Improving Hospital Oxygen Systems for COVID-19 in Low-Resource Settings: Lessons From the Field.
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ABSTRACT:
SUBMITTER: Graham HR
PROVIDER: S-EPMC7784072 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
Improving Hospital Oxygen Systems for COVID-19 in Low-Resource Settings: Lessons From the Field.
Graham Hamish R HR Bagayana Sheillah M SM Bakare Ayobami A AA Olayo Bernard O BO Peterson Stefan S SS Duke Trevor T Falade Adegoke G AG
Global health, science and practice 20201223 4
Oxygen therapy is an essential medicine and core component of effective hospital systems. However, many hospitals in low- and middle-income countries lack reliable oxygen access-a deficiency highlighted and exacerbated by the coronavirus disease (COVID-19) pandemic. Oxygen access can be challenged by equipment that is low quality and poorly maintained, lack of clinical and technical training and protocols, and deficiencies in local infrastructure and policy environment. We share learnings from 2 ...[more]
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Project description:BACKGROUND:Globally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings. METHODS AND FINDINGS:This study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters commonly available at delivery to correctly categorize births as either preterm (<37 weeks) or term, compared to GA assigned by early ultrasound as the gold standard. Trained midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual period (LMP), and birth weight were used individually to assign GA at delivery and categorize each birth as either preterm or term. Additionally, machine learning techniques incorporated combinations of these measures with several maternal and newborn characteristics associated with prematurity and SGA to develop GA at delivery and preterm birth prediction models. The distribution and accuracy of all models were compared to early ultrasound dating. Within our live-born cohort to date (n = 862), the median GA at delivery by early ultrasound was 39.4 weeks (IQR: 38.3-40.3). Among assessed newborns with complete data included in this analysis (n = 468), the median GA by ultrasound was 39.6 weeks (IQR: 38.4-40.3). Using machine learning, we identified a combination of six accessible parameters (LMP, birth weight, twin delivery, maternal height, hypertension in labor, and HIV serostatus) that can be used by machine learning to outperform current GA prediction methods. For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796. CONCLUSIONS:We identified a parsimonious list of variables that can be used by machine learning approaches to improve accuracy of preterm newborn identification. Our best-performing model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors associated with SGA. These variables are all easily collected at delivery, reducing the skill and time required by the frontline health worker to assess GA. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT02738892.
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Project description:BackgroundAlthough hypoxic patients attending low-resource hospitals have a high mortality, many are not given supplemental oxygen. If oximetry is not available, then the decision to provide oxygen must be based on other factors.MethodsThe variables associated with the decision to provide supplemental oxygen made by an emergency department staff, without access to oximetry, in a low resource Ugandan hospital were determined from data collected within 16 h of admission to the hospital's medical and surgical wards.ResultsOf 2,599 patients, 731 (28.1%) had an oxygen saturation <95%, and 164 (6.3%) an oxygen saturation <90%. Of the 731 patients with oxygen levels below 95% 573 (83%) were not given oxygen; oxygen was only given to 63 (38%) of the 164 patients with oxygen saturation <90%. On average, a patient given oxygen was more likely to die than one not given oxygen, regardless of their oxygen saturation (odds ratio 13.4, 95%CI 9.1-19.6). After multivariate analysis weakness, dyspnoea, low oxygen saturation, high heart rate, high respiratory rate, low temperature, alertness, gait, and a medical illness were all significantly associated with the use of supplemental oxygen and in-hospital mortality. Logistic regression modelling of these variables had comparable discrimination for both oxygen use (c statistic 0.88 SE 0.02) and in-hospital mortality (c statistic 0.84 SE 0.02).ConclusionThe intuitive decision to provide oxygen was strongly associated with in-hospital mortality, suggesting that oxygen was given to those considered the sickest patients. In the future, oximetry may guide oxygen therapy more efficiently.
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Project description:BACKGROUND:To develop effective and sustainable simulation training programs in low-resource settings, it is critical that facilitators are thoroughly trained in debriefing, a critical component of simulation learning. However, large knowledge gaps exist regarding the best way to train and evaluate debrief facilitators in low-resource settings. METHODS:Using a mixed methods approach, this study explored the feasibility of evaluating the debriefing skills of nurse mentors in Bihar, India. Videos of obstetric and neonatal post-simulation debriefs were assessed using two known tools: the Center for Advanced Pediatric and Perinatal Education (CAPE) tool and Debriefing Assessment for Simulation in Healthcare (DASH). Video data was used to evaluate interrater reliability and changes in debriefing performance over time. Additionally, twenty semi-structured interviews with nurse mentors explored perceived barriers and enablers of debriefing in Bihar. RESULTS:A total of 73 debriefing videos, averaging 18?min each, were analyzed by two raters. The CAPE tool demonstrated higher interrater reliability than the DASH; 13 of 16 CAPE indicators and two of six DASH indicators were judged reliable (ICC >?0.6 or kappa >?0.40). All indicators remained stable or improved over time. The number of 'instructors questions,' the amount of 'trainee responses,' and the ability to 'organize the debrief' improved significantly over time (p?<?0.01, p?<?0.01, p?=?0.04). Barriers included fear of making mistakes, time constraints, and technical challenges. Enablers included creating a safe learning environment, using contextually appropriate debriefing strategies, and team building. Overall, nurse mentors believed that debriefing was a vital aspect of simulation-based training. CONCLUSION:Simulation debriefing and evaluation was feasible among nurse mentors in Bihar. Results demonstrated that the CAPE demonstrated higher interrater reliability than the DASH and that nurse mentors were able to maintain or improve their debriefing skills overtime. Further, debriefing was considered to be critical to the success of the simulation training. However, fear of making mistakes and logistical challenges must be addressed to maximize learning. Teamwork, adaptability, and building a safe learning environment enhanced the quality enhanced the quality of simulation-based training, which could ultimately help to improve maternal and neonatal health outcomes in Bihar.
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Project description: Not available
| S-EPMC7333981 | biostudies-literature
Project description:RationaleThe spread of severe acute respiratory syndrome coronavirus-2 has suspended many non-COVID-19 related research activities. Where restarting research activities is permitted, investigators need to evaluate the risks and benefits of resuming data collection and adapt procedures to minimize risk.ObjectivesIn the context of the multicountry Household Air Pollution Intervention (HAPIN) trial conducted in rural, low-resource settings, we developed a framework to assess the risk of each trial activity and to guide protective measures. Our goal is to maximize the integrity of reseach aims while minimizing infection risk based on the latest scientific understanding of the virus.MethodsWe drew on a combination of expert consultations, risk assessment frameworks, institutional guidance and literature to develop our framework. We then systematically graded clinical, behavioral, laboratory and field environmental health research activities in four countries for both adult and child subjects using this framework. National and local government recommendations provided the minimum safety guidelines for our work.ResultsOur framework assesses risk based on staff proximity to the participant, exposure time between staff and participants, and potential viral aerosolization while performing the activity. For each activity, one of four risk levels, from minimal to unacceptable, is assigned and guidance on protective measures is provided. Those activities that can potentially aerosolize the virus are deemed the highest risk.ConclusionsBy applying a systematic, procedure-specific approach to risk assessment for each trial activity, we were able to protect our participants and research team and to uphold our ability to deliver on the research commitments we have made to our staff, participants, local communities, and funders. This framework can be tailored to other research studies conducted in similar settings during the current pandemic, as well as potential future outbreaks with similar transmission dynamics. The trial is registered with clinicaltrials.gov NCT02944682 on October 26. 2016 .
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Project description:BackgroundIn many low- and middle-income countries, where vaccinations will be delayed and healthcare systems are underdeveloped, the COVID-19 pandemic will continue for the foreseeable future. Mortality scales can aid frontline providers in low-resource settings (LRS) in identifying those at greatest risk of death so that limited resources can be directed towards those in greatest need and unnecessary loss of life is prevented. While many prognostication tools have been developed for, or applied to, COVID-19 patients, no tools to date have been purpose-designed for, and validated in, LRS.ObjectivesThis study aimed to develop a pragmatic tool to assist LRS frontline providers in evaluating in-hospital mortality risk using only easy-to-obtain demographic and clinical inputs.MethodsMachine learning was used on data from a retrospective cohort of Sudanese COVID-19 patients at two government referral hospitals to derive contextually appropriate mortality indices for COVID-19, which were then assessed by C-indices.FindingsData from 467 patients were used to derive two versions of the AFEM COVID-19 Mortality Scale (AFEM-CMS), which evaluates in-hospital mortality risk using demographic and clinical inputs that are readily obtainable in hospital receiving areas. Both versions of the tool include age, sex, number of comorbidities, Glasgow Coma Scale, respiratory rate, and systolic blood pressure; in settings with pulse oximetry, oxygen saturation is included and in settings without access, heart rate is included. The AFEM-CMS showed good discrimination: the model including pulse oximetry had a C-statistic of 0.775 (95% CI: 0.737-0.813) and the model excluding it had a C-statistic of 0.719 (95% CI: 0.678-0.760).ConclusionsIn the face of an enduring pandemic in many LRS, the AFEM-CMS serves as a practical solution to aid frontline providers in effectively allocating healthcare resources. The tool's generalisability is likely narrow outside of similar extremely LRS settings, and further validation studies are essential prior to broader use.
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Project description:ObjectivesTo assess acceptability of teleconsultation among the socioeconomically marginalized sections of patients with rheumatic and musculoskeletal diseases (RMDs), to identify the socioeconomic barriers in continuing rheumatology care during the COVID-19 crisis and to identify patients who could benefit by shifting to tele-rheumatology consultations.MethodsThis was a cross sectional analytical study done at a tertiary care teaching hospital in India including patients with RMDs who were not on biological diseases modifying agents. Assessment of disease status, socioeconomic status and economic impact of COVID-19 was done via tele-consultation.ResultsOut of the 680 patients satisfying inclusion criteria, 373 completed the study. The format was found easy by 334 (89.6%) of them and 284 (76.1%) considered tele-rheumatology better than in-person consultation. During the pre-COVID months, the median monthly per capita income of the families of our patients and cost of illness was Indian rupees (INR) 2000 (US$ 26) and INR 1685 (US$ 21.91), respectively. Families whose financial needs were met (OR?=?0.38, 95% CI: 0.239, 0.598) or those with schooling upto at least secondary school (OR?=?0.442, 95% CI: 0.260, 0.752) (P =0.002) were less likely to stop prescription drugs. In a hypothetical model, 289 (77.4%) could be successfully switched to tele-rheumatology follow-up.ConclusionThe acceptability of tele-rheumatology among socioeconomically marginalized patients with RMDs is good. During times of crisis, patients from poorer strata of society and lower educational background are likely to abruptly stop medications. Switching to a telemedicine-based hybrid model is likely to improve drug adherence with substantial savings on loss of pay and out of pocket expenditure.
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