Severity Index for Suspected Arbovirus (SISA) : machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

Funding: This study was supported, in part, by the Department of Defense Global Emerging Infection Surveillance (https://health.mil/Military-Health-Topics/Combat-Support/Armed-Forces-Health-Surveillance-Branch/Global-Emerging-Infections-Surveillance-and-Response) grant (P0220_13_OT) and the Department of Medicine of SUNY Upstate Medical University (http://www.upstate.edu/medicine/). D.F., M.H. and P.H. were supported by the Ben Kean Fellowship from the American Society for Tropical Medicine and Hygeine (https://www.astmh.org/awards-fellowships-medals/benjamin-h-keen-travel-fellowship-in-tropical-medi). S.J.R and A.M.S-I were supported by NSF DEB EEID 1518681, NSF DEB RAPID 1641145 (https://www.nsf.gov/), A.M.S-I was additionally supported by the Prometeo program of the National Secretary of Higher Education, Science, Technology, and Innovation of Ecuador (http://prometeo.educacionsuperior.gob.ec/).Background: Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. Methodology/Principal findings: Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. Conclusions/Significance: Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection.Publisher PDFPeer reviewe

The effect of two different doses of dexmedetomidine to attenuate cardiovascular and airway responses to tracheal extubation: a double blind randomized controlled trial

Background: The objective of the study is to assess the effectiveness of two different doses of dexmedetomidine, an alpha 2 adrenergic agonist, to attenuate the cardiovascular and airway responses to tracheal extubation and to observe the adverse effects. Methodology: Ninety ASA grade I and II patients aged 18-50 years were randomized into three groups; A, B, and C to receive dexmedetomidine 0.5µg/kg, 1µg/kg and normal saline placebo respectively about 15 minutes before discontinuation of inhalational agent. The heart rate, systolic blood pressure, diastolic blood pressure and mean arterial pressure were recorded during administration of drug, before extubation, during extubation, at 1, 3 minutes and every 5 minutes thereafter. Extubation quality was assessed on a 5 point scale and sedation by Ramsay sedation score. Results: There was significant decrease in heart rate and mean arterial pressure (p<0.001) during extubation in group A and B. Ninety percent of patients in group A, 93.3% patients in group B and 16.7% in group C could be extubated smoothly. The average time to extubate was 12.13±2.11, 14.08±3.19 and 10.27±2.09 minutes in groups A, B, and C respectively (P value <0.001). Higher incidence of bradycardia (p<0.001) was observed in Group A and B whereas incidence of breath holding was higher in group C (p=0.024). Conclusion: A dose of 0.5µg/kg of dexmedetomidine administered as a bolus infusion before extubation attenuates the stress response to extubation as effectively as 1µg/kg. Higher sedation scores and longer time to extubate are seen with a dose of 1µg/kg without causing respiratory depression

Associations Between Deceased-Donor Urine MCP-1 and Kidney Transplant Outcomes

Existing methods to predict recipient allograft function during deceased-donor kidney procurement are imprecise. Understanding the potential renal reparative role for monocyte chemoattractant protein-1 (MCP-1), a cytokine involved in macrophage recruitment after injury, might help to predict allograft outcomes. Methods: We conducted a substudy of the multicenter prospective Deceased Donor Study cohort that evaluated deceased kidney donors from 5 organ procurement organizations from May 2010 to December 2013. We measured urine MCP-1 (uMCP-1) concentrations from donor samples collected at nephrectomy to determine associations with donor acute kidney injury (AKI), recipient delayed graft function (DGF), 6-month estimated glomerular filtration rate (eGFR), and graft failure. We also assessed perfusate MCP-1 concentrations from pumped kidneys for associations with DGF and 6-month eGFR. Results: AKI occurred in 111 donors (9%). The median (interquartile range) uMCP-1 concentration was higher in donors with AKI compared with donors without AKI (1.35 [0.41–3.93] ng/ml vs. 0.32 [0.11–0.80] ng/ml, P < 0.001). DGF occurred in 756 recipients (31%), but uMCP-1 was not independently associated with DGF. Higher donor uMCP-1 concentrations were independently associated with a higher 6-month eGFR in those without DGF (0.77 [0.10–1.45] ml/min per 1.73 m2 per doubling of uMCP1). However, there were no independent associations between uMCP-1 and graft failure over a median follow-up of ∼2 years. Lastly, perfusate MCP-1 concentrations significantly increased during pump perfusion but were not associated with DGF or 6-month eGFR. Discussion: Donor uMCP-1 concentrations were modestly associated with higher recipient 6-month eGFR in those without DGF. However, the results suggest that donor uMCP-1 has minimal clinical utility given no associations with graft failure