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      Antidepressants Usage and Risk of Pneumonia Among Elderly Patients With the Parkinson's Disease: A Population-Based Case-Control Study

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          Abstract

          The patients with Parkinson's disease (PD) are associated with a higher risk of pneumonia. Antidepressants exert an anticholinergic effect in varying degrees and various classes of antidepressants also can produce a different effect on immune function. The relationship between the risk of pneumonia and the use of antidepressants among elderly patients with PD is unknown. The study investigated the risk of pneumonia associated with the use of antidepressants in elderly patients with PD. This case-control study was based on data from the longitudinal health insurance database in Taiwan. We analyzed the data of 551,975 elderly patients with PD between 2002 and 2018. To reduce the potential confounding caused by unbalanced covariates in non-experimental settings, we used propensity score matching to include older patients without pneumonia to serve as the comparison. The antidepressants in the study included tricyclic antidepressants (TCAs), monoamine oxidase inhibitors (MAOIs), selective serotonin reuptake inhibitors (SSRIs), serotonin, and norepinephrine reuptake inhibitors (SNRIs). The conditional logistic regression was used to investigate the association between antidepressants and pneumonia. Control variables in the study included sex, age, income level, urbanization, Charlson comorbidity index score, and comorbidities related to pneumonia. In terms of TCAs users, compared with patients not receiving TCAs, current users had a lower risk of incident pneumonia (adjusted odds ratio [ aOR] = 0.86, 95% CI = 0.82–0.90) and recent users (a OR = 0.83, 95% CI = 0.80–0.87). In terms of MAOIs users, current users had a lower risk of incident pneumonia (a OR = 0.88, 95% CI = 0.83–0.93), recent users (a OR = 0.89, 95% CI = 0.85–0.93). In terms of SSRIs users, current users had a higher risk of incident pneumonia (a OR = 1.13, 95% CI = 1.01–1.17), recent users (a OR = 1.01, 95% CI = 1.06–1.13), and past users (a OR = 1.19, 95% CI = 1.17–1.21). In terms of SNRIs users, past users had a higher risk of incident pneumonia (a OR = 1.07, 95% CI = 1.03–1.10). The incident pneumonia is associated with the use of individuals of different classes of antidepressants. The use of TCAs (such as, amitriptyline and imipramine) had a lower odds of incident pneumonia. The use of MAOIs (such as, selegiline and rasagiline) had a lower odds of pneumonia during recent use. The use of SSRIs (such as, fluoxetine, sertraline, escitalopram, paroxetine, and citalopram) and SNRIs (such as, milnacipran, and venlafaxine) had a higher odds of incident pneumonia.

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          A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation

          The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
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            Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

            Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
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              Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases

              R Deyo (1992)
              Administrative databases are increasingly used for studying outcomes of medical care. Valid inferences from such data require the ability to account for disease severity and comorbid conditions. We adapted a clinical comorbidity index, designed for use with medical records, for research relying on International Classification of Diseases (ICD-9-CM) diagnosis and procedure codes. The association of this adapted index with health outcomes and resource use was then examined with a sample of Medicare beneficiaries who underwent lumbar spine surgery in 1985 (n = 27,111). The index was associated in the expected direction with postoperative complications, mortality, blood transfusion, discharge to nursing home, length of hospital stay, and hospital charges. These associations were observed whether the index incorporated data from multiple hospitalizations over a year's time, or just from the index surgical admission. They also persisted after controlling for patient age. We conclude that the adapted comorbidity index will be useful in studies of disease outcome and resource use employing administrative databases.
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                Author and article information

                Contributors
                Journal
                Front Med (Lausanne)
                Front Med (Lausanne)
                Front. Med.
                Frontiers in Medicine
                Frontiers Media S.A.
                2296-858X
                18 February 2022
                2022
                : 9
                : 740182
                Affiliations
                [1] 1Department of Health Services Administration, China Medical University , Taichung, Taiwan
                [2] 2Department of Pharmacology, Chung Shan Medical University , Taichung, Taiwan
                [3] 3Department of Pharmacy, Chung Shan Medical University Hospital , Taichung, Taiwan
                [4] 4Department of Long Term Care, National Quemoy University , Kinmen, Taiwan
                [5] 5Department of Healthcare Administration, Asia University , Taichung, Taiwan
                [6] 6Department of Medical Research, China Medical University Hospital , Taichung, Taiwan
                Author notes

                Edited by: Santiago Perez-Lloret, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina

                Reviewed by: Madeleine Hinwood, The University of Newcastle, Australia; James Crispo, University of British Columbia, Canada

                *Correspondence: Chien-Ying Lee cshd015@ 123456csmu.edu.tw

                This article was submitted to Geriatric Medicine, a section of the journal Frontiers in Medicine

                †These authors have contributed equally to this work and share first authorship

                Article
                10.3389/fmed.2022.740182
                8896435
                35252227
                0ca36503-3256-438b-8492-b01ee2d1f4c1
                Copyright © 2022 Kuo, Huang, Kuan, Chang, Tsai and Lee.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 July 2021
                : 05 January 2022
                Page count
                Figures: 0, Tables: 3, Equations: 0, References: 48, Pages: 11, Words: 7409
                Funding
                Funded by: Ministry of Science and Technology, Taiwan, doi 10.13039/501100004663;
                Award ID: MOST 109-2410-H-039-004
                Award ID: MOST 110-2410-H-040-002
                Funded by: Chung Shan Medical University, doi 10.13039/501100008641;
                Award ID: CSMU-INT-109-07
                Funded by: China Medical University, Taiwan, doi 10.13039/501100012544;
                Award ID: CMU108-ASIA-13
                Award ID: CMU109-MF-120
                Categories
                Medicine
                Original Research

                antidepressants,pneumonia,the elderly,parkison's disease,pharmacoepidemiology

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