variables were converted into categorical variables and encoded according to different age groups. For unordered categorical variables, such as the number of diseases, number of medications, and reimbursement type, a binary or multicategory approach was used.\u003c/p\u003e\u003cp\u003eThe precision of the model is closely related to the factors included. Different factors have varying degrees of impact on the outcomes. Including all factors indiscriminately may reduce the computational efficiency of the model and even lead to overfitting. Therefore, it is essential to select factors that significantly influence the model. In this study, binary logistic regression was used for univariate analysis to screen for factors with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. These factors were then included in the multivariate logistic regression analysis. The results are presented as odds ratios (ORs) and 95% confidence intervals (CIs). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eVariables with statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the multivariate logistic regression analysis were used to construct a nomogram via R Studio version 4.2.0. A nomogram is a graphical tool for visualizing prediction models. It converts the values of each predictive variable into scores, sums these scores to obtain a total score, and then locates the corresponding PIM prediction probability on the nomogram on the basis of the total score. The nomogram can intuitively display the results of logistic regression\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe model was constructed on the basis of the selected factors. The prescriptions of older patients with depression in Chengdu in 2021 were divided by quarter. The data from the last three quarters were used as the training set, whereas the data from the first quarter were used as the internal validation set. The prescription data of older patients with depression in Zhengzhou, China, in 2021 were used as the external validation set. The goodness of fit of the model constructed from the training set was verified via both internal and external validation sets.\u003c/p\u003e\u003cp\u003eWhen evaluating the goodness of fit of a constructed model, the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are commonly used. The closer the AUC value is to 1, the stronger the model's discriminatory ability. An AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 is generally considered to indicate good discriminatory power\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The Hosmer\u0026ndash;Lemeshow test is used to assess the calibration ability of the model, that is, the consistency between the predicted probabilities of the model and the actual observed results. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the test is generally considered to indicate good calibration of the model\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Descriptive Statistics\u003c/h2\u003e\n \u003cp\u003eThis study included a total of 6,249 prescriptions for older patients with depression. In the training set (n\u0026thinsp;=\u0026thinsp;3,548), there were 1,547 prescriptions (45.5%) for older patients with depression involving PIM. In the internal validation set (n\u0026thinsp;=\u0026thinsp;1,081), 497 prescriptions (46.0%) involving PIMs were included. In the external validation set (n\u0026thinsp;=\u0026thinsp;1,620), there were 1,015 prescriptions (62.7%) involving PIMs. The patient selection process is depicted in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003ePrescriptions for older patients with depression aged\u0026thinsp;\u0026ge;\u0026thinsp;80 years accounted for 24.2% (n\u0026thinsp;=\u0026thinsp;1,512) of the sample. Among the prescriptions for older patients with depression, 78.16% (n\u0026thinsp;=\u0026thinsp;4,884) had multiple chronic conditions, and 16.96% (n\u0026thinsp;=\u0026thinsp;1,060) included\u0026thinsp;\u0026ge;\u0026thinsp;5 medications. Additionally, anxiety symptoms were present in 57.67% (n\u0026thinsp;=\u0026thinsp;3,604) of the prescriptions, and insomnia symptoms were present in 28.9% (n\u0026thinsp;=\u0026thinsp;1,806) of the prescriptions. The basic characteristics of the patients are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBasic characteristics of the training, internal validation, and external validation methods\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraining (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInternal Validation (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExternal Validation (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1615(45.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e497(46.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1015(62.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1933(54.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e584(54.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e605(37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospital level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2nd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68(1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31(2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73(4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3nd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3480(98.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1050(97.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1547(95.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2657(74.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e808(74.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1241(76.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychiatry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e743(20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233(21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e228(14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeriatrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148(4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40(3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151(9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2379(67.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e705(65.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e991(61.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1169(32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e376(34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e629(38.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2890(81.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e853(78.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e994(61.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e658(18.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e228(21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e626(38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e783(22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234(21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e348(21.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1758(49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e571(52.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e833(51.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1007(28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e276(25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e439(27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2969(83.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e924(85.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1296(80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e579(16.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e157(14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e324(20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePayment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1218(34.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e417(38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169(10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartial Fee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1368(38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e352(32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e447(27.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull fee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e962(27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e312(28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1004(62.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1971(55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e588(54.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1045(64.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1577(44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e493(45.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e575(35.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1012(28.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e315(29.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e479(29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2536(71.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e766(70.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1141(70.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78(2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15(1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85(5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3470(97.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1066(98.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1535(94.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e836(23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e251(23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e383(23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2712(76.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e830(76.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1237(76.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e324(9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101(9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e325(20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3224(90.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e980(90.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1295(79.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e389(11.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104(9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e202(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3159(89.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e977(90.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1418(87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54(1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13(1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35(2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3494(98.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1068(98.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1585(97.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e188(5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57(5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96(5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3360(94.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1024(94.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1524(94.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119(3.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20(1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38(2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3429(96.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1061(98.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1582(97.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e542(15.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e154(14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136(8.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3006(84.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e927(85.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1484(91.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Influencing Factors\u003c/h2\u003e\n \u003cp\u003eThe results of the univariate analysis for older patients with depression revealed that the number of comorbid diseases, type of reimbursement, department name, hospital level, anxiety disorder, sleep disorder, diabetes, hyperlipidemia, osteoporosis, and cerebrovascular disease were significantly associated with the occurrence of PIMs in older patients with depression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eThe 10 variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 from the univariate analysis were subsequently included in the multivariate logistic regression analysis. The results indicated that the number of comorbid diseases, type of reimbursement, department name, hospital level, anxiety disorder, sleep disorder, diabetes, hyperlipidemia, and cerebrovascular disease were significant factors influencing the occurrence of PIM use in older patients with depression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The detailed results are shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of univariate and multivariate logistic regression analyses\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate Analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate Analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospital level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospital level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2nd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2nd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3nd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.251(0.138\u0026ndash;0.431)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3nd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.193(0.104\u0026ndash;0.341)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepartment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychiatry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.563(2.167\u0026ndash;3.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePsychiatry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.812(2.325\u0026ndash;3.406)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeriatrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.357(0.233\u0026ndash;0.530)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGeriatrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.483(0.310\u0026ndash;0.732)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.949(0.825\u0026ndash;1.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.201(1.014\u0026ndash;1.423)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.414(1.168\u0026ndash;1.714)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.316(1.110\u0026ndash;1.560)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.444(1.165\u0026ndash;1.792)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.020(0.844\u0026ndash;1.233)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.273(0.979\u0026ndash;1.658)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo.of medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. of medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.835(0.697\u0026ndash;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.814(0.650\u0026ndash;1.017)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePayment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePayment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartial Fee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.306(0.260\u0026ndash;0.360)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartial Fee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.386(0.322\u0026ndash;0.461)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull fee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.843(0.711\u0026ndash;0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFull fee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.874(0.710\u0026ndash;1.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.839(0.734\u0026ndash;0.958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.784(0.674\u0026ndash;0.913)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSleep disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.421(2.086\u0026ndash;2.813)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.422(2.032\u0026ndash;2.890)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDementia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.974(0.617\u0026ndash;1.526)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.813(0.695\u0026ndash;0.951)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.816(0.669\u0026ndash;0.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary\u003c/p\u003e\n \u003cp\u003eheart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoronary\u003c/p\u003e\n \u003cp\u003eheart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.796(0.630\u0026ndash;1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.497(0.395\u0026ndash;0.621)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.495(0.379\u0026ndash;0.645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.700(0.395\u0026ndash;1.210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.766(0.566\u0026ndash;1.032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOsteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.488(0.323\u0026ndash;0.721)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.708(0.451\u0026ndash;1.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebrovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.205(1.003\u0026ndash;1.447)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.267(1.024\u0026ndash;1.567)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 A nomogram for predicting PIM\u003c/h2\u003e\n \u003cp\u003eUsing multivariate logistic regression, the following variables were selected to construct the prediction model: department name, reimbursement type, hospital level, age, number of diseases, sleep disorders, hypertension, and cerebrovascular disease. A nomogram was then developed on the basis of these variables, and the results are shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eThe AUC value of the training set for the PIM model in older patients with depression was 0.72, indicating good discriminatory ability. The Hosmer\u0026ndash;Lemeshow test value was 0.29, suggesting that the model had good calibration.\u003c/p\u003e\n \u003cp\u003eThe model was applied to the internal validation set for testing. The internal validation results revealed that the AUC value of the logistic regression model was 0.668, indicating acceptable discriminatory ability. The Hosmer\u0026ndash;Lemeshow test value was less than 0.05, suggesting that the model calibration was not ideal.\u003c/p\u003e\n \u003cp\u003eThe model was applied to the external validation set to evaluate its generalizability and extrapolation capability. The external validation results revealed that the AUC value of the logistic regression model was 0.663, indicating moderate discriminatory ability. The Hosmer\u0026ndash;Lemeshow test value was less than 0.05, suggesting that the model calibration was not ideal. The ROC curve is shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, and the calibration curve is shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eFor PIM in older patients with depression, DCA revealed that the nomogram has a greater net benefit than strategies based on considering either all patients or no patients for intervention at risk thresholds up to approximately 70% (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo the best of our knowledge, this study is the first attempt to assess the influencing factors of PIM in Chinese elderly outpatients. On the basis of the Chinese PIM standard, we calculated the detection proportion of PIMs in outpatient prescriptions for older patients with depression and identified influencing factors to construct a PIM risk prediction model for older patients with depression in Chengdu. The predictor analysis results revealed that age, number of diseases, and polypharmacy are closely related to the occurrence of PIM use in older patients. According to a study by Tian et al.\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, PIM use is associated with sex, polypharmacy, and multiple chronic diseases. Research by other scholars\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e has revealed that polypharmacy is positively correlated with the proportion of PIM prescriptions detected, which is consistent with the results of this study. Owing to the physiological decline caused by aging and the presence of chronic diseases\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, older patients are highly susceptible to polypharmacy. This discrepancy may be due to differences in sample size.\u003c/p\u003e\u003cp\u003ePIMs are also related to the patient's disease status and department. Alhawassi et al.\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e showed that polypharmacy and the presence of certain chronic comorbidities are associated with a greater risk of PIM use in older patients. Similar conclusions have been drawn by other scholars\u003csup\u003e[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Therefore, age and the number of diseases affect not only the types and quantities of medications used but also the factors influencing PIM use in older patients. Clinically, it is important to focus on older patients with multiple diseases, especially those who are older, and to strengthen the assessment and monitoring of medication use.\u003c/p\u003e\u003cp\u003eDifferent PIM evaluation criteria are formulated on the basis of national conditions and medication use in various countries, and there are differences in content, number of items, and target populations. A comparison of the 2019 Beers criteria and the 2014 STOPP criteria with the Chinese PIM standard revealed that the 2019 Beers and 2014 STOPP criteria apply to patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, whereas the Chinese PIM standard applies to patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years. The 2019 beer criteria list a total of 99 PIMs, the 2014 STOPP criteria contain 81 PIMs, and the Chinese PIM standard includes 106 PIMs. The 2019 Beers criteria cover five major categories: medications that older adults should avoid, medications to be used with caution in certain disease states, medications to be avoided in renal impairment, drug\u0026ndash;drug interactions, and drug\u0026ndash;disease interactions. The 2014 STOPP criteria are classified by physiological systems and involve 13 categories, including respiratory and central nervous systems, focusing mainly on PIMs in specific disease states, whereas the Chinese PIM standards are divided into two categories: medications to be avoided and PIMs in disease states. Therefore, the Chinese PIM fully considers the disease spectrum and medication habits of older individuals in China. It includes criteria for judging PIMs under disease conditions and has undergone multiple rounds of expert deliberation and validation in drug classification and risk assessment, ensuring the scientific and practical nature of the PIM items. Owing to cultural differences and medical backgrounds, standards developed for other populations may not be suitable for the older population in China. Moreover, standards developed for other populations may not be comprehensive in their updates and revisions. Therefore, this study used the Chinese PIM standard for analysis.\u003c/p\u003e\u003cp\u003eMachine learning models are now widely applied in various medical fields, including disease diagnosis, medical decision-making, and predicting the risk of adverse outcomes after medication use. Previous scholars have also constructed machine learning models to predict PIMs. Tian et al.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e Logistic regression was used to construct a nomogram to predict the risk of PIM use in older patients with lung cancer, and the nomogram was constructed using six influencing factors: payment, charge, disease, polypharmacy, insomnia, and pain. The results of the ROC curve analysis were satisfactory, and both the internal and external validation results were good. The nomogram demonstrated a high net benefit in DCA. This finding indicates that the use of a nomogram for analyzing influencing factors is valuable. When similar methods are used to study patients with other diseases, attention should be given to the selection of influencing factors, and a reasonable judgment should be made on whether the use of multiple medications by elderly patients with multimorbidity increases the risk of PIM. These studies focused on patients with lung cancer, and there are no reports on the construction of PIM risk prediction models for older patients with depression. Moreover, most of the above studies were single-center studies, while this study is based on multicenter data and uses the Chinese PIM standard, which is more suitable for domestic use. Therefore, we conclude that a PIM risk prediction model tailored to specific populations is essential. With a large, multicenter sample covering seven Chinese cities, our findings are robust. Using this nomogram, clinicians can identify PIMs in older patients with depression more accurately and rapidly, improving review efficiency, saving time and costs, and markedly reducing manual-review error rates. This tool promotes rational prescribing, enables early recognition of high-risk individuals, optimizes medication plans, diminishes adverse reactions, and enhances therapeutic outcomes, thereby providing a valuable reference for clinical practice.\u003c/p\u003e\u003cp\u003eThis study has several limitations that warrant attention. First, only the Chinese PIM criteria were applied; no comparison was made with the STOPP or 2019 Beers criteria, so global benchmarks for PIM are unavailable. Second, given the constraints of the dataset, the model may be overfit; techniques such as regularization and cross-validation were not employed for further optimization. Third, the data pertain to older outpatients with depression, and because follow-up in outpatient settings is limited, the subsequent real-world clinical impact remains uncertain.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOn the basis of real-world prescription data, this study used logistic regression to construct a PIM risk prediction model for older patients with depression according to the Chinese PIM standard. This model provides scientific support for doctors and clinical pharmacists and offers reliable evidence for the medication safety of older patients with depression. The results show that the prediction model can assist medical staff in different regions and different levels of medical institutions in prescribing for older patients with depression, effectively reducing the risk of PIM use and improving medication safety. This research is highly clinically important for improving rational medication use guidelines for older patients with depression in China.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study inquiries can be obtained from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Sichuan University West China Hospital Research Ethics Board (2024/810). All procedures performed in this study conformed to the standards of the 1964 Helsinki Declaration and subsequent relevant ethics. Due to the requirement for data to be anonymized, the individual patients could not be asked for consent to participate; therefore, we applied for an exception to the requirement of informed consent, and the West China Hospital Research Ethics Board ethics committee approved our request.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: Baihui Wu. Acquisition of data: Fangyuan Tian, Zhaoyan Chen. Analysis and interpretation of data: Baihui Wu. Drafting of the manuscript: Baihui Wu. Critical revision of the manuscript for important intellectual content: Fangyuan Tian\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University (Z2024YY003), Research project on high quality development of Hospital pharmacy, National Institute of Hospital Administration, NHC, China (NIHAYSZX2531), 1\u0026middot;3\u0026middot;5 project for disciplines of excellence-Clinical Research Fund, West China Hospital, Sichuan University (2024HXFH015), \u0026quot;Qimingxing\u0026quot; Research Fund for Young Talents (HXQMX0065), Research Project established by Chinese Pharmaceutical Association Hospital Phamacy Department (CPA-Z05-ZC-2024002).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of the Funder/Sponsor statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKaufmann C P, Tremp R, Hersberger K E, et al. 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BMC geriatrics, 2019, 19(1): 154.\u003c/li\u003e\n\u003cli\u003eSanghai S, Wong C, Wang Z, et al. Rates of Potentially Inappropriate Dosing of Direct-Acting Oral Anticoagulants and Associations With Geriatric Conditions Among Older Patients With Atrial Fibrillation: The SAGE-AF Study[J]. Journal of the American Heart Association, 2020, 9(6): e014108.\u003c/li\u003e\n\u003cli\u003eMuhlack D C, Hoppe L K, Saum K U, et al. Investigation of a possible association of potentially inappropriate medication for older adults and frailty in a prospective cohort study from Germany[J]. Age and Ageing, 2019, 49(1): 20-25.\u003c/li\u003e\n\u003cli\u003eDelgado J, Jones L, Bradley M C, et al. Potentially inappropriate prescribing in dementia, multi-morbidity and incidence of adverse health outcomes[J]. Age and Ageing, 2021, 50(2): 457-464.\u003c/li\u003e\n\u003cli\u003eTian F, Chen Z, Wu B. Development and validation of a nomogram to predict the risk of potentially inappropriate medication use in older lung cancer outpatients with multimorbidity[J]. Expert Opinion on Drug Safety, 2023, 22(8): 725-732.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Older, depression, potentially inappropriate medication, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7351975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7351975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Potentially inappropriate medication (PIM) for the elderly is a serious public health problem associated with increased adverse drug events. PIM refers to medications where the adverse risks outweigh the potential benefits. Identifying the risk factors for PIM is essential for optimizing prescription practices and improving patient safety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo establish a risk prediction model for potentially inappropriate medications (PIMs) in older patients with depression, providing guidance to optimize medication plans, reduce adverse drug reactions, and improve treatment outcomes and quality of life.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe prescription of depression patients in all hospitals in the Chengdu area was taken as an example. A significant factor influencing PIM risk was identified through univariate and multivariate logistic regression analyses, and a nomogram was constructed. The discrimination and calibration of the model were evaluated via receiver operating characteristic (ROC) curves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Data from the Chengdu area (n=4629) were divided into a training set (n=3548) and an internal validation set (n=1081), with Zhengzhou data (n=1620) used as the external validation set. ROC curve analysis revealed that the area under the curve (AUC) for the training set was 0.721, that for the internal validation set was 0.668, and that for the external validation set was 0.663.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The prediction model based on these factors has good predictive value for PIM occurrence in older patients with depression.\u003c/p\u003e","manuscriptTitle":"Development and validation of a nomogram to predict the risk of potentially inappropriate medication use in older depression outpatients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 08:17:16","doi":"10.21203/rs.3.rs-7351975/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T08:50:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T15:08:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-29T12:06:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T09:10:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T15:13:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149945775453197531582589007532478101572","date":"2026-03-24T12:39:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235526104114481128368857747089860955136","date":"2026-03-24T01:49:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123082835396689870647296396349380811312","date":"2026-03-23T01:44:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297740649141472005021118460030770189699","date":"2026-03-19T11:46:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209218846036499670994884375006887269843","date":"2026-03-19T04:07:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-21T09:11:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269291902653893527978429180396597264370","date":"2025-09-11T03:15:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216712523295393357527458650168965249525","date":"2025-09-11T00:02:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-08T21:31:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-21T06:19:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-19T09:41:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-19T09:40:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-08-12T06:19:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"852195eb-5fee-414a-b643-144ba7d7e85e","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T08:24:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 08:17:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7351975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7351975","identity":"rs-7351975","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}
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