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Zhao D, Sun H, Li H, Li C, Zhou B. A prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population. J Transl Med 2023; 21:62. [PMID: 36717874 PMCID: PMC9887817 DOI: 10.1186/s12967-023-03899-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 01/17/2023] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Cardiovascular disease (CVD) has evolved into a serious public health issue that demands the use of suitable methods to estimate the risk of the disease. As a result, in a sample of individuals who completed a 3-year low-sodium salt or conventional salt intervention in a hypertensive environment, we constructed a 13-year cardiovascular (CV) event risk prediction model with a 10-year follow-up. METHODS A Cox proportional hazards model was used to build a prediction model based on data from 306 participants who matched the inclusion criteria. Both the discriminating power and the calibration of the prediction models were assessed. The discriminative power of the prediction model was measured using the area under the curve (AUC). Brier scores and calibration plots were used to assess the prediction model's calibration. The model was internally validated using the tenfold cross-validation method. The nomogram served as a tool for visualising the model. RESULTS Among the 306 total individuals, there were 100 cases and 206 control. In the model, there were six predictors including age, smoking, LDL-C (low-density lipoprotein cholesterol), baseline SBP (systolic blood pressure), CVD (cardiovascular history), and CNV (genomic copy number variation) nsv483076. The fitted model has an AUC of 0.788, showing strong model discrimination, and a Brier score of 0.166, indicating that it was well-calibrated. According to the results of internal validation, the prediction model utilised in this study had a good level of repeatability. According to the model integrating the interaction of CNVs and baseline blood pressure, the effect of baseline SBP on CV events may be greater when nsv483076 was normal double copies than when nsv483076 was copy number variation. CONCLUSIONS The efficacy of risk prediction models for CV events that include environmental and genetic components is excellent, and they may be utilised as risk assessment tools for CV events in specific groups to offer a foundation for tailored intervention strategies.
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Affiliation(s)
- Dan Zhao
- grid.412636.40000 0004 1757 9485Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning China ,grid.412449.e0000 0000 9678 1884School of Public Health, China Medical University, Shenyang, Liaoning China
| | - Hao Sun
- grid.412636.40000 0004 1757 9485Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning China
| | - Huamin Li
- grid.412636.40000 0004 1757 9485Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning China ,grid.412449.e0000 0000 9678 1884School of Public Health, China Medical University, Shenyang, Liaoning China
| | - Chaoxiu Li
- grid.412636.40000 0004 1757 9485Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning China ,grid.412449.e0000 0000 9678 1884School of Public Health, China Medical University, Shenyang, Liaoning China
| | - Bo Zhou
- Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning, China.
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Moawed SA, El-Aziz AHA. The estimation and interpretation of ordered logit models for assessing the factors connected with the productivity of Holstein-Friesian dairy cows in Egypt. Trop Anim Health Prod 2022; 54:345. [PMID: 36242599 PMCID: PMC9569299 DOI: 10.1007/s11250-022-03329-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 09/14/2022] [Indexed: 12/14/2022]
Abstract
The incorporation of novel technologies such as artificial intelligence, data mining, and advanced statistical methodologies have received wide responses from researchers. This study was designed to model the factors impacting the actual milk yield of Holstein-Friesian cows using the proportional odds ordered logit model (OLM). A total of 8300 lactation records were collected for cows calved between 2005 and 2019. The actual milk yield, the outcome variable, was categorized into three levels: low (< 4500 kg), medium (4500-7500 kg), and high (> 7500 kg). The studied predictor variables were age at first calving (AFC), lactation order (LO), days open (DO), lactation period (LP), peak milk yield (PMY), and dry period (DP). The proportionality assumption of odds using the logit link function was verified for the current datasets. The goodness-of-fit measures revealed the suitability of the ordered logit models to datasets structure. Results showed that cows with younger ages at first calving produce two times higher milk quantities. Also, longer days open were associated with higher milk yield. The highest amount of milk yield was denoted by higher lactation periods (> 250 days). The peak yield per kg was significantly related to the actual yield (P < 0.05). Moreover, shorter dry periods showed about 1.5 times higher milk yield. The greatest yield was observed in the 2nd and 4th parities, with an odds ratio (OR) equal to 1.75, on average. In conclusion, OLM can be used for analyzing dairy cows' data, denoting fruitful information as compared to the other classical regression models. In addition, the current study showed the possibility and applicability of OLM in understanding and analyzing livestock datasets suited for planning effective breeding programs.
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Affiliation(s)
- Sherif A. Moawed
- Department of Animal Wealth Development (Biostatistics Division), Faculty of Veterinary Medicine, Suez Canal University, Ismailia, 41522 Egypt
| | - Ayman H. Abd El-Aziz
- Animal Husbandry and Animal Wealth Development Department, Faculty of Veterinary Medicine, Damanhour University, Damanhour, 22511 Egypt
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Hirosako S, Nakamura K, Hamada S, Sugahara K, Yoshida C, Saeki S, Kojima K, Okamoto S, Ichiyasu H, Fujii K, Kohrogi H. Respiratory evaluation of the risk for postoperative pulmonary complications in patients who preoperatively consulted pulmonologists: Studying both patients who underwent and who precluded planned surgery. Respir Investig 2018; 56:448-456. [PMID: 30146353 DOI: 10.1016/j.resinv.2018.07.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 06/27/2018] [Accepted: 07/12/2018] [Indexed: 10/28/2022]
Abstract
BACKGROUND Due to advances in medicine, patients with pulmonary diseases have become candidates for surgery under general anesthesia. They often consult pulmonologists to assess their tolerability for surgery. The purpose of this study was to evaluate the significant characteristics responsible for postoperative pulmonary complications (PPCs) and the preclusion of the planned surgery. METHODS The clinical data of 462 consecutive patients who consulted at the Department of Respiratory Medicine before surgery under general anesthesia were used in this study. The relationship between the patient׳s characteristics and their outcomes were analyzed. The patients who were scheduled for lung resection were excluded. RESULTS Of the 386 patients who underwent planned surgery, 353 had no PPCs (Group A) and 33 developed PPCs (Group B). Planned surgery under general anesthesia was precluded in 31 patients due to respiratory problems (Group C). The significant predictors for PPCs consisted of a higher age, male gender, asthma, gastrointestinal surgery, cardiovascular surgery and a lower percentage of the predicted forced expiratory volume in 1 second (% predicted FEV1). The significant factors associated with the preclusion of planned surgery included interstitial pneumonia (IP), dermatologic surgery and a lower % predicted FEV1. The predicted probability of PPCs in Group C was significantly higher than that in Group A and lower than that in Group B (all p-values < 0.05). CONCLUSION The common clinical finding for predicting PPCs and encouraging the preclusion of the planned surgery under general anesthesia was a lower % predicted FEV1.
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Affiliation(s)
- Susumu Hirosako
- Department of Respiratory Medicine, Kumamoto University Hospital, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Kazuyoshi Nakamura
- Department of Respiratory Medicine, Kumamoto University Hospital, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Shohei Hamada
- Department of Respiratory Medicine, Kumamoto University Hospital, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Kazuaki Sugahara
- Department of Respiratory Medicine, Kumamoto University Hospital, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Chieko Yoshida
- Department of Respiratory Medicine, Kumamoto University Hospital, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Sho Saeki
- Department of Respiratory Medicine, Kumamoto University Hospital, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Keisuke Kojima
- Department of Respiratory Medicine, Kumamoto University Hospital, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Shinichiro Okamoto
- Department of Respiratory Medicine, Kumamoto University Hospital, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Hidenori Ichiyasu
- Department of Respiratory Medicine, Kumamoto University Hospital, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Kazuhiko Fujii
- Department of Respiratory Medicine, Kumamoto University Hospital, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
| | - Hirotsugu Kohrogi
- Department of Respiratory Medicine, Kumamoto University Hospital, Faculty of Life Sciences, Kumamoto University, 1-1-1, Honjo, Chuo-ku, Kumamoto 860-8556, Japan.
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