Machine learning-based nonlinear regression-adjusted real-time quality control modeling: a multi-center study.

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.

A multi-model fusion algorithm as a real-time quality control tool for small shift detection.

BACKGROUND: Patient-based real-time quality control (PBRTQC), a complement to traditional QC, may eliminate matrix effect from QC materials, realize real-time monitoring as well as cut costs. However, the accuracy of PBRTQC has not been satisfactory as physicians expect till now. Our aim is to set up a artificial intelligence-based QC for small error detection in real laboratory settings. Taking tPSA as our unique research subject, data extraction, data stimulation, data partition, model construction and evaluation were designed. METHODS: 84241 deidentified results for tPSA were extracted from Laboratory Information System of Aviation General Hospital. The data set was accumulated by way of data simulation. Independent training and test datasets were separated. After three classification models (RF, SVM and DNN) in ML constructed and weighted by information entropy, a multi-model fusion algorithm was generated. Performance of the fusion model was evaluated by comparing with optimal PBRTQC. RESULTS: For 4 PBRTQC methods, MovSO showed overall better performance for 0.2 µg/L bias and optimal MNPed was equal to 200. For the fusion model, MNPeds were less than 12 for all biases, and ACC surpassed MovSO nearly 100 times. Except for 0.01 µg/L bias, ACC was more than 0.9 for the rest of biases. FPR was apparently lower than MovSO, only 0.2% and 0.1%. CONCLUSION: The fusion model shows outstanding performance and reduces incorrect and omitting error detection, adaptable for the real settings.

A highly accurate delta check method using deep learning for detection of sample mix-up in the clinical laboratory.

OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Risk Factors of Difficult Pharyngeal Accidental Fishbones Ingestion.

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.

Effects of rewarming interventions in hypothermia trauma patients:a network meta-analysis / 中华护理杂志

Objective To evaluate the clinical effects of multiple rewarming interventions in adult hypothermia trauma patients.Methods A systematic search of Cochrane Library,PubMed,EMBASE,Scopus,CINAHL,Chinese Biomedical Literature Database (CBM),Chinese Knowledge Infrastructure (CNKI),VIP and Wan Fang Database was carried out to identify all randomized controlled trials(RCTs) and controlled clinical trials(CCTs) that explored the effects of rewarming interventions in adult hypothermia trauma patients.The quality of the literature was evaluated using JBI 2008 RCT and quasi-experimental study evaluation criteria.Data and network plot were analyzed and drawn by ADDIS 1.16.7 software.Results Totally 6 RCTs and 1 quasi-experimental design were included,involving 10 interventions and 619 patients.There was statistically significant difference in body temperature after rewarming between the warm blankets and the forced-air blankets in all rewarming measures.The results of the top three interventions were carbon-fiber heating blanket(set to 42℃),forced-air blankets,warmed intravenous fluids plus blanket which resulted from the primary outcome indicators.The incidence of chills and cold discomfort decreased with the use of forced-air blankets and chemical heat pad as compared with traditional warm blankets,while the heart rate of the patients who used chemical heating pads and continuous heating of carbon fiber blanket were declined more than those used normal blankets.Conclusion The effects of carbon-fiber heating blanket which set to 42°C was the best method in all rewarming interventions.But this conclusion still requires randomized controlled trials with larger sample size to further verify.

Establishment and validation of serum free light chain reference intervals in an ethnic Chinese population.

BACKGROUND: Quantification of serum free light chains (FLC) and calculation of a kappa/lambda ratio using polyclonal antisera based immunoassays provide laboratories with a sensitive alternative to urine protein electrophoresis (UPE). However, the published 0.26 - 1.65 serum FLC kappa/lambda ratio reference intervals may not be suitable for different ethnic populations (such as the Han Chinese population presented) and require validation. This is particularly important where there are significant differences in ethnicity, and hence HLA prevalence, in the population studied. METHODS: Serum FLC reference intervals were determined using 326 Han Chinese blood donor volunteers. Sensitivities and specificities for the (i) serum FLC kappa/lambda ratio reference interval and (ii) UPE analyses were determined using 68 pre-treatment, serum immunofixation (sIFE) positive multiple myeloma (MM) patient samples, 54 sera from MM patients undergoing treatment, and 56 sIFE-negative samples from patients with no plasma cell dyscrasia (PCD). RESULTS: The 100% range for the serum FLC kappa/lambda ratio in this Han Chinese population was 0.32 - 1.52. Both Han Chinese blood donors and published kappa/lambda ratio reference ranges demonstrated higher diagnostic sensitivity and specificity for PCD than was seen with UPE. Highly abnormal serum FLC kappa/lambda ratios were observed in 68% of MM patients who had a negative UPE. Furthermore, a MM screening panel of SPE plus serum FLC assays achieved 100% diagnostic sensitivity compared to 97% for a UPE plus SPE algorithm. For MM patients undergoing therapy, 70% of UPE negative samples displayed an abnormal FLC ratio. CONCLUSIONS: This study confirms the requirement to verify normal FLC reference ranges in local populations. This Han Chinese reference range is narrower than the published range. FLC testing provides a robust, sensitive, and specific alternative to classic UPE assessment.

Construction of a multiple myeloma diagnostic model by magnetic bead-based MALDI-TOF mass spectrometry of serum and pattern recognition software.

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.