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All Studies   Meta Analysis    Recent:   

Prognostic Factors for COVID-19 Hospitalized Patients with Preexisting Type 2 Diabetes

Fu et al., International Journal of Endocrinology, doi:10.1155/2022/9322332
Jan 2022  
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Unfavorable outcome 72% Improvement Relative Risk Metformin for COVID-19  Fu et al.  Prophylaxis Is prophylaxis with metformin beneficial for COVID-19? Retrospective 80 patients in China (January - March 2020) Study compares with other diabetes medications Improved recovery with metformin (p=0.026) c19early.org Fu et al., Int. J. Endocrinology, January 2022 Favorsmetformin Favorsother diabet.. 0 0.5 1 1.5 2+
Metformin for COVID-19
3rd treatment shown to reduce risk in July 2020
 
*, now with p < 0.00000000001 from 93 studies.
No treatment is 100% effective. Protocols combine treatments. * >10% efficacy, ≥3 studies.
4,400+ studies for 79 treatments. c19early.org
Retrospective 108 T2D patients hospitalized with COVID-19, showing lower risk of unfavorable outcomes with metformin use vs. other diabetic medications.
risk of unfavorable outcome, 71.9% lower, RR 0.28, p = 0.03, treatment 4 of 49 (8.2%), control 9 of 31 (29.0%), NNT 4.8, unfavorable outcome, metformin vs. other treatments.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Fu et al., 17 Jan 2022, retrospective, China, peer-reviewed, median age 63.0, 14 authors, study period 8 January, 2020 - 7 March, 2020, this trial compares with another treatment - results may be better when compared to placebo. Contact: dengy@hawaii.edu.
This PaperMetforminAll
Prognostic Factors for COVID-19 Hospitalized Patients with Preexisting Type 2 Diabetes
Yuanyuan Fu, Ling Hu, Hong-Wei Ren, Yi Zuo, Shaoqiu Chen, Qiu-Shi Zhang, Chen Shao, Yao Ma, Lin Wu, Jun-Jie Hao, Chuan-Zhen Wang, Zhanwei Wang, Richard Yanagihara, Youping Deng
International Journal of Endocrinology, doi:10.1155/2022/9322332
Background. Type 2 diabetes (T2D) as a worldwide chronic disease combined with the COVID-19 pandemic prompts the need for improving the management of hospitalized COVID-19 patients with preexisting T2D to reduce complications and the risk of death. is study aimed to identify clinical factors associated with COVID-19 outcomes specifically targeted at T2D patients and build an individualized risk prediction nomogram for risk stratification and early clinical intervention to reduce mortality. Methods. In this retrospective study, the clinical characteristics of 382 confirmed COVID-19 patients, consisting of 108 with and 274 without preexisting T2D, from January 8 to March 7, 2020, in Tianyou Hospital in Wuhan, China, were collected and analyzed. Univariate and multivariate Cox regression models were performed to identify specific clinical factors associated with mortality of COVID-19 patients with T2D. An individualized risk prediction nomogram was developed and evaluated by discrimination and calibration. Results. Nearly 15% (16/108) of hospitalized COVID-19 patients with T2D died. Twelve risk factors predictive of mortality were identified. Older age (HR � 1.076, 95% CI � 1.014-1.143, p � 0.016), elevated glucose level (HR � 1.153, 95% CI � 1.038-1.28, p � 0.0079), increased serum amyloid A (SAA) (HR � 1.007, 95% CI � 1.001-1.014, p � 0.022), diabetes treatment with only oral diabetes medication (HR � 0.152, 95%CI � 0.032-0.73, p � 0.0036), and oral medication plus insulin (HR � 0.095, 95%CI � 0.019-0.462, p � 0.019) were independent prognostic factors. A nomogram based on these prognostic factors was built for early prediction of 7-day, 14-day, and 21-day survival of diabetes patients. High concordance index (C-index) was achieved, and the calibration curves showed the model had good prediction ability within three weeks of COVID-19 onset. Conclusions. By incorporating specific prognostic factors, this study provided a user-friendly graphical risk prediction tool for clinicians to quickly identify high-risk T2D patients hospitalized for COVID-19.
Conflicts of Interest e authors declare no conflicts of interest. Authors' Contributions YD and LH conceived and supervised the study. LH, QSZ, CS, YM, LW, JJH, and CZW collected the epidemiological and clinical data. LH, YZ, SC, and HWR contributed to radiological figure interpretation. YF and ZW processed and conducted statistical data analyses. YF, ZW, and RY drafted the manuscript. All the authors reviewed and approved the final version for publication. YD and YF are responsible for the integrity of the data and the accuracy of the analyzed data. Supplementary Materials Supplementary table S1 : clinical characteristics of COVID-19 patients with and without T2D. Supplementary table S2 : clinical characteristics between survivors and nonsurvivors in COVID-19 patients with T2D. Fig. S1 : representative dynamic changes in chest computer tomography (CT) scans between admission and discharge for the three diabetes treatment groups. Fig. S2 : survival analysis for the three diabetes treatment groups. Fig. S3 : blood glucose levels of the three diabetes treatment groups. (Supplementary Materials)
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This study aimed ' 'to identify clinical factors associated with COVID-19 outcomes specifically targeted at T2D ' 'patients and build an individualized risk prediction nomogram for risk stratification and ' 'early clinical intervention to reduce mortality. Methods. In this retrospective study, the ' 'clinical characteristics of 382 confirmed COVID-19 patients, consisting of 108 with and 274 ' 'without preexisting T2D, from January 8 to March 7, 2020, in Tianyou Hospital in Wuhan, ' 'China, were collected and analyzed. Univariate and multivariate Cox regression models were ' 'performed to identify specific clinical factors associated with mortality of COVID-19 ' 'patients with T2D. An individualized risk prediction nomogram was developed and evaluated by ' 'discrimination and calibration. Results. Nearly 15% (16/108) of hospitalized COVID-19 ' 'patients with T2D died. Twelve risk factors predictive of mortality were identified. Older ' 'age (HR\u2009=\u20091.076, 95% CI\u2009=\u20091.014–1.143,<jats:inline-formula><math ' 'xmlns="http://www.w3.org/1998/Math/MathML" ' 'id="M1"><mi>p</mi><mo>=</mo><mn>0.016</mn></math></jats:inline-formula>), elevated glucose ' 'level (HR\u2009=\u20091.153, 95% CI\u2009=\u20091.038–1.28,<jats:inline-formula><math ' 'xmlns="http://www.w3.org/1998/Math/MathML" ' 'id="M2"><mi>p</mi><mo>=</mo><mn>0.0079</mn></math></jats:inline-formula>), increased serum ' 'amyloid A (SAA) (HR\u2009=\u20091.007, 95% CI\u2009=\u2009' '1.001–1.014,<jats:inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" ' 'id="M3"><mi>p</mi><mo>=</mo><mn>0.022</mn></math></jats:inline-formula>), diabetes treatment ' 'with only oral diabetes medication (HR\u2009=\u20090.152, 95%CI\u2009=\u2009' '0.032–0.73,<jats:inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" ' 'id="M4"><mi>p</mi><mo>=</mo><mn>0.0036</mn></math></jats:inline-formula>), and oral ' 'medication plus insulin (HR\u2009=\u20090.095, 95%CI\u2009=\u2009' '0.019–0.462,<jats:inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" ' 'id="M5"><mi>p</mi><mo>=</mo><mn>0.019</mn></math></jats:inline-formula>) were independent ' 'prognostic factors. A nomogram based on these prognostic factors was built for early ' 'prediction of 7-day, 14-day, and 21-day survival of diabetes patients. High concordance index ' '(C-index) was achieved, and the calibration curves showed the model had good prediction ' 'ability within three weeks of COVID-19 onset. Conclusions. By incorporating specific ' 'prognostic factors, this study provided a user-friendly graphical risk prediction tool for ' 'clinicians to quickly identify high-risk T2D patients hospitalized for COVID-19.</jats:p>', 'DOI': '10.1155/2022/9322332', 'type': 'journal-article', 'created': {'date-parts': [[2022, 1, 17]], 'date-time': '2022-01-17T23:50:10Z', 'timestamp': 1642463410000}, 'page': '1-13', 'source': 'Crossref', 'is-referenced-by-count': 2, 'title': 'Prognostic Factors for COVID-19 Hospitalized Patients with Preexisting Type 2 Diabetes', 'prefix': '10.1155', 'volume': '2022', 'author': [ { 'given': 'Yuanyuan', 'family': 'Fu', 'sequence': 'first', 'affiliation': [ { 'name': 'Department of Quantitative Health Sciences, John A. 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Burns School ' 'of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA'}]}, { 'given': 'Qiu-Shi', 'family': 'Zhang', 'sequence': 'additional', 'affiliation': [ { 'name': 'Tianyou Hospital, Affiliated to Wuhan University of Science and ' 'Technology, Wuhan, Hubei, China'}]}, { 'given': 'Chen', 'family': 'Shao', 'sequence': 'additional', 'affiliation': [ { 'name': 'Tianyou Hospital, Affiliated to Wuhan University of Science and ' 'Technology, Wuhan, Hubei, China'}]}, { 'given': 'Yao', 'family': 'Ma', 'sequence': 'additional', 'affiliation': [ { 'name': 'Tianyou Hospital, Affiliated to Wuhan University of Science and ' 'Technology, Wuhan, Hubei, China'}]}, { 'given': 'Lin', 'family': 'Wu', 'sequence': 'additional', 'affiliation': [ { 'name': 'Tianyou Hospital, Affiliated to Wuhan University of Science and ' 'Technology, Wuhan, Hubei, China'}]}, { 'given': 'Jun-Jie', 'family': 'Hao', 'sequence': 'additional', 'affiliation': [ { 'name': 'Tianyou Hospital, Affiliated to Wuhan University of Science and ' 'Technology, Wuhan, Hubei, China'}]}, { 'given': 'Chuan-Zhen', 'family': 'Wang', 'sequence': 'additional', 'affiliation': [ { 'name': 'Tianyou Hospital, Affiliated to Wuhan University of Science and ' 'Technology, Wuhan, Hubei, China'}]}, { 'given': 'Zhanwei', 'family': 'Wang', 'sequence': 'additional', 'affiliation': [ { 'name': 'Cancer Epidemiology Program, University of Hawaii Cancer Center, ' 'University of Hawaii at Manoa, Honolulu, HI, USA'}]}, { 'given': 'Richard', 'family': 'Yanagihara', 'sequence': 'additional', 'affiliation': [ { 'name': 'Department of Pediatrics, John A. 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