Clinical characteristics of spinning-induced rhabdomyolysis and other causes of rhabdomyolysis: a comparative study.

Introduction: Spinning is an indoor stationary cycling programme that can cause severe rhabdomyolysis. We compared the clinical characteristics of spinning-induced exertional rhabdomyolysis (SER) with other exertional rhabdomyolysis (ER) and non-exertional rhabdomyolysis (NER). Methods: This was a retrospective observational study of adult patients presenting with rhabdomyolysis to an emergency department from August 2018 to August 2019. Patients were classified as SER, ER or NER based on chart review. We compared patient demographics, serum creatine kinase (CK), transaminase and creatinine levels, admission rates, duration of hospitalisation and treatment prescribed. Results: 62 patients were analysed. SER patients were predominantly female (77% vs. 24% vs. 26%, P < 0.01), Chinese (100% vs. 47% vs. 79%, P < 0.01) and younger (mean age 27.7 vs. 34.6 vs. 59.4 years, P < 0.01) than those with ER and NER. The SER group had the highest CK level (20,000 vs. 10,465 vs. 6,007 U/L, P < 0.01) but the lowest mean serum creatinine level (53.5 vs. 80.9 vs. 143.5 µmol/L, P < 0.01) compared to the ER and NER groups. Admission rates were the highest in SER patients (100% vs. 57% vs. 90%, P < 0.01). SER mean inpatient length of stay was longer than ER but shorter than NER patients (4.3 vs. 1.9 vs. 6.0 days, P = 0.02). Conclusion: SER is a unique form of rhabdomyolysis. Predominantly seen in young, healthy women, it often presents with extremely high CK levels. However, the prognosis is good and the rate of complication is low with fluid treatment.

Statistical Process Control Charts for Monitoring Next-Generation Sequencing and Bioinformatics Turnaround in Precision Medicine Initiatives.

PURPOSE: Precision oncology, such as next generation sequencing (NGS) molecular analysis and bioinformatics are used to guide targeted therapies. The laboratory turnaround time (TAT) is a key performance indicator of laboratory performance. This study aims to formally apply statistical process control (SPC) methods such as CUSUM and EWMA to a precision medicine programme to analyze the learning curves of NGS and bioinformatics processes. PATIENTS AND METHODS: Trends in NGS and bioinformatics TAT were analyzed using simple regression models with TAT as the dependent variable and chronologically-ordered case number as the independent variable. The M-estimator "robust" regression and negative binomial regression were chosen to serve as sensitivity analyses to each other. Next, two popular statistical process control (SPC) approaches which are CUSUM and EWMA were utilized and the CUSUM log-likelihood ratio (LLR) charts were also generated. All statistical analyses were done in Stata version 16.0 (StataCorp), and nominal P < 0.05 was considered to be statistically significant. RESULTS: A total of 365 patients underwent successful molecular profiling. Both the robust linear model and negative binomial model showed statistically significant reductions in TAT with accumulating experience. The EWMA and CUSUM charts of overall TAT largely corresponded except that the EWMA chart consistently decreased while the CUSUM analyses indicated improvement only after a nadir at the 82nd case. CUSUM analysis found that the bioinformatics team took a lower number of cases (54 cases) to overcome the learning curve compared to the NGS team (85 cases). CONCLUSION: As NGS and bioinformatics lead precision oncology into the forefront of cancer management, characterizing the TAT of NGS and bioinformatics processes improves the timeliness of data output by potentially spotlighting problems early for rectification, thereby improving care delivery.