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0 0.5 1 1.5 2+ Mortality 33% Improvement Relative Risk HCQ for COVID-19  Naseem et al.  LATE TREATMENT Is late treatment with HCQ beneficial for COVID-19? Retrospective 1,214 patients in Pakistan Lower mortality with HCQ (not stat. sig., p=0.34) Naseem et al., medRxiv, December 2020 Favors HCQ Favors control

Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep Neural Network

Naseem et al., medRxiv, doi:10.1101/2020.12.13.20247254
Dec 2020  
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HCQ for COVID-19
1st treatment shown to reduce risk in March 2020
*, now known with p < 0.00000000001 from 421 studies, recognized in 42 countries.
No treatment is 100% effective. Protocols combine complementary and synergistic treatments. * >10% efficacy in meta analysis with ≥3 clinical studies.
3,800+ studies for 60+ treatments.
Retrospective 1,214 hospitalized patients in Pakistan, 77 HCQ patients, showing 33% lower mortality with HCQ, multivariate Cox HR 0.67, p = 0.34.
Although the 33% lower mortality is not statistically significant, it is consistent with the significant 24% lower mortality [20‑29%] from meta analysis of the 249 mortality results to date.
risk of death, 33.3% lower, RR 0.67, p = 0.34, treatment 77, control 1,137, multivariate Cox.
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
Naseem et al., 14 Dec 2020, retrospective, Pakistan, preprint, 5 authors.
This PaperHCQAll
Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep Neural Network
Maleeha Naseem, Hajra Arshad, Syeda Amrah Hashmi, Furqan Irfan, Lead Scientist, Fahad Shabbir Ahmed
Background The second wave of COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy. Methods The current Deep-Neo-V model is built on our previously statistically rigorous machine learning framework [Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework] that evaluated statistically significant risk factors, generated new combined variables and then supply these risk factors to deep neural network to predict mortality in RT-PCR positive COVID-19 patients in the inpatient setting. We analyzed adult patients (≥18 years) admitted to the Aga Khan University Hospital, Pakistan with a working diagnosis of COVID-19 infection (n=1228). We excluded patients that were negative on COVID-19 on RT-PCR, had incomplete or missing health records. The first phase selection of risk factor was done using Cox-regression univariate and multivariate analyses. In the second phase, we generated new variables and tested those statistically significant for mortality and in the third and final phase we applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models and others. Results A total of 1228 cases were diagnosed as COVID-19 infection, we excluded 14 patients after the exclusion criteria and (n=)1214 patients were analyzed. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Late treatment
is less effective
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