Citations

Key Points Mutation patterns in blast phase MPN, including paired sample analysis, point to specific mutations with potential pathogenetic relevance. RUNX1 mutations predict inferior survival in blast phase MPN, independent of specific treatment strategies.

Background: Current laboratory risk management principles emphasize the importance of assessing laboratory quality control (QC) practices in terms of the risk of patient harm. Limited practical guidance or examples on how to do this are available. Methods: The patient risk model described in a published laboratory risk management guideline was combined with a recently reported approach to computing the predicted probability of patient harm to produce a risk management index (RMI) that compares the predicted probability of patient harm for a QC strategy to the acceptable probability of patient harm based on the expected severity of harm caused by an erroneously reported patient result. Results: Measurement procedure capability and quality control performance for two instruments measuring HbA1c in a laboratory were assessed by computing the RMI for each instrument individually and for the laboratory as a whole. Conclusions: This assessment provides a concrete example of how laboratory QC practices can be directly correlated to the risk of patient harm from erroneously reported patient results.

AbstractTo develop a genetic risk model for primary myelodysplastic syndromes (MDS), we queried the prognostic significance of next‐generation sequencing (NGS)‐derived mutations, in the context of the Mayo cytogenetic risk stratification, which includes high‐risk (monosomal karyotype; MK), intermediate‐risk (non‐MK, classified as intermediate/poor/very poor, per the revised international prognostic scoring system; IPSS‐R), and low‐risk (classified as good/very good, per IPSS‐R). Univariate analysis in 300 consecutive patients with primary MDS identified TP53, RUNX1, U2AF1, ASXL1, EZH2, and SRSF2 mutations as “unfavorable” and SF3B1 as “favorable” risk factors for survival; for the purposes of the current study, the absence of SF3B1 mutation was accordingly dubbed as an “adverse” mutation. Analysis adjusted for age and MK, based on our previous observation of significant clustering between MK and TP53 mutations, confirmed independent prognostic contribution from RUNX1, ASXL1, and SF3B1 mutations. Multivariable analysis that included age, the Mayo cytogenetics risk model and the number of adverse mutations resulted in HRs (95% CI) of 5.3 (2.5‐10.3) for presence of three adverse mutations, 2.4 (1.6‐3.7) for presence of two adverse mutations, 1.5 (1.02‐2.2) for presence of one adverse mutation, 5.6 (3.4‐9.1) for high‐risk karyotype, 1.5 (1.1‐2.2) for intermediate‐risk karyotype and 2.4 (1.8‐3.3) for age >70 years; HR‐weighted risk point assignment generated a three‐tiered genetic risk model: high (N = 65; 5‐year survival 2%), intermediate (N = 100; 5‐year survival 18%), and low (N = 135; 5‐year survival 56%). The current study provides a practically simple risk model in MDS that is based on age, karyotype, and mutations only.

Importance: Media reports have questioned the safety of overlapping surgical procedures, and national scrutiny has underscored the necessity of single-center evaluations of its safety; however, sample sizes are likely small. We compared the safety profiles of overlapping and nonoverlapping pediatric procedures at a single children's hospital and discussed methodological considerations of the evaluation. Data and Design: Retrospective analysis of inpatient pediatric surgical procedures (January 2013 to September 2015) at a single pediatric referral center. Overlapping and nonoverlapping procedures were matched in an unbalanced manner (m:n) by procedure. Mixed models adjusting for Vizient-predicted risk, case-mix, and surgeon compared inpatient mortality and length of stay (LOS). Results: Among 315 overlapping procedures, 256 (81.3%) were matched to 645 nonoverlapping procedures. There were 6 deaths in all. The adjusted odds ratio for mortality did not differ significantly between nonoverlapping and overlapping procedures (adjusted odds ratio = 0.94 vs overlapping; 95% CI, 0.02–48.5; P = 0.98). Wide confidence intervals were minimally improved with Bayesian methods (95% CI, 0.07–12.5). Adjusted LOS estimates were not clinically different by overlapping status (0.6% longer for nonoverlapping; 95% CI, 9.7% shorter to 12.2% longer; P = 0.91). Among the 87 overlapping procedures with the greatest overlap (≥60 min or ≥50% of operative duration), there were no deaths. Conclusions: The safety of overlapping and nonoverlapping surgical procedures did not differ at this children's center. These findings may not extrapolate to other centers. LOS or intraoperative measures may be more appropriate than mortality for safety evaluations due to low event rates for mortality.