Potentially inappropriate prescribing in elderly: a comparison of Beers and STOPP criteria in tertiary care

Background: Prescribing in elderly is a challenging task as they have age related physiological changes, various co-morbidities, altered pharmacological properties and higher propensity for adverse events. They are often prescribed medications which are potentially inappropriate for them, sometimes may even be unnecessary. The medicines are considered as inappropriate if the risk associated with them outweighs benefits. The objective of this study is to assess the prevalence of potentially inappropriate medications (PIM) at a tertiary care teaching hospital according to the Beers updated 2015 criteria and STOPP criteria and to compare the two criteria in detection of PIMs.Methods: A prospective observational study involving 228 elderly patients (>65years) of medicine wards was conducted from October 2015 to March 2016. Relevant information was recorded in a predesigned proforma. The use of potentially inappropriate medications is assessed using Beers updated 2015 criteria and STOPP criteria using descriptive statistics.Results: The prevalence of PIM use in the sample was 26.31% according to the 2015 Beers criteria and 14.03% using the STOPP criteria. The most prevalent PIM according to the Beers criteria were sliding scale insulin (17.54%) and long acting benzodiazepines (5.26%); according to the STOPP criteria, they were aspirin in heart failure (5.26%) and chlorpheniramine (3.07%).Conclusions: The prevalence of PIM varied when different criteria were applied. The 2015 Beers criteria identified more PIM than the STOPP criteria

Potential drug-drug interactions among elderly in-patients with cardiac illness at a tertiary care centre

Background: Drug interactions are major cause of concern in hospitalized patients with cardiac illness especially in elderly population. Therefore, the study was conducted to determine the prevalence and pattern of potential drug-drug interactions (pDDI) and risk factors, if any.Methods: It was a prospective observational study involving 75 elderly in-patients with cardiac diseases. IHEC approval was taken before commencement of study and written informed consent was taken from all the study participants. Data was collected using structured data collection tool. pDDI were analyzed using MEDSCAPE databse. Data was analyzed using SPSS 20.0 in terms of descriptive statistics. Pearson correlation coefficient was used to find the association between the risk factors and potential DDIs. P value of ≤0.05 was considered statistically significant.Results: The prevalence of pDDI was found to be 100%. Total 593 pDDI and 33 interacting drug pairs were observed in the study. The common drug interacting pairs were aspirin and furosemide 140 (23.61%), followed by aspirin+ enalapril 98 (16.53%) and heparin and clopidogrel 56 (9.44%). Majority of pDDI 480 (81%) were found to be of moderate severity. A significant association was documented between length of hospital stay (p=0.041) and occurrence of pDDI. A statistically significant correlation (r =0.621; p<0.01) was noted between number of drugs prescribed and total number of pDDIs.Conclusions: A high prevalence of pDDI was observed. The prevalence rate is directly related to number of drugs prescribed and length of hospital stay. Therefore, close monitoring of hospitalized patients is recommended

Review of code blue system and audit

Background: Code Blue systems are communication systems that ensure the most rapid and effective resuscitation of a patient in respiratory or cardiac arrest. Code blue was established in Bharati Hospital and Research Centre in Sept 2011 in order to reduce morbidity and mortality in wards. The aim of the study was to evaluate the current code blue system and suggest possible interventions to strengthen the system.Methods: It was retrospective observational descriptive study. The study population included all consecutive patients above the age of 18 years for whom code blue had been activated. Data was collected using code blue audit forms. The data was analysed using SPSS (Statistical Package for social sciences) software.Results: A total of 260 calls were made using the blue code system between September 2011 to December 2012. The most common place for blue code activation was casualty. The wards were next, followed by dialysis unit and OPD. The indications for code blue team activation were cardio-respiratory arrest (CRA) (88 patients, 33.84%), change in mental status (52 patients, 20%), road traffic accidents RTA (21, 8.07%), convulsions (29 patients 11.15%), chest pain (19 patients, 8.46%), breathlessness (18 patients,6.92%) and worry of staff about the patient (17 patients, 6.53%), presyncope (10 patients, 3.84%), and others (6 patients, 2.30%). The average response time was 1.58±0.96 minutes in our study. Survival rate was more in medical emergency group 46.15% than in CRA group 31.61%. Initial success rate was 35.2% and a final success rate was 34.6%.Conclusions: Establishment of code blue team in the hospital enabled us to provide timely resuscitation for patients who had “out of ICU” CRA. Further study is needed to establish the overall effectiveness and the optimal implementation of code blue teams. The increasing use of an existing service to review patients meeting blue code criteria requires repeated education and a periodic assessment of site-specific obstacles to utilization

Drug utilization study of antimicrobial agents in patients of neonatal sepsis in neonatal intensive care unit at a tertiary care hospital in western part of India

Background: Neonatal sepsis is one of the major causes of death and morbidity among neonates in India; however, studies related to neonatal sepsis are somewhat in limited numbers. Furthermore, time to time sensitivity and efficacy of various antimicrobial agents (AMA) change which necessitates studies related to antimicrobial drug utilization in hospitals. The objective of present study was to evaluate the pattern of use of AMAs in neonatal sepsis at the neonatal intensive care unit (NICU) at a tertiary care hospital in western part of India.Methods: It was a prospective cross-sectional study conducted over a period of 6-month duration in NICU at tertiary care hospital. Data were collected and analyzed.Results: It has been observed that 57.67% patients were pre-term, 42.32% full term; 23.28% were of normal birth weight, 58.73% low birth weight and 15.34% were very low birth weight. In 48.7% of patients, two different antibiotics were prescribed while in 40.1% of patients three different antibiotics were prescribed. A total number of antibiotics prescribed were 499, per patient 2.78 antibiotics were prescribed. Amikacin was used in 73.01% cases while cefotaxime was used in 64.55% of cases. Piperacilin + tazobactam combination was used in 41.26% cases. In 50.9% cases, antibiotics were prescribed by generic name.Conclusion: Antibiotic resistance is increasing due to the irrational prescribing habits of physicians, leading to increasing morbidity, mortality and treatment costs. Therefore, the medical professionals as well as government personnel who are related to the health sector need to understand that antibiotics are precious and finite resources. The remedy of this situation requires that regular educational awareness programs should be conducted in hospitals at a regular basis

The Effect of Rural-to-Urban Migration on Obesity and Diabetes in India: A Cross-Sectional Study

Shah Ebrahim and colleagues examine the distribution of obesity, diabetes, and other cardiovascular risk factors among urban migrant factory workers in India, together with their rural siblings. The investigators identify patterns of change of cardiovascular risk factors associated with urban migration

A review of hydro-meteorological hazard, vulnerability, and risk assessment frameworks and indicators in the context of nature-based solutions

Nature-based solutions (NBS) are increasingly being implemented as suitable approaches for reducing vulnerability and risk of social-ecological systems (SES) to hydro-meteorological hazards. Understanding vulnerability and risk of SES is crucial in order to design and implement NBS projects appropriately. A systematic literature review was carried out to examine the suitability of, or gaps in, existing frameworks for vulnerability and risk assessment of SES to hydro-meteorological hazards. The review confirms that very few frameworks have been developed in the context of NBS. Most of the frameworks have emphasised social systems over ecological systems. Furthermore, they have not explicitly considered the temporal dimension of risk reduction measures. The study proposes an indicator-based vulnerability and risk assessment framework in the context of NBS (VR-NBS) that addresses both the above limitations and considers established NBS principles. The framework aims to allow for a better consideration of the multiple benefits afforded by NBS and which impact all the dimensions of risk. A list of 135 indicators is identified through literature review and surveys in NBS project sites. This list is composed of indicators representing the social sub-system (61% of total indicators) and the ecological sub-system (39% of total indicators). The list will act as a reference indicator library in the context of NBS projects and will be regularly updated as lessons are learnt. While the proposed VR-NBS framework is developed considering hydro-meteorological hazards and NBS, it can be adapted for other natural hazards and different types of risk reduction measures

A lightweight magnetically shielded room with active shielding

Magnetically shielded rooms (MSRs) use multiple layers of materials such as MuMetal to screen external magnetic fields that would otherwise interfere with high precision magnetic field measurements such as magnetoencephalography (MEG). Optically pumped magnetometers (OPMs) have enabled the development of wearable MEG systems which have the potential to provide a motion tolerant functional brain imaging system with high spatiotemporal resolution. Despite significant promise, OPMs impose stringent magnetic shielding requirements, operating around a zero magnetic field resonance within a dynamic range of ± 5 nT. MSRs developed for OPM-MEG must therefore effectively shield external sources and provide a low remnant magnetic field inside the enclosure. Existing MSRs optimised for OPM-MEG are expensive, heavy, and difficult to site. Electromagnetic coils are used to further cancel the remnant field inside the MSR enabling participant movements during OPM-MEG, but present coil systems are challenging to engineer and occupy space in the MSR limiting participant movements and negatively impacting patient experience. Here we present a lightweight MSR design (30% reduction in weight and 40–60% reduction in external dimensions compared to a standard OPM-optimised MSR) which takes significant steps towards addressing these barriers. We also designed a ‘window coil’ active shielding system, featuring a series of simple rectangular coils placed directly onto the walls of the MSR. By mapping the remnant magnetic field inside the MSR, and the magnetic field produced by the coils, we can identify optimal coil currents and cancel the remnant magnetic field over the central cubic metre to just |B|= 670 ± 160 pT. These advances reduce the cost, installation time and siting restrictions of MSRs which will be essential for the widespread deployment of OPM-MEG

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14