OBJECTIVE: This paper aimed to describe the development process of the COVID-19 Symptom Monitoring System (CoSMoS), which consists of a self-monitoring, algorithm-based Telegram bot and a teleconsultation system. We describe all the essential steps from the clinical perspective and our technical approach in designing, developing, and integrating the system into clinical practice during the COVID-19 pandemic as well as lessons learned from this development process.
METHODS: CoSMoS was developed in three phases: (1) requirement formation to identify clinical problems and to draft the clinical algorithm, (2) development testing iteration using the agile software development method, and (3) integration into clinical practice to design an effective clinical workflow using repeated simulations and role-playing.
RESULTS: We completed the development of CoSMoS in 19 days. In Phase 1 (ie, requirement formation), we identified three main functions: a daily automated reminder system for patients to self-check their symptoms, a safe patient risk assessment to guide patients in clinical decision making, and an active telemonitoring system with real-time phone consultations. The system architecture of CoSMoS involved five components: Telegram instant messaging, a clinician dashboard, system administration (ie, back end), a database, and development and operations infrastructure. The integration of CoSMoS into clinical practice involved the consideration of COVID-19 infectivity and patient safety.
CONCLUSIONS: This study demonstrated that developing a COVID-19 symptom monitoring system within a short time during a pandemic is feasible using the agile development method. Time factors and communication between the technical and clinical teams were the main challenges in the development process. The development process and lessons learned from this study can guide the future development of digital monitoring systems during the next pandemic, especially in developing countries.
PATIENTS AND METHODS: Sixty-two patients with AML excluding acute promyelocytic leukemia were retrospectively analyzed. Patients in the earlier cohort (n = 36) were treated on the Medical Research Council (MRC) AML12 protocol, whereas those in the recent cohort (n = 26) were treated on the Malaysia-Singapore AML protocol (MASPORE 2006), which differed in terms of risk group stratification, cumulative anthracycline dose, and timing of hematopoietic stem-cell transplantation for high-risk patients.
RESULTS: Significant improvements in 10-year overall survival and event-free survival were observed in patients treated with the recent MASPORE 2006 protocol compared to the earlier MRC AML12 protocol (overall survival: 88.0% ± 6.5% vs 50.1% ± 8.6%, P = .002; event-free survival: 72.1% ± 9.0 vs 50.1% ± 8.6%, P = .045). In univariate analysis, patients in the recent cohort had significantly lower intensive care unit admission rate (11.5% vs 47.2%, P = .005) and numerically lower relapse rate (26.9% vs 50.0%, P = .068) compared to the earlier cohort. Multivariate analysis showed that treatment protocol was the only independent predictive factor for overall survival (hazard ratio = 0.21; 95% confidence interval, 0.06-0.73, P = .014).
CONCLUSION: Outcomes of pediatric AML patients have improved over time. The more recent MASPORE 2006 protocol led to significant improvement in long-term survival rates and reduction in intensive care unit admission rate.
METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks.
RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.
MATERIALS AND METHODS: Snakebite patients were prospectively recruited between 2017 and 2019. All patients were examined with POCUS to locate edema and directly visualize and measure the arterial flow in the compressed artery. The presence of DRAF in the compressed artery is suggestive of ACS development because when compartment space restriction occurs, increased retrograde arterial flow is observed in the artery.
RESULTS: Twenty-seven snakebite patients were analyzed. Seventeen patients (63%) were bitten by Crotalinae snakes, seven (26%) by Colubridae, one (4%) by Elapidae, and two (7%) had unidentified snakebites. All Crotalinae bit patients received antivenom, had subcutaneous edema and lacked DRAF in a POCUS examination series.
DISCUSSION: POCUS facilitates clinical decisions for snakebite envenomation. We also highlighted that the anatomic site of the snakebite is an important factor affecting the prognosis of the wounds. There were limitations of this study, including a small number of patients and no comparison with the generally accepted invasive evaluation for ACS.
CONCLUSIONS: We are unable to state that POCUS is a valid surrogate measurement of ACS from this study but see this as a starting point to develop further research in this area. Further study will be needed to better define the utility of POCUS in patients envenomated by snakes throughout the world.