Wilaiphorn Thinkhamrop's research while affiliated with Khon Kaen University and other places

This study investigated the incidence of work-related diseases and injuries among cultivating farmers in Thailand by conducting a three-year retrospective study with 207,465 farmers from Roi-Et province and 154,478 farmers from Udon Thani province. Health data were collected from the hospital information systems (HIS) of Roi-Et and Udon Thani provinces for a three-year period (2014–2016). We used the ICD-10 classification codes for diseases and injuries to analyse the incidence rates per 100,000 farmers. The number of cases that visited health centres were 53,794 in Udon Thani (34.82%) and 77,438 in Roi-Et (37.32%). The top five morbidity rates in Roi-Et and Udon Thani were work-related musculoskeletal disorders (WMSDs), followed by injuries, infectious disease, skin disease, and lung disease. The incidence of diseases and injuries found in Roi-Et, a more cultivating-agriculture-based province, was higher than that found in Udon Thani province. WMSDs were the greatest health concern in regard to cultivating activities. Infectious and lung diseases might have also been related to the cultivating activities, which warrants further studies. Our findings highlight an urgent need for targeted occupational health services, and health and safety measures among cultivating farmers in both provinces. Regular monitoring and surveillance programmes for cultivating farmers need to be in place, at the provincial level, to mitigate risk and protect the health and well-being of the farmers.

Predicting renal disease progression in patients with diabetes is an important clinical and policy challenge. This study aimed to investigate the survival function of renal disease progression by assessing the doubling of serum creatinine (DSC) over time and the factors associated with DSC in patients with type 2 diabetes mellitus (T2DM). This retrospective cohort study included 3,465 T2DM patients from a tertiary hospital in Thailand. Patients' data from 2011 to 2017 were extracted from the electronic medical records. The Kaplan-Meier method and Cox's proportional hazard model were analyzed to determine the survival time of T2DM patients with DSC and the factors associated with DSC. Results showed that at the end of the follow-up period, 1,028 of 3,465 patients underwent DSC. The incidence rate of DSC was 5.0/1,000 person-months. The median survival time of DSC was 81.5 months (95% confidence interval [CI]: 79.28-83.74). The variables associated with an increased hazard ratio of DSC were high triglyceride level (≥200 mg/dl) (adjusted hazard ratio (aHR): 1.17, 95% CI: 1.01-1.34), hypertension (aHR: 1.33, 95% CI: 1.18-1.51), high glycated hemoglobin level (≥7.0%) (aHR: 1.27, 95% CI: 1.07-1.51), and high total cholesterol level (≥ 240 mg/dl) (aHR: 1.58, 95% CI: 1.32-1.87). Obesity (Body Mass Index (BMI) of ≥ 25.0 kg/m 2) was negatively associated with DSC (aHR: 0.80, 95% CI: 0.71-0.91). In conclusion, T2DM patients under observation had a median survival time of DSC of 6.8 years. Hypertension and uncontrolled blood glucose and lipids are predictors of renal disease progression in T2DM patients.

Clinical predictors of mortality in systemic sclerosis (SSc) are diversely reported due to different healthcare conditions and populations. A simple predictive model for early mortality among patients with SSc is needed as a precise referral tool for general practitioners. We aimed to develop and validate a simple predictive model for predicting mortality among patients with SSc. Prognostic research with a historical cohort study design was conducted between January 1, 2013, and December 31, 2020, in adult SSc patients attending the Scleroderma Clinic at a university hospital in Thailand. The data were extracted from the Scleroderma Registry Database. Early mortality was defined as dying within 5 years after the onset of SSc. Deep learning algorithms with Adam optimizer and different machine learning algorithms (including Logistic Regression, Decision tree, AdaBoost, Random Forest, Gradient Boosting, XGBoost, and Autoencoder neural network) were used to classify SSc mortality. In addition, the model’s performance was evaluated using the area under the receiver operating characteristic curve (auROC) and its 95% confidence interval (CI) and values in the confusion matrix. The predictive model development included 528 SSc patients, 343 (65.0%) were females and 374 (70.8%) had dcSSc. Ninety-five died within 5 years after disease onset. The final 2 models with the highest predictive performance comprise the modified Rodnan skin score (mRSS) and the WHO-FC ≥ II for Model 1 and mRSS and WHO-FC ≥ III for Model 2. Model 1 provided the highest predictive performance, followed by Model 2. After internal validation, the accuracy and auROC were good. The specificity was high in Models 1 and 2 (84.8%, 89.8%, and 98.8% in model 1 vs. 84.8%, 85.6%, and 98.8% in model 2). This simplified machine learning model for predicting early mortality among patients with SSc could guide early referrals to specialists and help rheumatologists with close monitoring and management planning. External validation across multi-SSc clinics should be considered for further study.

Background The prevalence of peritoneal dialysis (PD) in Thailand is increasing rapidly in part because of Thailand’s Peritoneal Dialysis First policy. PD is a home-based renal replacement therapy in which patients with chronic kidney disease perform up to 4 exchanges of dialysate fluid per day in the peritoneal cavity. Overhydration is one of the most common complications in patients on PD and is associated with increased morbidity and mortality. To monitor hydration status, patients collect hydration metrics, including body weight, blood pressure, urine output, and ultrafiltration volume, from each dialysis cycle and enter this information into a PD logbook. This information is reviewed bimonthly at PD clinic appointments. The chronic kidney disease-PD (CKD-PD) app with near-field communication (NFC) and optical character recognition (OCR) was developed to automate hydration metric collection. The information was displayed in the app for self-monitoring and uploaded to a database for real-time monitoring by the PD clinic staff. Early detection and treatment of overhydration could potentially reduce the morbidity and mortality related to overhydration. Objective This study aims to identify usability issues and technology adoption barriers for the CKD-PD app with NFC and OCR and a monitoring system and to use this information to make rapid cycle improvements. Methods A multidisciplinary team of nephrologists, PD clinic nurses, computer programmers, and engineers trained and observed 2 groups of 5 participants in the use of the CKD-PD app with NFC and OCR and a monitoring system. The participants were observed using technology in their homes in 3 phases. The data collected included the Unified Theory of Acceptance and Use of Technology questionnaire, think-aloud observation, user ratings, completion of hydration metrics, and upload of hydration metrics to the central database. These results were used by the team between phases to improve the functionality and usefulness of the app. Results The CKD-PD app with NFC and OCR and a monitoring system underwent 3 rapid improvement cycles. Issues were identified regarding the usability of the NFC and OCR data collection, app stability, user interface, hydration metric calculation, and display. NFC and OCR improved hydration metric capture; however, issues remained with their usability. App stability and user interface issues were corrected, and hydration metrics were successfully uploaded by the end of phase 3. Participants’ scores on technology adoption decreased but were still high, and there was enthusiasm for the self-monitoring and clinical communication features. Conclusions Our rapid cycle process improvement methodology identified and resolved key barriers and usability issues for the CKD-PD app with NFC and OCR and a monitoring system. We believe that this methodology can be accomplished with limited training in data collection, statistical analysis, and funding.

Background Clinical predictors of mortality in systemic sclerosis (SSc) are diversely reported due to different healthcare conditions and populations. A universal and simplified predictive model for SSc mortality is needed so that practitioners can be used for managing their patients appropriately. Objectives We aimed to develop and validate a simple predictive model for predicting mortality among patients with SSc. Methods Prognostic research with a historical cohort study design was conducted between January 1, 2013, and December 31, 2019, in adult SSc patients and attending the Scleroderma Clinic at a university hospital in Thailand. The data were extracted from the Scleroderma Registry Database. A deep learning algorithm with Adam optimizer and different machine learning algorithms (including Decision tree, AdaBoost, Random Forest, Gradient Boosting, and XGBoost) was used to classify SSc mortality. In addition, the model’s performance was evaluated using the area under the receiver operating characteristic curve (auROC) and its 95% confidence interval (CI) and values in the confusion matrix. Results The analysis and predictive model development included 658 SSc patients, 416 (63.2%) females, 452 (69.1%) had dcSSc, and 218 died. The final model included the modified Rodnan skin score (mRSS) and the WHO functional class (WHO-FC) ≥II (model 1). The final model provided the highest predictive performance, followed by model 2 (mRSS and WHO-FC ≥III). After internal validation, the accuracy and auROC were good, and the specificity was high in models 1 and 2 (81.1%, 0.84, and 95.5% in model 1 vs. 82.7%, 0.82, and 87.1% in model 2). Table 1. Generalizability of selected model(s) presented as accuracy, area under ROC, positive predictive value, positive likelihood ratio, specificity, and sensitivity Selected Model Accuracy AUC (95% ) PPV (95% ) +LR (95%CI ) Specificity (95% ) Sensitivity (95% ) Model 1 mRSS and WHO FC ≥ II 81.1 83.6 (77.5 – 89.6) 84.6 (69.5 - 94.1) 11.3 (5.0-25.7) 95.5 (90.4 - 98.3) 51.6 (38.7 - 64.2) Model 2 mRSS and WHO FC ≥ III 82.7 82.4 (75.8 - 88.9) 73.4 (60.9 - 83.7) 5.7 (3.6-9.1 87.1 (87.2 - 92.3) 73.4 (60.9 - 83.7) 95%CI 95% confidence interval, AUC Area Under the receiver operating characteristics (ROC), mRSS modified Rodnan skin score Conclusion This simplified machine learning model for predicting mortality among patients with SSc could guide early referrals to specialists and help rheumatologists with close monitoring and management planning. External validation across multi-SSc clinics should be considered for further study. References [1]Ferri C, Valentini G, Cozzi F, Sebastiani M, Michelassi C, La Montagna G, et al. Systemic sclerosis: demographic, clinical, and serologic features and survival in 1,012 Italian patients. Medicine (Baltimore). 2002;81(2):139–53. [2]Rubio-Rivas M, Royo C, Simeón CP, Corbella X, Fonollosa V. Mortality and survival in systemic sclerosis: systematic review and meta-analysis. Semin Arthritis Rheum. 2014;44(2):208–19. [3]Foocharoen C, Peansukwech U, Mahakkanukrauh A, Suwannaroj S, Pongkulkiat P, Khamphiw P, et al. Clinical characteristics and outcomes of 566 Thais with systemic sclerosis: A cohort study. Int J Rheum Dis. 2020;23(7):945–57. [4]Tyndall AJ, Bannert B, Vonk M, Airò P, Cozzi F, Carreira PE, et al. Causes and risk factors for death in systemic sclerosis: a study from the EULAR Scleroderma Trials and Research (EUSTAR) database. Ann Rheum Dis. 2010;69(10):1809–15. [5]Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49(12):1373–9. [6]Elhai M, Meune C, Boubaya M, Avouac J, Hachulla E, Balbir-Gurman A, et al. Mapping and predicting mortality from systemic sclerosis. Ann Rheum Dis. 2017;76(11):1897–905. [7]Wangkaew S, Prasertwitayakij N, Phrommintikul A, Puntana S, Euathrongchit J. Causes of death, survival and risk factors of mortality in Thai patients with early systemic sclerosis: inception cohort study. Rheumatol Int. 2017;37(12):2087–94. Acknowledgements The authors thank (a) Thailand’s National Science, Research, and Innovation Fund for funding support, (b) the Scleroderma Research Group for research assistance, and (c) Mr. Bryan Roderick Hamman—under the aegis of the Publication Clinic Khon Kaen University, Thailand—for assistance with the English-language presentation. Disclosure of Interests Chingching Foocharoen Speakers bureau: By Boeringer Ingelheim, Bandit Thinkhamrop: None declared, Wilaiphorn Thinkhamrop: None declared, Nathaphop Chaichaya: None declared, Ajanee Mahakkanukrauh Speakers bureau: By Boeringer Ingelheim, Norvatis, Johnson & Johnson, Siraphop Suwannaroj Speakers bureau: By Boeringer Ingelheim, Johnson & Johnson, Norvatis

BACKGROUND The prevalence of peritoneal dialysis (PD) in Thailand is increasing rapidly in part because of Thailand’s Peritoneal Dialysis First policy. PD is a home-based renal replacement therapy in which patients with chronic kidney disease perform up to 4 exchanges of dialysate fluid per day in the peritoneal cavity. Overhydration is one of the most common complications in patients on PD and is associated with increased morbidity and mortality. To monitor hydration status, patients collect hydration metrics, including body weight, blood pressure, urine output, and ultrafiltration volume, from each dialysis cycle and enter this information into a PD logbook. This information is reviewed bimonthly at PD clinic appointments. The chronic kidney disease-PD (CKD-PD) app with near-field communication (NFC) and optical character recognition (OCR) was developed to automate hydration metric collection. The information was displayed in the app for self-monitoring and uploaded to a database for real-time monitoring by the PD clinic staff. Early detection and treatment of overhydration could potentially reduce the morbidity and mortality related to overhydration. OBJECTIVE This study aims to identify usability issues and technology adoption barriers for the CKD-PD app with NFC and OCR and a monitoring system and to use this information to make rapid cycle improvements. METHODS A multidisciplinary team of nephrologists, PD clinic nurses, computer programmers, and engineers trained and observed 2 groups of 5 participants in the use of the CKD-PD app with NFC and OCR and a monitoring system. The participants were observed using technology in their homes in 3 phases. The data collected included the Unified Theory of Acceptance and Use of Technology questionnaire, think-aloud observation, user ratings, completion of hydration metrics, and upload of hydration metrics to the central database. These results were used by the team between phases to improve the functionality and usefulness of the app. RESULTS The CKD-PD app with NFC and OCR and a monitoring system underwent 3 rapid improvement cycles. Issues were identified regarding the usability of the NFC and OCR data collection, app stability, user interface, hydration metric calculation, and display. NFC and OCR improved hydration metric capture; however, issues remained with their usability. App stability and user interface issues were corrected, and hydration metrics were successfully uploaded by the end of phase 3. Participants’ scores on technology adoption decreased but were still high, and there was enthusiasm for the self-monitoring and clinical communication features. CONCLUSIONS Our rapid cycle process improvement methodology identified and resolved key barriers and usability issues for the CKD-PD app with NFC and OCR and a monitoring system. We believe that this methodology can be accomplished with limited training in data collection, statistical analysis, and funding.

Introduction: Musculoskeletal Disorders (MSDs) is one of the leading public health problems in Thailand especially among the working age and elderly population. MSDs is the disease that can be affected by many factors such as working environment, abnormal body structure and age related degenerative conditions. Aim: To explore the prevalence and association between Socioeconomic Status factors (SES) and MSDs in Thai population. Materials and Methods: A cross-sectional study was performed by using the data from the National Socioeconomics Survey conducted by the National Statistical Office (NSO) in 2010 and 2012. A total of 17,040 and 16,905 participants who met the inclusion criteria of this study were recruited in 2010 and 2012 respectively. The required information was collected by face-to-face interview with structured questionnaires. Multilevel mixed-effects logistic regression analysis was performed to determine potential socioeconomic factors associated with MSDs. Results: The prevalence of MSDs in 2010 and 2012 was 3.90% (95%CI: 3.61 to 4.19) and 2.96% (95%CI: 2.70 to 3.21) respectively. The multilevel mixed-effects logistic regression observed that odds of having MSDs were significantly higher among those who had low education attainment (ORadj=1.52: 95% CI: 1.18 to 1.95), were agriculturist (ORadj=1.69; 95% CI: 1.32 to 2.18), aged 40 year old or greater (ORadj=9.10; 95% CI: 7.02 to 11.80) and were female (ORadj=1.22; 95%CI: 1.04 to 1.44). In addition, the factors that were associated with MSDs in 2012 data were respondents residing in the North region (ORadj=2.25; 95% CI: 1.36 to 3.70), were agriculturist (ORadj=1.72; 95% CI: 1.28 to 2.32), had low education attainment (ORadj=1.51; 95% CI: 1.14 to 1.99) and aged 40 year old or greater (ORadj=11.28; 95% CI: 7.96 to 15.98). Conclusion: The study indicated that socioeconomic status was associated with musculoskeletal disorders. Those who were female, agriculturalist and had low educational attainment were vulnerable to MSDs in Thai population.

In the original publication of this article [1], there is an error in the section of ‘Ethics approval and consent to participate’ at the end of the article, the correct Ethics reference number should be HE551404 rather than HE591067.