Diagnostic characteristics of the 20-minute whole blood clotting test in detecting venom-induced consumptive coagulopathy following carpet viper envenoming

Introduction Envenoming by Echis spp. (carpet or saw-scaled vipers) causes haemorrhage and coagulopathy and represents a significant proportion of snakebites in the savannah regions of West Africa. Early diagnosis of envenoming is crucial in the management of these patients and there is limited evidence on the utility of the 20-minute whole blood clotting test (20WBCT) in diagnosing venom-induced consumptive coagulopathy (VICC) following envenoming by Echis ocellatus. Methods A prospective observational cohort study was conducted at the Kaltungo General Hospital in North-eastern Nigeria from September 2019 to September 2021. Standardised 20WBCTs were conducted by trained hospital staff and citrated plasma samples were collected at numerous timepoints. Prothrombin time (PT) and international normalised ratio (INR) were determined using a semi-automated analyser and INR values were calculated using international sensitivity indices (ISI). The sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV), and likelihood ratios of the 20WBCT compared to an INR ≥ 1.4 were calculated, alongside 95% confidence intervals. Results We enrolled 121 patients into our study, with a median age of 26 (18.0–35.0) years and a male predominance (75.2%). The 20WBCT was positive (abnormal) in 101 out of 121 patients at timepoint 0h, of which 95 had an INR ≥ 1.4, giving a sensitivity of 87.2% (95%CI 79.4–92.8). Among patients with a negative 20WBCT (normal), six had an INR < 1.4 giving a specificity of 50% (95%CI 21.1–78.9%). The positive and negative likelihood ratios were 1.7 (95%CI 1.6–1.9) and 0.3 (95%CI 0.1–0.4) respectively. Conclusion The 20WBCT is a simple, cheap, and easily accessible bedside test with a high sensitivity for the detection of patients with venom induced consumptive coagulopathy (VICC) following envenoming by E. ocellatus, although false positives do occur. Repeated 20WBCTs can identify patients with new, persistent, and rebound coagulopathy

Accidental poisoning with aluminum phosphide presenting with excessive cholinergic symptoms with response to atropine: A case report

Accidental poisoning in children, though underreported in our environment, is common and could prove fatal. It is important to identify the primary chemical agent that is responsible for the poisoning. We present a case of accidental ingestion of fish poisoned with aluminum phosphide (AlP) used as rat poisoning by a 14-month-old girl. At presentation, the actual chemical content of the poison was not available and clinical features were suggestive of organophosphate poisoning. She was commenced on atropine together with other treatment, on which she made remarkable improvement. The atropine was continued with complete resolution of symptoms on the third day of admission. We, therefore, report a serendipitous use of atropine in the management of AlP poisoning with successful outcome

Anomalous origin of the left coronary artery from the main pulmonary artery

The anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) is a rare congenital heart anomaly; this occurs in 1/300,000 live births. ALCAPA syndrome was first described in 1933 by Bland and co-authors in autopsy specimens; however, further description of its clinical manifestations resulted in the naming of Bland–White–Garland syndrome. The case of a 2-year-old boy who was referred for echocardiographic investigation due to recurrent cough, catarrh, and occasional noisy breathing is reported in this communication; his chest X-ray was normal, while electrocardiogram showed Q-waves on limb leads I and aVL and the echocardiographic study showed ALCAPA

Quality of sleep among clinical medical students of Bayero university, Kano, Nigeria

Background: Sleep deprivation and disruptions are associated with both psychological and physiological disorders. Medical students have an increased risk for sleep deprivation. This study aimed to determine the quality of sleep among clinical medical students of Bayero University Kano (BUK). Materials and Methods: This is a cross-sectional study that was conducted between February and March 2019 among clinical students of BUK. The data were obtained using sociodemographic questionnaire as well as the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) self-administered questionnaires. Two hundred questionnaires were administered to the students in their 4th, 5th, and 6th academic year. SPSS version 20 was used for the statistical analysis of the data collected. Results: A total of 181 questionnaires were completed out of the 200 that were administered, making a response rate of 90.5%. There were 114 males (63%) and 67 females (37%), giving a male-to-female ratio of 1.7:1, which is in keeping with the gender distribution of the students. The mean age (± standard deviation) of the students was 23.8 ± 2.9 years. The overall poor sleep quality was found to be 53.0% based on the PSQI, whereas the overall excessive daytime sleepiness was found to be 38.3% using the ESS. Poor sleep quality was found to be 61.1%, 51.7%, and 42.9% among the students in the 4th, 5th, and 6th academic years, respectively (P = 0.14). Excessive daytime sleepiness was found to be 45.8%, 41.7%, and 22.9% among the students in the 4th, 5th, and 6th academic years, respectively (P = 0.03). Conclusion: The prevalence of poor sleep quality is high among the clinical students of BUK

Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery

Artificial intelligence (AI) and wearable sensors are gradually transforming healthcare service delivery from the traditional hospital-centred model to the personal-portable-device-centred model. Studies have revealed that this transformation can provide an intelligent framework with automated solutions for clinicians to assess patients’ general health. Often, electronic systems are used to record numerous clinical records from patients. Vital sign data, which are critical clinical records are important traditional bioindicators for assessing a patient’s general physical health status and the degree of derangement happening from the baseline of the patient. The vital signs include blood pressure, body temperature, respiratory rate, and heart pulse rate. Knowing vital signs is the first critical step for any clinical evaluation, they also give clues to possible diseases and show progress towards illness recovery or deterioration. Techniques in machine learning (ML), a subfield of artificial intelligence (AI), have recently demonstrated an ability to improve analytical procedures when applied to clinical records and provide better evidence supporting clinical decisions. This literature review focuses on how researchers are exploring several benefits of embracing AI techniques and wearable sensors in tasks related to modernizing and optimizing healthcare data analyses. Likewise, challenges concerning issues associated with the use of ML and sensors in healthcare data analyses are also discussed. This review consequently highlights open research gaps and opportunities found in the literature for future studies

Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery

Artificial intelligence (AI) and wearable sensors are gradually transforming healthcare service delivery from the traditional hospital-centred model to the personal-portable-device-centred model. Studies have revealed that this transformation can provide an intelligent framework with automated solutions for clinicians to assess patients&rsquo; general health. Often, electronic systems are used to record numerous clinical records from patients. Vital sign data, which are critical clinical records are important traditional bioindicators for assessing a patient&rsquo;s general physical health status and the degree of derangement happening from the baseline of the patient. The vital signs include blood pressure, body temperature, respiratory rate, and heart pulse rate. Knowing vital signs is the first critical step for any clinical evaluation, they also give clues to possible diseases and show progress towards illness recovery or deterioration. Techniques in machine learning (ML), a subfield of artificial intelligence (AI), have recently demonstrated an ability to improve analytical procedures when applied to clinical records and provide better evidence supporting clinical decisions. This literature review focuses on how researchers are exploring several benefits of embracing AI techniques and wearable sensors in tasks related to modernizing and optimizing healthcare data analyses. Likewise, challenges concerning issues associated with the use of ML and sensors in healthcare data analyses are also discussed. This review consequently highlights open research gaps and opportunities found in the literature for future studies

Artificial Intelligence, Sensors and Vital Health Signs: A Review

Large amounts of patient vital/physiological signs data are usually acquired in hospitals manually via centralized smart devices. The vital signs data are occasionally stored in spreadsheets and may not be part of the clinical cloud record; thus, it is very challenging for doctors to integrate and analyze the data. One possible remedy to overcome these limitations is the interconnection of medical devices through the internet using an intelligent and distributed platform such as the Internet of Things (IoT) or the Internet of Health Things (IoHT) and Artificial Intelligence/Machine Learning (AI/ML). These concepts permit the integration of data from different sources to enhance the diagnosis/prognosis of the patient’s health state. Over the last several decades, the growth of information technology (IT), such as the IoT/IoHT and AI, has grown quickly as a new study topic in many academic and business disciplines, notably in healthcare. Recent advancements in healthcare delivery have allowed more people to have access to high-quality care and improve their overall health. This research reports recent advances in AI and IoT in monitoring vital health signs. It investigates current research on AI and the IoT, as well as key enabling technologies, notably AI and sensors-enabled applications and successful deployments. This study also examines the essential issues that are frequently faced in AI and IoT-assisted vital health signs monitoring, as well as the special concerns that must be addressed to enhance these systems in healthcare, and it proposes potential future research directions

Global variation in postoperative mortality and complications after cancer surgery: a multicentre, prospective cohort study in 82 countries

© 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licenseBackground: 80% of individuals with cancer will require a surgical procedure, yet little comparative data exist on early outcomes in low-income and middle-income countries (LMICs). We compared postoperative outcomes in breast, colorectal, and gastric cancer surgery in hospitals worldwide, focusing on the effect of disease stage and complications on postoperative mortality. Methods: This was a multicentre, international prospective cohort study of consecutive adult patients undergoing surgery for primary breast, colorectal, or gastric cancer requiring a skin incision done under general or neuraxial anaesthesia. The primary outcome was death or major complication within 30 days of surgery. Multilevel logistic regression determined relationships within three-level nested models of patients within hospitals and countries. Hospital-level infrastructure effects were explored with three-way mediation analyses. This study was registered with ClinicalTrials.gov, NCT03471494. Findings: Between April 1, 2018, and Jan 31, 2019, we enrolled 15 958 patients from 428 hospitals in 82 countries (high income 9106 patients, 31 countries; upper-middle income 2721 patients, 23 countries; or lower-middle income 4131 patients, 28 countries). Patients in LMICs presented with more advanced disease compared with patients in high-income countries. 30-day mortality was higher for gastric cancer in low-income or lower-middle-income countries (adjusted odds ratio 3·72, 95% CI 1·70–8·16) and for colorectal cancer in low-income or lower-middle-income countries (4·59, 2·39–8·80) and upper-middle-income countries (2·06, 1·11–3·83). No difference in 30-day mortality was seen in breast cancer. The proportion of patients who died after a major complication was greatest in low-income or lower-middle-income countries (6·15, 3·26–11·59) and upper-middle-income countries (3·89, 2·08–7·29). Postoperative death after complications was partly explained by patient factors (60%) and partly by hospital or country (40%). The absence of consistently available postoperative care facilities was associated with seven to 10 more deaths per 100 major complications in LMICs. Cancer stage alone explained little of the early variation in mortality or postoperative complications. Interpretation: Higher levels of mortality after cancer surgery in LMICs was not fully explained by later presentation of disease. The capacity to rescue patients from surgical complications is a tangible opportunity for meaningful intervention. Early death after cancer surgery might be reduced by policies focusing on strengthening perioperative care systems to detect and intervene in common complications. Funding: National Institute for Health Research Global Health Research Unit

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

© 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide. Methods: A multimethods analysis was performed as part of the GlobalSurg 3 study—a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital. Findings: Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3·85 [95% CI 2·58–5·75]; p<0·0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63·0% vs 82·7%; OR 0·35 [0·23–0·53]; p<0·0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer. Interpretation: Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised. Funding: National Institute for Health and Care Research