Clinical features and outcomes of COVID-19 admissions in a population with a high prevalence of HIV and tuberculosis: a multicentre cohort study

Background There is still a paucity of evidence on the outcomes of coronavirus disease 2019 (COVID-19) among people living with human immunodeficiency virus (PWH) and those co-infected with tuberculosis (TB), particularly in areas where these conditions are common. We describe the clinical features, laboratory findings and outcome of hospitalised PWH and human immunodeficiency virus (HIV)-uninfected COVID-19 patients as well as those co-infected with tuberculosis (TB). Methods We conducted a multicentre cohort study across three hospitals in Cape Town, South Africa. All adults requiring hospitalisation with confirmed COVID-19 pneumonia from March to July 2020 were analysed. Results PWH comprised 270 (19%) of 1434 admissions. There were 47 patients with active tuberculosis (3.3%), of whom 29 (62%) were PWH. Three-hundred and seventy-three patients (26%) died. The mortality in PWH (n = 71, 26%) and HIV-uninfected patients (n = 296, 25%) was comparable. In patients with TB, PWH had a higher mortality than HIV-uninfected patients (n = 11, 38% vs n = 3, 20%; p = 0.001). In multivariable survival analysis a higher risk of death was associated with older age (Adjusted Hazard Ratio (AHR) 1.03 95%CI 1.02–1.03, p < 0.001), male sex (AHR1.38 (95%CI 1.12–1.72, p = 0.003) and being “overweight or obese” (AHR 1.30 95%CI 1.03–1.61 p = 0.024). HIV (AHR 1.28 95%CI 0.95–1.72, p 0.11) and active TB (AHR 1.50 95%CI 0.84–2.67, p = 0.17) were not independently associated with increased risk of COVID-19 death. Risk factors for inpatient mortality in PWH included CD4 cell count < 200 cells/mm3, higher admission oxygen requirements, absolute white cell counts, neutrophil/lymphocyte ratios, C-reactive protein, and creatinine levels. Conclusion In a population with high prevalence of HIV and TB, being overweight/obese was associated with increased risk of mortality in COVID-19 hospital admissions, emphasising the need for public health interventions in this patient population

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy.

Peer reviewe

Risk factors for Coronavirus disease 2019 (Covid-19) death in a population cohort study from the Western Cape province, South Africa

Risk factors for coronavirus disease 2019 (COVID-19) death in sub-Saharan Africa and the effects of human immunodeficiency virus (HIV) and tuberculosis on COVID-19 outcomes are unknown. We conducted a population cohort study using linked data from adults attending public-sector health facilities in the Western Cape, South Africa. We used Cox proportional hazards models, adjusted for age, sex, location, and comorbidities, to examine the associations between HIV, tuberculosis, and COVID-19 death from 1 March to 9 June 2020 among (1) public-sector “active patients” (≥1 visit in the 3 years before March 2020); (2) laboratory-diagnosed COVID-19 cases; and (3) hospitalized COVID-19 cases. We calculated the standardized mortality ratio (SMR) for COVID-19, comparing adults living with and without HIV using modeled population estimates.Among 3 460 932 patients (16% living with HIV), 22 308 were diagnosed with COVID-19, of whom 625 died. COVID19 death was associated with male sex, increasing age, diabetes, hypertension, and chronic kidney disease. HIV was associated with COVID-19 mortality (adjusted hazard ratio [aHR], 2.14; 95% confidence interval [CI], 1.70–2.70), with similar risks across strata of viral loads and immunosuppression. Current and previous diagnoses of tuberculosis were associated with COVID-19 death (aHR, 2.70 [95% CI, 1.81–4.04] and 1.51 [95% CI, 1.18–1.93], respectively). The SMR for COVID-19 death associated with HIV was 2.39 (95% CI, 1.96–2.86); population attributable fraction 8.5% (95% CI, 6.1–11.1)

Early mobilisation in critically ill COVID-19 patients: a subanalysis of the ESICM-initiated UNITE-COVID observational study

Background Early mobilisation (EM) is an intervention that may improve the outcome of critically ill patients. There is limited data on EM in COVID-19 patients and its use during the first pandemic wave. Methods This is a pre-planned subanalysis of the ESICM UNITE-COVID, an international multicenter observational study involving critically ill COVID-19 patients in the ICU between February 15th and May 15th, 2020. We analysed variables associated with the initiation of EM (within 72 h of ICU admission) and explored the impact of EM on mortality, ICU and hospital length of stay, as well as discharge location. Statistical analyses were done using (generalised) linear mixed-effect models and ANOVAs. Results Mobilisation data from 4190 patients from 280 ICUs in 45 countries were analysed. 1114 (26.6%) of these patients received mobilisation within 72 h after ICU admission; 3076 (73.4%) did not. In our analysis of factors associated with EM, mechanical ventilation at admission (OR 0.29; 95% CI 0.25, 0.35; p = 0.001), higher age (OR 0.99; 95% CI 0.98, 1.00; p ≤ 0.001), pre-existing asthma (OR 0.84; 95% CI 0.73, 0.98; p = 0.028), and pre-existing kidney disease (OR 0.84; 95% CI 0.71, 0.99; p = 0.036) were negatively associated with the initiation of EM. EM was associated with a higher chance of being discharged home (OR 1.31; 95% CI 1.08, 1.58; p = 0.007) but was not associated with length of stay in ICU (adj. difference 0.91 days; 95% CI − 0.47, 1.37, p = 0.34) and hospital (adj. difference 1.4 days; 95% CI − 0.62, 2.35, p = 0.24) or mortality (OR 0.88; 95% CI 0.7, 1.09, p = 0.24) when adjusted for covariates. Conclusions Our findings demonstrate that a quarter of COVID-19 patients received EM. There was no association found between EM in COVID-19 patients' ICU and hospital length of stay or mortality. However, EM in COVID-19 patients was associated with increased odds of being discharged home rather than to a care facility. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021)

Implementing a video call visit system in a coronavirus disease 2019 unit

CITATION: Moolla, M. S. 2020. Implementing a video call visit system in a coronavirus disease 2019 unit. African Journal of Primary Health Care & Family Medicine, 12(1):a2637, doi:10.4102/phcfm.v12i1.2637.The original publication is available at https://phcfm.org/index.php/phcfmENGLISH ABSTRACT: The lockdown and physical distancing strategies imposed to combat COVID-19 have caused seismic shifts at all levels of society. Hospitals have been particularly affected. Healthcare workers (HCW’s) wore PPE during all patient interactions and visitors were prohibited. Life for a patient became lonelier and for those with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) measures were even more severe. HCW’s must treat patients following a biopsychosocial approach and promote communication between patients and loved ones. We implemented a low cost Video Call Visit system at Tygerberg Hospital, Cape Town. In this article we discuss the elements of a successful implementation and potential pitfalls in the context of a pandemic, notably cross-infection and privacy. Rapid but responsible innovation using 21st century tools was required to address the many challenges of the pandemic, including improving the lived experience for patients and families. These should be intended to last after the pandemic has passed.https://phcfm.org/index.php/phcfm/article/view/2637Publisher's versio

Abstract 18343: B-type Natriuretic Peptide (BNP) Levels Correlate With Indices of Left Ventricular Function and Predict Outcomes Following Transcatheter Aortic Valve Implantation (TAVI): A Cardiovascular Magnetic Resonance Imaging Study

Background: High levels of B-type natriuretic peptide (BNP) have been shown to predict outcomes following transcatheter aortic valve implantation (TAVI). However, the relationship between pre-procedural BNP levels and magnetic resonance imaging (MRI) parameters of cardiac function has not previously been described in patients with severe aortic stenosis undergoing TAVI. Methods: 108 patients underwent cardiac MRI and had measurements of BNP taken prior to TAVI. MRI was performed using a 1.5T scanner; steady-state free precession sequences were used for aortic valve planimetry and to assess ventricular volumes and mass. Gadolinium enhancement was assessed after injection of 0.1 mmol/kg of gadolinium contrast agent. Semi-automated image analysis was performed by two specialist reviewers blinded to patient treatment. Results: The mean age of the population studied was 79 years (IQR 74-85); mean follow-up was 805 days (IQR 368-1025). The mean BNP level was 698 ng/l (range 14-6048); univariate linear regression showed significant relationships between BNP and left ventricular (LV) ejection fraction (p&lt;0.0001), end diastolic volume (p=0.001), end systolic volume (p&lt;0.0001), and indexed mass (p=0.012). Median BNP levels were lower in patients who survived than in those who died (287ng/l vs. 560ng/l; p=0.099); ROC curve analysis found a BNP level of &gt;170ng/l to be the optimal cutoff for the prediction of all-cause mortality. Survival curve analysis showed a significantly increased risk of all-cause mortality in patients with BNP&gt;170ng/l and MRI LV ejection fraction &lt;50% (Hazard ratio 5.72, p=0.015; Figure). Conclusions: In patients with severe aortic stenosis undergoing TAVI, BNP levels are closely correlated with LV function, indexed LV mass, and both end-systolic and end-diastolic volumes, as measured by MRI. Patients with BNP&gt;170ng/l and MRI LV ejection fraction&lt;50% are at significantly higher risk of death following TAVI. </jats:p

Machine learning as a diagnostic decision aid for patients with transient loss of consciousness

BackgroundTransient loss of consciousness (TLOC) is a common reason for presentation to primary/emergency care; over 90% are because of epilepsy, syncope, or psychogenic non-epileptic seizures (PNES). Misdiagnoses are common, and there are currently no validated decision rules to aid diagnosis and management. We seek to explore the utility of machine-learning techniques to develop a short diagnostic instrument by extracting features with optimal discriminatory values from responses to detailed questionnaires about TLOC manifestations and comorbidities (86 questions to patients, 31 to TLOC witnesses).MethodsMulti-center retrospective self- and witness-report questionnaire study in secondary care settings. Feature selection was performed by an iterative algorithm based on random forest analysis. Data were randomly divided in a 2:1 ratio into training and validation sets (163:86 for all data; 208:92 for analysis excluding witness reports).ResultsThree hundred patients with proven diagnoses (100 each: epilepsy, syncope and PNES) were recruited from epilepsy and syncope services. Two hundred forty-nine completed patient and witness questionnaires: 86 epilepsy (64 female), 84 PNES (61 female), and 79 syncope (59 female). Responses to 36 questions optimally predicted diagnoses. A classifier trained on these features classified 74/86 (86.0% [95% confidence interval 76.9%–92.6%]) of patients correctly in validation (100 [86.7%–100%] syncope, 85.7 [67.3%–96.0%] epilepsy, 75.0 [56.6%–88.5%] PNES). Excluding witness reports, 34 features provided optimal prediction (classifier accuracy of 72/92 [78.3 (68.4%–86.2%)] in validation, 83.8 [68.0%–93.8%] syncope, 81.5 [61.9%–93.7%] epilepsy, 67.9 [47.7%–84.1%] PNES).ConclusionsA tool based on patient symptoms/comorbidities and witness reports separates well between syncope and other common causes of TLOC. It can help to differentiate epilepsy and PNES. Validated decision rules may improve diagnostic processes and reduce misdiagnosis rates.Classification of evidenceThis study provides Class III evidence that for patients with TLOC, patient and witness questionnaires discriminate between syncope, epilepsy and PNES.</jats:sec