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Making a science out of preanalytics: An analytical method to determine optimal tissue fixation in real-time.

ABSTRACT: Modern histopathology is built on the cornerstone principle of tissue fixation, however there are currently no analytical methods of detecting fixation and as a result, in clinical practice fixation is highly variable and a persistent source of error. We have previously shown that immersion in cold formalin followed by heated formalin is beneficial for preservation of histomorphology and have combined two-temperature fixation with ultra-sensitive acoustic monitoring technology that can actively detect formalin diffusing into a tissue. Here we expand on our previous work by developing a predictive statistical model to determine when a tissue is properly diffused based on the real-time acoustic signal. We trained the model based on the morphology and characteristic diffusion curves of 30 tonsil cores. To test our model, a set of 87 different tonsil samples were fixed with four different protocols: dynamic fixation according to our predictive algorithm (C/H:Dynamic, N = 18), gold-standard 24 hour room temperature (RT:24hr, N = 24), 6 hours in cold formalin followed by 1 hour in heated formalin (C/H:6+1, N = 21), and 2 hours in cold formalin followed by 1 hour in heated formalin (C/H:2+1, N = 24). Digital pathology analysis revealed that the C/H:Dynamic samples had FOXP3 staining that was spatially uniform and statistically equivalent to RT:24hr and C/H:6+1 fixation protocols. For comparison, the intentionally underfixed C/H:2+1 samples had significantly suppressed FOXP3 staining (p<0.002). Furthermore, our dynamic fixation protocol produced bcl-2 staining concordant with standard fixation techniques. The dynamically fixed samples were on average only submerged in cold formalin for 4.2 hours, representing a significant workflow improvement. We have successfully demonstrated a first-of-its-kind analytical method to assess the quality of fixation in real-time and have confirmed its performance with quantitative analysis of downstream staining. This innovative technology could be used to ensure high-quality and standardized staining as part of an expedited and fully documented preanalytical workflow.

SUBMITTER: Bauer DR

PROVIDER: S-EPMC8516200 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background. Accurate diagnosis of patients' preferences is central to shared decision making. Missing from clinical practice is an approach that links pretreatment preferences and patient-reported outcomes. Objective. We propose a Bayesian collaborative filtering (CF) algorithm that combines pretreatment preferences and patient-reported outcomes to provide treatment recommendations. Design. We present the methodological details of a Bayesian CF algorithm designed to accomplish 3 tasks: 1) eliciting patient preferences using conjoint analysis surveys, 2) clustering patients into preference phenotypes, and 3) making treatment recommendations based on the posttreatment satisfaction of like-minded patients. We conduct a series of simulation studies to test the algorithm and to compare it to a 2-stage approach. Results. The Bayesian CF algorithm and 2-stage approaches performed similarly when there was extensive overlap between preference phenotypes. When the treatment was moderately associated with satisfaction, both methods made accurate recommendations. The kappa estimates measuring agreement between the true and predicted recommendations were 0.70 (95% confidence interval = 0.052-0.88) and 0.73 (0.56-0.90) under the Bayesian CF and 2-stage approaches, respectively. The 2-stage approach failed to converge in settings in which clusters were well separated, whereas the Bayesian CF algorithm produced acceptable results, with kappas of 0.73 (0.56-0.90) and 0.83 (0.69-0.97) for scenarios with moderate and large treatment effects, respectively. Limitations. Our approach assumes that the patient population is composed of distinct preference phenotypes, there is association between treatment and outcomes, and treatment effects vary across phenotypes. Findings are also limited to simulated data. Conclusion. The Bayesian CF algorithm is feasible, provides accurate cluster treatment recommendations, and outperforms 2-stage estimation when clusters are well separated. As such, the approach serves as a roadmap for incorporating predictive analytics into shared decision making.

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Making a science out of preanalytics: An analytical method to determine optimal tissue fixation in real-time.

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