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Patient-based real-time quality control (PBRTQC), a laboratory tool for monitoring the performance of the testing process, has gained increasing attention in recent years. It has been questioned for its generalizability among analytes, instruments, laboratories, and hospitals in real-world settings. Our purpose was to build a machine learning, nonlinear regression-adjusted, patient-based real-time quality control (mNL-PBRTQC) with wide application. Using computer simulation, artificial biases were added to patient population data of 10 measurands. An mNL-PBRTQC was created using eight hospital laboratory databases as a training set and validated by three other hospitals' independent patient datasets. Three different Patient-based models were compared on these datasets, the IFCC PBRTQC model, linear regression-adjusted real-time quality control (L-RARTQC), and the mNL-PBRTQC model. Our study showed that in the three independent test data sets, mNL-PBRTQC outperformed the IFCC PBRTQC and L-RARTQC for all measurands and all biases. Using platelets as an example, it was found that for 20 % bias, both positive and negative, the uncertainty of error detection for mNL-PBRTQC was smallest at the median and maximum values. mNL-PBRTQC is a robust machine learning framework, allowing accurate error detection, especially for analytes that demonstrate instability and for detecting small biases. [ABSTRACT FROM AUTHOR]

Objective: Accidental pharyngeal fishbone ingestion is a common complaint in ear, nose, and throat clinics. Approximately two-thirds of the accidentally ingested fishbones can be removed using tongue depressors and indirect laryngoscopy. However, the remaining third is challenging to identify and remove using these methods. These difficult fishbones require identification and removal via more advanced approaches. Video-guided laryngoscope is used to deal with difficult fishbones in our center. This study aimed to explore the risk factors for difficult fishbones. Methods: A prospective study was performed at a teaching hospital on 2080 patients. Univariate and multivariate analyses were performed to identify the risk factors. Results: The common fishbone locations were the tonsils (39.8%; defined as STEP-I), tongue base (37.1%), vallecula (13.3%; STEP-II), and hypopharynx (9.8%; STEP-III). With increasing STEP level, the ratio of difficult fishbones correspondingly increased (Z = 13.919, P <.001), and the proportions were 21.1%, 41.9%, and 70% in STEP-I, II, and III, respectively. In particular, fishbones in STEP-III (vs STEP-I) had a higher risk of difficult fishbones (odds ratio [OR]: 11.573, 95% CI: 7.987-16.769). Complaints of neck pain (yes vs no), foreign body sensation (yes vs no), and shorter length of fishbones always had a lower risk of difficult fishbones (OR: 0.455, 95% CI: 0.367-0.564; OR: 0.284, 95% CI: 0.191-0.422; OR: 0.727, 95% CI: 0.622-0.85). Missing teeth (yes vs no), swallowing behavior after fishbone ingestion (yes vs no), and male patients (vs female) had a higher risk of difficult fishbones (OR: 1.9, 95% CI: 1.47-2.456; OR: 1.631, 95% CI: 1.293-2.059; OR: 1.278, 95% CI: 1.047-1.56). Conclusions: Neck pain, foreign body sensation, fishbone length, patient age and sex, tooth status, and swallowing behavior after fishbone ingestion are independent risk factors for difficult fishbones. [ABSTRACT FROM AUTHOR]

Delta check (DC) is widely used for detecting sample mix-up. Owing to the inadequate error detection and high false-positive rate, the implementation of DC in real-world settings is labor-intensive and rarely capable of absolute detection of sample mix-ups. The aim of the study was to develop a highly accurate DC method based on designed deep learning to detect sample mix-up. A total of 22 routine hematology test items were adopted for the study. The hematology test results, collected from two hospital laboratories, were independently divided into training, validation, and test sets. By selecting six mainstream algorithms, the Deep Belief Network (DBN) was able to learn error-free and artificially (intentionally) mixed sample results. The model's analytical performance was evaluated using training and test sets. The model's clinical validity was evaluated by comparing it with three well-recognized statistical methods. When the accuracy of our model in the training set reached 0.931 at the 22nd epoch, the corresponding accuracy in the validation set was equal to 0.922. The loss values for the training and validation sets showed a similar (change) trend over time. The accuracy in the test set was 0.931 and the area under the receiver operating characteristic curve was 0.977. DBN demonstrated better performance than the three comparator statistical methods. The accuracy of DBN and revised weighted delta check (RwCDI) was 0.931 and 0.909, respectively. DBN performed significantly better than RCV and EDC. Of all test items, the absolute difference of DC yielded higher accuracy than the relative difference for all methods. The findings indicate that input of a group of hematology test items provides more comprehensive information for the accurate detection of sample mix-up by machine learning (ML) when compared with a single test item input method. The DC method based on DBN demonstrated highly effective sample mix-up identification performance in real-world clinical settings. [ABSTRACT FROM AUTHOR]

Objective: Accidental pharyngeal fishbone ingestion is a common complaint in ear, nose, and throat clinics. Approximately two-thirds of the accidentally ingested fishbones can be removed using tongue depressors and indirect laryngoscopy. However, the remaining third is challenging to identify and remove using these methods. These difficult fishbones require identification and removal via more advanced approaches. Video-guided laryngoscope is used to deal with difficult fishbones in our center. This study aimed to explore the risk factors for difficult fishbones., Methods: A prospective study was performed at a teaching hospital on 2080 patients. Univariate and multivariate analyses were performed to identify the risk factors., Results: The common fishbone locations were the tonsils (39.8%; defined as STEP-I), tongue base (37.1%), vallecula (13.3%; STEP-II), and hypopharynx (9.8%; STEP-III). With increasing STEP level, the ratio of difficult fishbones correspondingly increased (Z = 13.919, P < .001), and the proportions were 21.1%, 41.9%, and 70% in STEP-I, II, and III, respectively. In particular, fishbones in STEP-III (vs STEP-I) had a higher risk of difficult fishbones (odds ratio [OR]: 11.573, 95% CI: 7.987-16.769). Complaints of neck pain (yes vs no), foreign body sensation (yes vs no), and shorter length of fishbones always had a lower risk of difficult fishbones (OR: 0.455, 95% CI: 0.367-0.564; OR: 0.284, 95% CI: 0.191-0.422; OR: 0.727, 95% CI: 0.622-0.85). Missing teeth (yes vs no), swallowing behavior after fishbone ingestion (yes vs no), and male patients (vs female) had a higher risk of difficult fishbones (OR: 1.9, 95% CI: 1.47-2.456; OR: 1.631, 95% CI: 1.293-2.059; OR: 1.278, 95% CI: 1.047-1.56)., Conclusions: Neck pain, foreign body sensation, fishbone length, patient age and sex, tooth status, and swallowing behavior after fishbone ingestion are independent risk factors for difficult fishbones., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objectives: Patient-based real-time quality control (PBRTQC), a laboratory tool for monitoring the performance of the testing process, has gained increasing attention in recent years. It has been questioned for its generalizability among analytes, instruments, laboratories, and hospitals in real-world settings. Our purpose was to build a machine learning, nonlinear regression-adjusted, patient-based real-time quality control (mNL-PBRTQC) with wide application., Methods: Using computer simulation, artificial biases were added to patient population data of 10 measurands. An mNL-PBRTQC was created using eight hospital laboratory databases as a training set and validated by three other hospitals' independent patient datasets. Three different Patient-based models were compared on these datasets, the IFCC PBRTQC model, linear regression-adjusted real-time quality control (L-RARTQC), and the mNL-PBRTQC model., Results: Our study showed that in the three independent test data sets, mNL-PBRTQC outperformed the IFCC PBRTQC and L-RARTQC for all measurands and all biases. Using platelets as an example, it was found that for 20 % bias, both positive and negative, the uncertainty of error detection for mNL-PBRTQC was smallest at the median and maximum values., Conclusions: mNL-PBRTQC is a robust machine learning framework, allowing accurate error detection, especially for analytes that demonstrate instability and for detecting small biases., (© 2023 Walter de Gruyter GmbH, Berlin/Boston.)

A diagnosis of multiple myeloma (MM) is difficult to make on the basis of any single laboratory test result. Accurate diagnosis of MM generally results from a number of costly and invasive laboratory tests and medical procedures. The aim of this work is to find a new, highly specific and sensitive method for MM diagnosis. Serum samples were tested in groups representing MM (n = 54) and non-MM (n = 108). These included a subgroup of 17 plasma cell dyscrasias, a subgroup of 17 reactive plasmacytosis, 5 B cell lymphomas, and 7 other tumors with osseus metastasis, as well as 62 healthy donors as controls. Bioinformatic calculations associated with MM were performed. The decision algorithm, with a panel of three biomarkers, correctly identified 24 of 24 (100%) MM samples and 46 of 49 (93.88%) non-MM samples in the training set. During the masked test for the discriminatory model, 26 of 30 MM patients (sensitivity, 86.67%) were precisely recognized, and all 34 normal donors were successfully classified; patients with reactive plasmacytosis were also correctly classified into the non-MM group, and 11 of the other patients were incorrectly classified as MM. The results suggested that proteomic fingerprint technology combining magnetic beads with MALDI-TOF-MS has the potential for identifying individuals with MM. The biomarker classification model was suitable for preliminary assessment of MM and could potentially serve as a useful tool for MM diagnosis and differentiation diagnosis.