Harmless Acute Pancreatitis Score

Introduction: Acute Pancreatitis is a common disease in our region. It can range from mild to severe disease with high mortality rate. It is critical to identify patients who are at high risk for a severe disease course, since they require close monitoring and immediate aggressive treatment. Aims: To compare the effectiveness of Harmless Acute Pancreatitis Score with Ranson’s scoring system in predicting the severity of Acute Pancreatitis. Methods: A prospective cross sectional study was done among 45 patients who were admitted in surgery department over a period of one year with diagnosis of acute pancreatitis. If haematocrit was less than39% in female and less than43% in male, serum creatinine less than two miligram /deciliter and no sign of peritonitis, it was assigned as Harmless Acute Pancreatitis Score Zero. If at least one parameter was abnormal it was assigned as Harmless Acute Pancreatitis Score +. Severe pancreatitis (poor prognosis) was considered in those who required Intensive Care Unit care, who had in hospital mortality and who had hospitalization of more than five days. Patients with on admission Ranson’s score of more than three were suspected to have severe Pancreatitis. Results: There were total 45 patients, 18 females and 27 males. Twenty four patients were assigned as Harmless Acute Pancreatitis Score zero and 21 patients were assigned as Harmless Acute Pancreatitis Score +. Harmless Acute Pancreatitis Score was able to predict correctly in 18 out of 26 patients who fulfilled the criteria of poor prognosis (p<0.001). Conclusion: Harmless Acute Pancreatitis Score proved to be a better screening tool compared to on admission Ranson’s scoring system to predict the severity of Acute Pancreatitis, which may help predict the prognosis of the patient.

Ranking patients on the kidney transplant waiting list based on fuzzy inference system

Background Kidney transplantation is the best treatment for people with End-Stage Renal Disease (ESRD). Kidney allocation is the most important challenge in kidney transplantation process. In this study, a Fuzzy Inference System (FIS) was developed to rank the patients based on kidney allocation factors. The main objective was to develop an expert system, which would mimic the expert intuitive thinking and decision-making process in the face of the complexity of kidney allocation. Methods In the first stage, kidney allocation factors were identified. Next, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) has been used to weigh them. The purpose of this stage is to develop a point scoring system for kidney allocation. Fuzzy if-then rules were extracted from the United Network for Organ Sharing (UNOS) dataset by constructing the decision tree, in the second stage. Then, a Multi-Input Single-Output (MISO) Mamdani fuzzy inference system was developed for ranking the patients on the waiting list. Results To evaluate the performance of the developed Fuzzy Inference System for Kidney Allocation (FISKA), it was compared with a point scoring system and a filtering system as two common approaches for kidney allocation. The results indicated that FISKA is more acceptable to the experts than the mentioned common methods. Conclusion Given the scarcity of donated kidneys and the importance of optimal use of existing kidneys, FISKA can be very useful for improving kidney allocation systems. Countries that decide to change or improve the kidney allocation system can simply use the proposed model. Furthermore, this model is applicable to other organs, including lung, liver, and heart.

Improving the time and space complexity of the WFA algorithm and generalizing its scoring

Modern genomic sequencing data is trending toward longer sequences with higher accuracy. Many analyses using these data will center on alignments, but classical exact alignment algorithms are infeasible for long sequences. The recently proposed WFA algorithm demonstrated how to perform exact alignment for long, similar sequences in O(sN) time and O(s2) memory, where s is a score that is low for similar sequences (Marco-Sola et al., 2021). However, this algorithm still has infeasible memory requirements for longer sequences. Also, it uses an alternate scoring system that is unfamiliar to many bioinformaticians. We describe variants of WFA that improve its asymptotic memory use from O(s2) to O(s3/2) and its asymptotic run time from O(sN) to O(s2 + N). We expect the reduction in memory use to be particularly impactful, as it makes it practical to perform highly multithreaded megabase-scale exact alignments in common compute environments. In addition, we show how to fold WFA's alternate scoring into the broader literature on alignment scores.

A new functional Hip Scoring System compatible with Asian Lifestyle

doi: https://doi.org/10.12669/pjms.38.3.5758 How to cite this:Junaid J, Bhatti A. A new functional Hip Scoring System compatible with Asian Lifestyle. Pak J Med Sci. 2022;38(3):442-444. doi: https://doi.org/10.12669/pjms.38.3.5758 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Semi-quantitative seismic risk screening tool for existing buildings in Canada

The National Research Council Canada (NRC) recently developed a semi-quantitative seismic risk screening tool (SQST) for existing buildings in Canada. The SQST aims to supersede the Manual for Screening of Buildings for Seismic Investigation developed by NRC in the early 1990s. The SQST consists of three key components: (1) a structural scoring system that quantitatively assesses the structural seismic risk based on probability of collapse; (2) a non-structural component scoring system that qualitatively assesses the seismic risk of non-structural components based on seismic demand; and (3) a ranking procedure that prioritizes potentially hazardous buildings for seismic evaluations and possible upgrading. The SQST intends to inexpensively identify and exempt buildings with acceptable life safety risk and optimize the allocation of resources to assess the seismic risk of portfolios of buildings. Seismic screening with the SQST can be completed with either paper-based screening forms or a web-based application. The applicability of the SQST is demonstrated by conducting a pilot study for 33 existing buildings across Canada.

On the Use of Hemadsorption with CytoSorb in Patients with Septic Shock. Comment on Kogelmann et al. First Evaluation of a New Dynamic Scoring System Intended to Support Prescription of Adjuvant CytoSorb Hemoadsorption Therapy in Patients with Septic Shock. J. Clin. Med. 2021, 10, 2939

With great interest we read the article by Klaus Kogelmann and co-authors on the “First Evaluation of a New Dynamic Scoring System Intended to Support Prescription of Adjuvant CytoSorb Hemoadsorption Therapy in Patients with Septic Shock” [...]

A Simple Scoring Model Based on Machine Learning Predicts Intravenous Immunoglobulin Resistance in Kawasaki Disease

In Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions. To establish a simple and accurate scoring model predicting IVIG resistance, we conducted a retrospective cohort study of 996 KD patients that were diagnosed at 11 facilities for 10 years, in which 108 cases (23.5%) were resistant to initial IVIG treatment. We performed machine learning with random forest model using 30 clinical variables at diagnosis in 796 and 200 cases for training and test datasets, respectively. Random forest model accurately predicted IVIG resistance (AUC; 0.75, sensitivity; 0.54, specificity; 0.80). Next, using top five influential features (days of illness at initial therapy, serum levels of C-reactive protein, sodium, total bilirubin, and total cholesterol) in the random forest model, we designed a simple scoring system. In spite of its simplicity, the scoring system predicted IVIG resistance (AUC; 0.73, sensitivity; 0.55, specificity; 0.83) as accurately as the random forest model itself. Moreover, accuracy of our scoring system with five clinical features was almost identical to that of Gunma score with seven clinical features (AUC; 0.73, sensitivity; 0.53, specificity; 0.83), a well-known logistic regression scoring model, and superior to that of two widely used scores (Kurume score; 0.67, 0.46 and 0.76, respectively, and Osaka score; 0.69, 0.33 and 0.84, respectively). Conclusions: Our simple scoring system based on the findings in machine learning, as well as machine learning itself, seems to be useful to accurately predict IVIG resistance in KD patients.

Comparison Accuracy in mSIRS, NEWS and qSOFA Score to Triage Sepsis Patients in The Emergency Department at a Hospital in Thailand

Objective: We aimed to apply the modified systemic inflammatory response syndrome (mSIRS), the quick sequential organ failure assessment score (qSOFA), and National Early Warning Score (NEWS) to triage suspected sepsis patients. Therefore, knowing the predictive performance of each scoring system, using given cut-points for triaging patients with suspected sepsis, could help predict the progression of sepsis.Material and Methods: This study is a single-center retrospective chart review. The study enrolled patients older than 18 years with suspected sepsis patient at the time they presented at the triage zone. The primary outcome was to determine which scoring system were the most accurate to triage sepsis patients. The secondary outcomes were predictions of mortality related to the scoring.Results: Considering the outcome to be represented by a SOFA score of ≥2, the area under the curve of the receiver operating characteristic curves for the entire range of mSIRS, qSOFA and NEWS were 0.494, 0.669 and 0.751, respectively. Using a cut point for qSOFA of ≥2 provided a low sensitivity of 36.2% and high specificity of 93.0%; whereas, using a cut point for NEWS of >4 provided a high sensitivity of 89.0% and low specificity of 33.0%.Conclusion: In summary, qSOFA is the most accurate scoring system for diagnosis sepsis which was consistent with previous study. However, qSOFA had the lowest sensitivity, so is not appropriate in a triage situation. Therefore, we decided to use NEWS as the triage tool because of its better sensitivity and acceptable specificity as we need to triage almost all possible cases.

An Immune Feature-Based, Three-Gene Scoring System for Prognostic Prediction of Head-and-Neck Squamous Cell Carcinoma

Head-and-neck squamous cell carcinoma (HNSCC) is characterized by a high frequency of neck lymph node metastasis (LNM), a key prognostic factor. Therefore, identifying the biological processes during LNM of HNSCC has significant clinical implications for risk stratification. This study performed Gene Ontology enrichment analysis of differentially expressed genes between tumors with LNM and those without LNM and identified the involvement of immune response in the lymphatic metastasis of HNSCC. We further identified greater infiltrations of CD8+ T cells in tumors than in adjacent normal tissues through immunochemistry in the patient cohort (n = 62), indicating the involvement of CD8+ T cells in the antitumor immunity. Hierarchical clustering analysis was conducted to initially identify the candidate genes relevant to lymphocyte-mediated antitumor response. The candidate genes were applied to construct a LASSO Cox regression analysis model. Three genes were eventually screened out as progression‐related differentially expressed candidates in HNSCC and a risk scoring system was established based on LASSO Cox regression model to predict the outcome in patients with HNSCC. The score was calculated using the formula: 0.0636 × CXCL11 − 0.4619 × CXCR3 + 0.2398 × CCR5. Patients with high scores had significantly worse overall survival than those with low scores (p &lt; 0.001). The risk score showed good performance in characterizing tumor-infiltrating lymphocytes and provided a theoretical basis for stratifying patients receiving immune therapies. Additionally, a nomogram including the risk score, age, and TNM stage was constructed. The prediction model displayed marginally better discrimination ability and higher agreement in predicting the survival of patients with HNSCC compared with the TNM stage.