Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study

IntroductionAcute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF.MethodsIn this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients’ demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection. To test the efficacy of the suggested prediction model, Extreme Gradient Boosting (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were all computed to evaluate the model’s efficacy. Permutation-based analysis and SHAP were used to assess the importance and influence of the model’s incorporated risk factors.ResultsWhite blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p < 0.05). On the other hand, erythrocyte, hemoglobin, basophil, lymphocyte, mean platelet volume (MPV), platelet, hematocrit, mean erythrocyte hemoglobin (MCH), and procalcitonin (PCT) values were found to be significantly lower in AHF patients compared to healthy controls (p < 0.05). When XGBoost was used in conjunction with LASSO to diagnose AHF, the resulting model had an AUC of 87.9%, an F1 score of 87.4%, a Brier score of 0.036, and an F1 score of 87.4%. PDW, age, RDW-SD, and PLT were identified as the most crucial risk factors in differentiating AHF.ConclusionThe results of this study showed that XAI combined with ML could successfully diagnose AHF. SHAP descriptions show that advanced age, low platelet count, high RDW-SD, and PDW are the primary hematological parameters for the diagnosis of AHF

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

A Case of Ectrodactyly, Ectodermal Dysplasia, Cleft Lip and Palate Syndrome Associated with Hydrocephaly

Ectrodactyly, ectodermal dysplasia, cleft lip, and palate syndrome (EEC) is a genetic developmental disorder characterized by ectrodactyly, ectodermal dysplasia and orofacial clefts (cleft lip/ palate). A few cases have been reported in literature. The cardinal components of the syndrome are ectrodactyly and syndactyly of the hands and feet, cleft lip with or without cleft palate, and abnormalities ectodermal structures including skin (i.e. hypopigmented and dry skin, hyperkeratosis, skin atrophy), hair (sparse hair and eye brows), teeth (small, absent or dysplastic teeth), nails (nail dystrophy) and exocrine glands (reduction/ absence of sweat, sebaceous and salivary glands). A multidisciplinary approach for treatment is needed which is co-ordinated by orthopedic, plastic, dental surgeons, ophthalmologist, dermatologists and speech therapists, psychologists. We presented EEC syndrome case with hydrocephaly by the literature. [Cukurova Med J 2013; 38(3.000): 531-535

Highly compressible binder-free sponge supercapacitor electrode based on flower-like NiO/MnO2/CNT

The increasing demand for flexible electronics encourages the innovative and functional designs of electrode materials with high performance and compressibility. In this work, we report a compressible supercapacitor electrode which is prepared by coating electrically active NiO/MnO2/carbon nanotube (CNT) composite onto a sponge. A cube of sugar was used as the template to obtain the sponge through infiltration and cross-linking of polydimethylsiloxane (PDMS). NiO/MnO2/CNT was deposited on the PDMS sponge to generate substantial amount of interface, resulting in a specific capacitance of 23 F/g at 0.1 A /g in a three-electrode system and 1.32 F/g at 0.5 mA in a symmetric supercapacitor. The proposed system exhibits excellent cycling stability with capacitance retention over 10.000 cycles. The strong adhesion of the binary metal oxides and carbon material onto the porous nonconductive sponge enables mechanical stability under compression-release cycles. Our study indicates that this electrode is a promising candidate for applications in flexible electronics. Furthermore, this research might guide the development of flexible, high-performance, and low-cost electrodes, which will be useful in wearable energy storage systems. (C) 2022 Published by Elsevier B.V

Outstanding supercapacitor performance with intertwined flower-like NiO/ MnO2/CNT electrodes

Binary metal oxides have been broadly investigated as a new electrode material for supercapacitor applications owing to their high reversible performance and good conductivity. When compared to a single candidate, the composite's electrochemical performance is considerably improved by the unique mix of pseudo-material and carbon material. Herein, we report a facile and rational synthesis procedure to fabricate a high performance supercapacitor electrode. The prepared NiO/MnO2/ carbon nanotube (CNT) composite has wonderfully stratified flower-like morphology. The positive synergism among components and unique structure has enabled a high specific capacitance of 1320 F/g at 1 A g-1. After 3000 cycles, the supercapacitor maintains more than 90% of its initial capacitance. Moreover, we also successfully prepared a symmetrical supercapacitor which is made up of two pieces of composite electrode separated with a membrane. The findings highlight that NiO/MnO2/CNT composite is highly desirable for future hybrid energy storage applications

High energy density hybrid supercapacitors based on graphitic carbon nitride modified BiFeO3 and biomass-derived activated carbon

One of the major challenges to the commercialization of supercapacitors (SCs) remains the low energy density, especially at high current densities. Electrode materials with a wide operating voltage and high capacitance are promising for addressing this challenge. Bismuth ferrite (BiFeO3) is utilized as a battery-type electrode which exhibits a wide voltage window and high specific capacitance by storing charge through reversible redox reactions. In this study, bismuth ferrite functionalized with graphitic carbon nitride (g-C3N4) is synthesized by hydrothermal and ball milling techniques to form the cathode for hybrid SCs. As anode, biowaste-derived activated carbon is synthesized from orange peels via a sequential carbonization and activation process. The device exhibits a high specific capacitance of 330 F/g, excellent energy density of 244.8 Wh kg−1 and power density of 1.155 kW kg−1 at a current density of 1 A g−1. An innovative pathway has been developed for designing and fabricating asymmetric supercapacitors with high energy density. The assembled device provides a high operating voltage window of 2.4 V, opening up new possibilities for high-voltage high performance energy storage devices

Assessment of Hematological Predictors via Explainable Artificial Intelligence in the Prediction of Acute Myocardial Infarction

Acute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach. The purpose of this research is to implement the principles of explainable artificial intelligence (XAI) in the analysis of hematological predictors for AMI. In this retrospective analysis, 477 (48.6%) patients with AMI and 504 (51.4%) healthy individuals were enrolled and assessed in predicting AMI. Of the patients with AMI, 182 (38%) had an ST-segment elevation MI (STEMI), and 295 (62%) had a non-ST-segment elevation MI (NSTEMI). Demographic and hematological information of the patients was analyzed to determine AMI. The XAI approach combined with machine learning approaches (Extreme Gradient Boosting, XGB; Adaptive Boosting, AB; Light Gradient Boosting Machine, LGBM) was applied for the estimation of AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). The SHAP approach was used to explain the predictions intuitively. After selecting the 10 most important hematological parameters for AMI, the LGBM model achieved 83% and 74% accuracy for prediction of AMI, and distinguishing subgroups of AMI (STEMI and NSTEMI), respectively. SHAP results showed that neutrophil (NEU), white blood cell (WBC), platelet width of distribution (PDW), and basophil (BA) were the most important for AMI prediction. Mean corpuscular volume (MCV), BA, monocytes (MO), and lymphocytes (LY) were the most important hematological parameters that distinguish STEMI from NSTEMI. The proposed model serves as a valuable tool for physicians, facilitating the diagnosis, treatment, and follow-up of patients with AMI and distinguishing subgroups of AMI (STEMI and NSTEMI). Analyzing readily accessible hemogram parameters empowers medical professionals to make informed decisions and provide enhanced care to a wide range of individuals

Effects of carbon nanomaterials and MXene addition on the performance of nitrogen doped MnO2 based supercapacitors

Nitrogen-doped composites have the potential to achieve well electrochemical performance by enabling convenient contact of the electrolyte ions for carbon-based materials. A good combination of metal oxide and carbonaceous material is a critical challenge in the development of composites. Herein, we demonstrate a highly capacitive and superior cycle performance of MnO2 based supercapacitor electrodes. The addition of different forms of carbon nanomaterials (carbon nanotube and graphene) and MXene is particularly studied. MnO2 based composite materials are capable of capacitance retention over 95%, with high specific capacitance compared to pure N-doped MnO2. The highest specific capacitance was achieved with MXene based MnO2 composite, which exhibits 457 Fg(-1), at a current density of 1 A g(-1) with extreme cycling efficiency (102.5%, after 1000 cycles). High conductivity and large surface area are stimulated by the propitious interaction between MnO2 and nanoscale materials, resulting in superior supercapacitor efficiency. This study highlights the possible potential of carbon-based MnO2 composite electrodes which could be useful for future energy storage applications