Positive Predictive Accuracy Research Articles

Accuracy of nodal staging by 18F-FDG-PET/CT in limited disease small-cell lung cancer.

Small-cell lung cancer (SCLC) is highly aggressive with a nearly incurable disease in most cases. The most important prognostic factor is the status of the mediastinal lymph nodes. Only a small proportion of patients can be diagnosed at early stages and directed to curative multimodal treatment. Therefore, accuracy of nodal staging by (18F)-Fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) computed tomography (18F-FDG-PET/CT) in (very) limited disease SCLC, although not well investigated, is highly important. Treatment naive, non-bulky patients treated or diagnosed with SCLC between June 2012 and April 2020 with complete data including FDG-PET/CT and invasive mediastinal staging were retrospectively analyzed (n = 19). Sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) and accuracy of mediastinal lymph node staging of 18F-FDG-PET/CT was calculated. The FDG-PET/CT showed a sensitivity of 91%, and the specificity was calculated as 87.5%. In this cohort, the disease prevalence in lymph nodes was 58% (n = 11). Positive predictive value was 91%, NPV 88% and accuracy calculated at 89%. One patient was upstaged from single-level N2 to multilevel N2. In one patient, upstaging in invasive staging was performed from N2 to N3, and one patient was downstaged from N1 to N0. FDG-PET/CT is a valuable tool for the detection of distant metastases, but in mediastinal staging of SCLC some limitations might remain. Invasive methods remain the gold standard. Therefore, the mediastinal lymph nodal status of patients with SCLC screened for multimodal treatment should be further evaluated by additional invasive techniques to verify the exact N-staging and to optimize treatment stratification.

Predicting Role of the Appendicitis Inflammatory Response Score in Diagnosis and Risk Stratification of Acute Appendicitis among Adult Patients

Background: Appendicitis is a global disease. Understanding the global evolution of appendicitis in highly industrialized countries and in newly industrialized countries is necessary for planning healthcare resource utilization. Scoring systems have been designed to aid in the clinical assessment of patients with acute appendicitis. The recently introduced Appendicitis Inflammatory Response (AIR) score was designed to overcome these drawbacks. This simple clinical score can correctly classify the majority of patients with suspected appendicitis. To evaluate the AIR score on patients with suspicion of acute appendicitis and its risk stratification and assess the sensitivity and specificity of the AIR score. Materials and methods: This descriptive of observational study was carried out in the Department of Surgery, Chittagong Medical College Hospital, Chattogram from 21st March to 20th September 2017. Total 100 cases of appendicitis patients were selected after careful history taking, thorough general and local examination and appropriate investigations fulfilling inclusion and exclusion criteria. Results: The present study of 100 selected cases reveals that maximum number of patients (54.0%) were between 15-25 years age group, mean age of appendicitis was 24.5±8.9 years. The sensitivity and Specificity of the Appendicitis Inflammatory Response Score (AIR score) in diagnosis and risk stratification of acute appendicitis was 85.2% and 96.9% respectively. Similarly, the positive predictive value and accuracy rate for the same is 93.5 % and 93.0% respectively. Conclusion: Through associating easily applicable clinical criteria and two simple laboratory tests AIR score help in diagnosis and risk stratification of suspected appendicitis and could guide decision-making to reduce admissions, optimize utility of diagnostic imaging and prevent negative explorations. IAHS Medical Journal Vol 5(2), Dec 2022; 46-51

Percutaneous Magnetic Resonance Imaging-Guided Coaxial Cutting Needle Biopsy of Pancreatic Lesions: Diagnostic Accuracy and Safety.

To appraise the diagnostic performance of magnetic resonance imaging-guided percutaneous coaxial cutting needle biopsy of pancreatic lesions using a 0.4-T open magnetic resonance imaging scanner with optical tracking navigation. This retrospective study included 158 patients who underwent magnetic resonance imaging-guided pancreatic lesion biopsy procedures from May 2019 to December 2020. Two to four specimens were collected from each patient. Pathological diagnosis and clinical follow-ups were conducted to establish the final diagnosis. The procedures were evaluated for sensitivity, specificity, positive and negative predictive values, diagnostic accuracy, and complications. The Cardiovascular and Interventional Radiological Society of Europe guidelines were used to classify complications. Biopsy pathology revealed 139 pancreatic tumor malignancies and 19 benign pancreatic lesions. Finally, 151 patients were diagnosed with pancreatic malignancy and 7 with benign disease confirmed by surgery, re-biopsy, and clinical follow-up. The sensitivity, specificity, positive and negative predictive value, and accuracy for diagnosis of pancreatic diseases were 92.1%, 100%, 100%, 36.8%, and 92.4%, respectively. The biopsy accuracy was significantly related to the size (≤ 2cm, 76.2%; 2-4cm, 94.0%; > 4cm, 96.2%, P = .02) and not the lesion's location (head of pancreas, 90.7%; neck of pancreas, 88.9%; body of pancreas, 94.3%; tail of pancreas, 96.7%, P = .73). Minor complications included two patients experiencing mild abdominal pain and two with a minor occurrence of hemorrhage. Percutaneous magnetic resonance imaging-guided pancreatic lesion biopsy combined with optical navigation has high diagnostic accuracy and is safe for clinical practice. Level of Evidence Level 4, Case-series.

A Predictive Score after Neoadjuvant Chemotherapy for Optimal Cytoreduction at Interval Debulking Surgery in Advanced Epithelial Ovarian Cancer

Background: Ovarian cancer is the seventh most common cancer and eighth most common cause of death of female. More than 75% patients are diagnosed at Stage (III - IV) and their 5-year survival rate is (25-50%) . Primary debulking surgery (PDS) followed by adjuvant chemotherapy is the cornerstone treatment for advanced ovarian cancer. Unfortunately, primary debulking surgery is not always feasible and not associated with optimal cytoreduction. Recently, neoadjuvant chemotherapy followed by Interval Debulking Surgery (NACT- IDS) is increasingly adopted. (NACT-IDS) improves optimal cytoreduction and reduces complications in comparison with PDS . However, a significant proportion of patients cannot be optimally cytoreduced even after NACT-IDS and causes futile laparotomy. So, it is necessary to develop a Predictive Score for Cytoreduction (PSC) after NACT for optimal cytoreduction at (IDS). Objective: To find out a predictive score after NACT for optimal cytoreduction at IDS in advanced epithelial ovarian cancer. Method: This was a prospective observational study conducted among 55 patients of advanced ovarian cancer to develop a predictive score after NACT at IDS in department of Gynecological Oncology of BSMMU, from January 2020 to December 2020. Result: Among the 55 patients with advanced epithelial ovarian cancer 44(80%) could be optimally cytoreduced whereas in 11(20%) suboptimal cytoreduction occurred. The sensitivity, specificity, Negative predictive value (NPV), Positive predictive value (PPV) and accuracy of CA-125 for prediction of optimal cytoreduction was 87.5%, 30.8%,85.7%,34.1% and 47.3% respectively. It was observed that 37 (84.1%) have peritoneal cancer index within 0-16 in optimal cytoreduction (R0) and 3 (27.3%) in non-R0 (p value 0.001). The sensitivity, specificity, NPV, PPV and accuracy of Peritoneal Cancer Index (PCI) for prediction of optimal cytoreduction was 62.5%, 89.7%, 85.4% 74.1% and 81.8% respectively. PSC after NACT for optimal cytoreduction at (IDS) was 3 and it indicates 83.3% Patients could be optimally cytoreduced limiting the rate of suboptimal cytoreduction in 16.7%. Conclusion: The result of the present study showed that PSC after NACT influences Optimal cytoreduction (R <1cm) at (IDS). So, this study concluded that IDS after NACT should be performed in patients with a PSC up to 2 to avoid suboptimal cytoreduction. Bangladesh Medical Res Counc Bull 2022; 48: 154-159

Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording

Fetal movement (FM) is an important indicator of fetal health. However, the current methods of FM detection are unsuitable for ambulatory or long-term observation. This paper proposes a non-contact method for monitoring FM. We recorded abdominal videos from pregnant women and then detected the maternal abdominal region within each frame. FM signals were acquired by optical flow color-coding, ensemble empirical mode decomposition, energy ratio, and correlation analysis. FM spikes, indicating the occurrence of FMs, were recognized using the differential threshold method. FM parameters including number, interval, duration, and percentage were calculated, and good agreement was found with the manual labeling performed by the professionals, achieving true detection rate, positive predictive value, sensitivity, accuracy, and F1_score of 95.75%, 95.26%, 95.75%, 91.40%, and 95.50%, respectively. The changes in FM parameters with gestational week were consistent with pregnancy progress. In general, this study provides a novel contactless FM monitoring technology for use at home.

Diagnostic Accuracy of Admission C Reactive Proteins in Predicting Severity of Acute Pancreatitis Keeping CT Scan as Gold Standard

Introduction: Acute pancreatitis is common, with an alarming rise in the global incidence. The clinical course of AP is highly variable, ranging from mild, self-limiting symptoms to multiple organ dysfunction and/or death. Objective: To evaluate the diagnostic accuracy of admission C reactive proteins in predicting the severe acute pancreatitis keeping computed tomography as gold standard. Material and methods: This Cross-sectional study was conducted at Department of Gastroenterology, Aga khan hospital, Karachi from 19-06-2019 till 19-06-2020. Data was prospectively collected from patients after taking a verbal consent. 181 patients who met the diagnostic criteria were included. Quantitative data was presented as simple descriptive statistics giving mean and standard deviation and qualitative variables was presented as frequency and percentages. Sensitivity, specificity, positive and negative predictive values and diagnostic accuracy was calculated. Results: A total of 181 patients who met the inclusion and exclusion criteria were included in this study. Mean age, duration of symptoms, BMI, height, weight and CRP in our study was 49.48±10.36 years, 14.88±4.87 hours, 29.8±4.74 kg/m2, 146.5±10.41 cm, 78.9±8.58 kg and 110.9±28.54 mg/L. Sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of CRP for diagnosis of severe acute pancreatitis by taking CT scan as gold standard was found to be 88.8%, 81.5%, 94.7%, 65.9% and 87.2%. Conclusion: CRP it is an inexpensive alternative to the conventional severity assessment methods for the prediction of severity and outcome of patients with acute pancreatitis and can serve as an early indicator of the progression of acute pancreatitis into a serious state. Keywords: Severe acute pancreatitis, C reactive protein and CT scan.

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CVr) and herd-out (CVh). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R2 value from 5% to 75% for CVr and 2% to 139% for CVh, whereas the stacking ensemble increased the R2 value from 4% to 70% for CVr and 4% to 150% for CVh. Considering the best model, with the CVr scenario, good prediction accuracies were obtained for glucose (R2 = 0.81), urea (R2 = 0.73), albumin (R2 = 0.75), total reactive oxygen metabolites (R2 = 0.79), total thiol groups (R2 = 0.76), ceruloplasmin (R2 = 0.74), total proteins (R2 = 0.81), globulins (R2 = 0.87), and Na (R2 = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.

Evaluation of Mannheim's Peritoneal index score (MPI) for predicting mortality in patients with peritonitis.

Introduction: Despite latest development the diagnosis and prognosis of acute intraperitoneal infection is still offer a challenge. Different scoring algorithms have been formulated to grade the severity and decision making.Objective: To document reliability of Mannheim Peritonitis Index as a criterion for prediction of mortality after acute intraperitoneal infectionMethodology: This cross-sectional study was conducted at department of Surgery; Civil Hospital Karachi/Dow University of Health Sciences during December 2019 to June 2020. Total 200 patients aged 16 to 55 years from either gender, presenting with peritonitis were enrolled. Patients were evaluated using Mannheim Peritonitis Index (MPI) score for mortality taking real mortality within 7 days as gold standard.Results: The mean age was 37.3 ±9 years with male (71%) dominance, while mean MPI score was 27.12 ±7.1. The sensitivity was 91.8 %, specificity was 84.4 %, while positive predictive and negative predictive values and diagnostic accuracy values were 87.8 %, 89.4 % and 93% respectively.Conclusion: Several scoring systems are utilized to evaluate and stratify patients in overcrowded emergency department which is crucially important for selection of patients who need early clinical decisions. MPI scoring index is the specific and simply accessible in such condition to predict the mortality.Keywords: Mannheim Peritonitis Index, acute intraperitoneal infection, In-hospital mortality, Diagnostic accuracy.

Validade relativa e reprodutibilidade de indicadores da OMS para avaliação da alimentação de crianças menores de dois anos

The study aimed to evaluate relative validity and reproducibility of seven WHO indicators of dietary practices in children aged 6-23.9 months. Data from probabilistic sample of children who used primary healthcare services in Rio de Janeiro, Brazil were collected using a 24h dietary recall (24HR) and a closed questionnaire (Q1) on feeding in the day before the study. The last one was reapplied (Q2) around 16 days later. Validity was assessed by comparing the prevalence rates estimated by 24HR and Q1 and calculating the positive (PPV) and negative (NPV) predictive values, sensitivity (Se), specificity (Sp), and accuracy index (AI) for the resulting indicators. For reproducibility, estimated prevalence rates based on Q1 and Q2 were compared and the kappa index and prevalence-adjusted bias-adjusted kappa were estimated. Of the seven estimated indicators, the prevalence of two was overestimated (Continued breastfeeding: 50.0% vs 40.0%; Sweet beverage consumption: 65.1% vs 52.7%) and the prevalence of one was underestimated (Zero vegetable or fruit consumption: 6.5% vs 18.1%). For most indicators, Se and PPV were higher than Sp and NPV. The prevalence rates determined with Q1 and Q2 were similar for 6 indicators. More than half showed good, very good or excellent agreement.

AN OBSERVATIONAL CLINICAL STUDY OF ASSOCIATION OF LINTULA SCORE IN SUSPECTED CASE OF ACUTE APPENDICITIS IN TERTIARY CARE HOSPITAL

Acute appendicitis is the most common general surgical emergency. About 8% of people have appendicitis at some time during their life. (1) Early surgical intervention improves outcomes. (1) Lintula scoring system is based solely on clinical examination. This might improve the clinical outcomes in adults with suspected appendicitis. A study was conducted on 50 patients suspected of having acute appendicitis were admitted in surgery department. The variables of the Lintula score were recorded and the sum of the score for each patient was calculated. Patients were treated according to standard protocol decided by consultant. The result of the score will be compared to intraoperative diagnosis using chi square test.After comparing positive and negative appendicitis with positive and negative Lintula scores, sensitivity and specificity of Lintula scoring system in diagnosis of acute appendicitis were found to be 55% and 90% respectively. The positive predictive value and diagnostic accuracy of the test has been found to be 95.65% and 80% respectively. (p value <0.05).

A machine learning model for identifying systemic lupus erythematosus through laboratory information system and electronic medical record.

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Its diagnosis poses significant challenges especially at early stages and in atypical cases. The aim of this study was to develop a machine learning model based on common laboratory tests that can aid SLE diagnosis. A standard protocol was developed to collect data of SLE and control immune diseases. A 10-fold cross-validation was performed in the modeling dataset (n=862), and an external dataset (n=198) was used for model validation. Machine learning algorithms were applied to construct a diagnostic model. Performance was evaluated based on area under the curve (AUC) values, F1-score, negative predictive value, positive predictive value, accuracy, sensitivity, and specificity. The optimal model was based on a random forest algorithm with 10 clinical features. Thrombin time, prothrombin activity, and uric acid contributed most to the diagnostic model. The SLE diagnostic model showed sufficient predictive accuracy, with AUC values of 0.8286 in the validation dataset. Our diagnostic model based on 10 common laboratory tests identified the patients with SLE with high accuracy. An online version of the model can potentially be applied in clinical settings for the differential diagnosis of SLE.

PET-PCR reveals low parasitaemia and submicroscopic malarial infections in Honduran Moskitia

BackgroundMalaria remains a main parasitic disease of humans. Although the largest number of cases is reported in the African region, there are still endemic foci in the Americas. Central America reported 36,000 malaria cases in 2020, which represents 5.5% of cases in the Americas and 0.015% of cases globally. Most malaria infections in Central America are reported in La Moskitia, shared by Honduras and Nicaragua. In the Honduran Moskitia, less than 800 cases were registered in 2020, considering it an area of low endemicity. In low endemicity settings, the number of submicroscopic and asymptomatic infections tends to increase, leaving many cases undetected and untreated. These reservoirs challenge national malaria elimination programmes. This study aimed to assess the diagnostic performance of Light Microscopy (LM), a nested PCR test and a photoinduced electron transfer polymerase chain reaction (PET-PCR) in a population of febrile patients from La Moskitia.MethodsA total of 309 febrile participants were recruited using a passive surveillance approach at the Puerto Lempira hospital. Blood samples were analysed by LM, nested PCR, and PET-PCR. Diagnostic performance including sensitivity, specificity, negative and positive predictive values, kappa index, accuracy, and ROC analysis was evaluated. The parasitaemia of the positive samples was quantified by both LM and PET-PCR.ResultsThe overall prevalence of malaria was 19.1% by LM, 27.8% by nPCR, and 31.1% by PET-PCR. The sensitivity of LM was 67.4% compared to nPCR, and the sensitivity of LM and nPCR was 59.6% and 80.8%, respectively, compared to PET-PCR. LM showed a kappa index of 0.67, with a moderate level of agreement. Forty positive cases by PET-PCR were not detected by LM.ConclusionsThis study demonstrated that LM is unable to detect parasitaemia at low levels and that there is a high degree of submicroscopic infections in the Honduran Moskitia.

Diagnostic Accuracy of NIHSS Score in Predicting in-Hospital Mortality in Patients of Ischemic Stroke taking Actual in-Hospital Mortality as Gold Standard

Abstract: Objective: To determine the diagnostic accuracy of NIHSS score for predicting in-hospital mortality among patients of ischemic stroke taking in-hospital mortality as gold standard. Material & Methods: The present cross-sectional study was carried out from May 2020 to November 2020 at Medicine Department, Services Institute of Medical Sciences Lahore. A total of 142 patients aged 30-70 years of both sex with ischemic stroke patients as per-operational definition of duration ?24 hours were enrolled. Patients of hemorrhagic stroke, cardiac disease and CRF were excluded. After taking informed consent, NIHSS score was evaluated in each patient and in hospital mortality will be noted. All patients were followed by the researcher herself and in hospital mortality was noted. Results: Mean age was 53.5+9.2 years with74 (52.11%) females. Mean disease duration was 6.8+1.7 hours and mean NIHSS score was 24.3+7.1. NIHSS score was evaluated in each patient and found 75 True Positive while 06 False Positive. In the 61 NIHSS score negative patients, in-hospital mortality was seen in 06 (False Negative) while 55 (True Negative) did not have in-hospital mortality (p-value <0.001). The Specificity, Sensitivity, Negative Predictive Value (NPV), Positive Predictive Value (PPV) and diagnostic accuracy of NIHSS score to predict in-hospital mortality among patients of ischemic stroke taking in-hospital mortality as gold standard was 92.59%, 90.16%, 92.59%, 90.16% and 91.55% respectively. Conclusion: Diagnostic accuracy of NIHSS score in predicting in-hospital mortality in ischemic stroke patients is quite high.

DEPROMP Trial: the additive value of PSMA-PET/CT-guided biopsy for prostate cancer management in biopsy naïve men—study protocol for a randomized trial

BackgroundThe primary objective is to determine the proportion of men with suspected prostate cancer (PCA) in whom the management plans are changed by additive gallium-68 prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA-PET/CT) guided prostate biopsy (PET-TB) in combination with standard of care (SOC) using systematic (SB) and multiparametric magnetic resonance imaging-guided biopsy (MR-TB) compared with SOC alone. The major secondary objectives are to determine the additive value of the combined approach of SB + MR-TB + PET-TB (PET/MR-TB) for detecting clinically significant PCA (csPCA) compared to SOC; to determine sensitivity, specificity, positive and negative predictive value and diagnostic accuracy of imaging techniques, respective imaging classification systems, and each biopsy method; and to compare preoperatively defined tumor burden and biomarker expression and pathological tumor extent in prostate specimens.MethodsThe DEPROMP study is a prospective, open-label, interventional investigator-initiated trial. Risk stratification and management plans after PET/MR-TB are conducted randomized and blinded by different evaluation teams of experienced urologists based on histopathological analysis and imaging information: one including all results of the PET/MR-TB and one excluding the additional information gained by PSMA-PET/CT guided biopsy. The power calculation was centered on pilot data, and we will recruit up to 230 biopsy-naïve men who will undergo PET/MR-TB for suspected PCA. Conduct and reporting of MRI and PSMA-PET/CT will be performed in a blinded fashion.DiscussionThe DEPROMP Trial will be the first to evaluate the clinically relevant effects of the use of PSMA-PET/CT in patients with suspected PCA compared to current SOC. The study will provide prospective data to determine the diagnostic yields of additional PET-TB in men with suspected PCA and the impact on treatment plans in terms of intra- and intermodal changes. The results will allow a comparative analysis of risk stratification by each biopsy method, including a performance analysis of the corresponding rating systems. This will reveal potential intermethod and pre- and postoperative discordances of tumor stage and grading, providing the opportunity to critically assess the need for multiple biopsies.Trial registrationGerman Clinical Study Register DRKS 00024134. Registered on 26 January 2021.

Diagnostic value of PET with different radiotracers and MRI for recurrent glioma: a Bayesian network meta-analysis

ObjectiveThe purpose of this study was to evaluate the diagnostic accuracy of 6 different imaging modalities for differentiating glioma recurrence from postradiotherapy changes by performing a network meta-analysis (NMA) using...

Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study.

Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports. This study aims to develop and validate a rule-based NLP algorithm for near real-time automatic extraction of AAA diagnosis from radiology reports for case identification. The AAA-NLP algorithm was developed and deployed to an EHR big data infrastructure for near real-time processing of radiology reports from May 1, 2019, to September 2020. NLP extracted named entities for AAA case identification and classified subjects as cases and controls. The reference standard to assess algorithm performance was a manual review of processed radiology reports by trained physicians following standardized criteria. Reviewers were blinded to the diagnosis of each subject. The AAA-NLP algorithm was refined in 3 successive iterations. For each iteration, the AAA-NLP algorithm was modified based on performance compared to the reference standard. A total of 360 reports were reviewed, of which 120 radiology reports were randomly selected for each iteration. At each iteration, the AAA-NLP algorithm performance improved. The algorithm identified AAA cases in near real-time with high positive predictive value (0.98), sensitivity (0.95), specificity (0.98), F1 score (0.97), and accuracy (0.97). Implementation of NLP for accurate identification of AAA cases from radiology reports with high performance in near real time is feasible. This NLP technique will support automated input for patient care and clinical decision support tools for the management of patients with AAA. .

Strain-level bacterial typing directly from patient samples using optical DNA mapping

BackgroundIdentification of pathogens is crucial to efficiently treat and prevent bacterial infections. However, existing diagnostic techniques are slow or have a too low resolution for well-informed clinical decisions.MethodsIn this study, we have developed an optical DNA mapping-based method for strain-level bacterial typing and simultaneous plasmid characterisation. For the typing, different taxonomical resolutions were examined and cultivated pure Escherichia coli and Klebsiella pneumoniae samples were used for parameter optimization. Finally, the method was applied to mixed bacterial samples and uncultured urine samples from patients with urinary tract infections.ResultsWe demonstrate that optical DNA mapping of single DNA molecules can identify Escherichia coli and Klebsiella pneumoniae at the strain level directly from patient samples. At a taxonomic resolution corresponding to E. coli sequence type 131 and K. pneumoniae clonal complex 258 forming distinct groups, the average true positive prediction rates are 94% and 89%, respectively. The single-molecule aspect of the method enables us to identify multiple E. coli strains in polymicrobial samples. Furthermore, by targeting plasmid-borne antibiotic resistance genes with Cas9 restriction, we simultaneously identify the strain or subtype and characterize the corresponding plasmids.ConclusionThe optical DNA mapping method is accurate and directly applicable to polymicrobial and clinical samples without cultivation. Hence, it has the potential to rapidly provide comprehensive diagnostics information, thereby optimizing early antibiotic treatment and opening up for future precision medicine management.

Prediction of esophageal varices in patients with hepatitis B-associated liver cirrhosis by non-invasive markers

AimsTo assess the non-invasive markers for diagnosing and grading esophageal varices(EV) in hepatitis B-associated liver cirrhosis by non-invasive method. MethodsA total of 209 treatment-naive patients with hepatitis B-associated liver cirrhosis were included in this study. These patients were divided into no EV, large grade EV and any grade EV groups according to electronic gastroscope (EGD) results. Blood tests, physical examination, and abdominal ultrasound scan (USS) were measured. The non-invasive indexes for predicting esophageal varices were screened, and non conditional logistic regression model was established. Performance was expressed as sensitivity, specificity, positive, and negative predictive values (PPV, NPV), accuracy, and area under the curve (AUC). ResultsIn the 209 patients, 155 (74.2%) EV and 106(50.7%) large EV were found. There were significant differences in spleen-length (SL), portal vein diameter (PVD), platelet blood cell (PLT) and white blood cell (WBC) between patients with any grade or large and without EV by univariate analysis. The logistic regression model was Y=-2.083-0.011∗PLT+0.101∗PVD+0.013∗SL with any grade EV, the prediction formula is Y= ​0.427-0.013∗PLT -0.861∗WBC+0.008∗SL with large EV. ConclusionsSpleen-length, portal vein diameter, platelet and white blood cell can predict esophageal varices and large esophageal varices in hepatitis B-associated liver cirrhosis.

Detection of cerebral cortical vein thrombosis with high-resolution susceptibility weighted imaging - A comparison with MR venography and standard MR sequences.

Comparison of the performance of high-resolution susceptibility weighted imaging with standard MR sequences and MR venography to identify cortical vein clots. A retrospective review of 51 consecutive cases of cerebral venous thrombosis and 27 controls was performed with independent analysis of all MR sequences. Reference standard was obtained with consensus in a separate session by reviewing all MR sequences together. Cortical vein clots were observed in 30 cases including 9 males and 21 females in the age range of 1 month to 70 years (Mean 34.9 ± 20.2 years). Sensitivity, specificity, negative predictive value, positive predictive value and accuracy of susceptibility weighted imaging for the identification of cortical vein clots were 0.93, 1.0, 1.0, 0.96 and 0.97 respectively. For all other sequences, sensitivity ranged from 0.06 to 0.39 and accuracy from 0.60 to 0.73. Combination of all sequences yielded a value of 1.0 for sensitivity, specificity, positive predictive value, negative predictive value and accuracy for the detection of cortical vein clots. Significant result for area under the receiver operating curve was observed only for SWI with a value of 0.91 (p - .000). Susceptibility weighted imaging demonstrates the best sensitivity and accuracy amongstandardMR sequences including MR venography for the detection of early stage cortical vein clots. However, it needs to be interpreted in combination with other MR sequences for the most accurate evaluation of cortical vein clots.

Role of different imaging modalities and FNAC correlation in diagnosing thyroid gland pathologies

Background: Thyroid nodule, goiter, thyroiditis, and thyroid cancer are all common conditions that may have a negative impact on normal thyroid function. Objective: Determine how FNAC correlation and various imaging modalities contribute to the diagnosis of thyroid gland diseases. Patients and Methods: Al-Hussein University Hospital's outpatient departments (OPD) for thyroid problems saw 100 participants for the research. All patients were screened with ultrasound, and based on the results of that exam, some were sent for further radiological testing. Results: Out of the 100 patients included in the study, US was able to classify the thyroid diseases as focal (69 cases) diffuse (30 cases) and others (11 cases). 48 thyroid nodules underwent FNAC and distributed based on Bethesda score as 3 (6.2%) in category I, 30 (62.5%) in category II, 5 (10.4%) in category III, 1 (2%) in category IV, 3 (6.2%) in category V and 6 (12.5%) in category VI; the sensitivity, specificity, positive and negative predictive values, accuracy, and likelihood ratios of US, and pathology were determined. about 22 patients (22 %) underwent a CT scan mainly to evaluate retrosternal extension and for pre-operative staging; 22 patients (22%) underwent a radionuclide scan. Conclusion: Patients with normal thyroid function (euthyroid) should first have an ultrasonography. In the same way, ultrasonography is the first line of diagnosis for a goiter's size and extent. CT without contrast is appropriate if there is any worry about effect on the trachea or if it is necessary to determine how deep the goiter sits.