scholarly journals Predicting the course of Covid-19 and other epidemic and endemic disease

2021 ◽  
Author(s):  
Ana Cascon ◽  
William F Shadwick
Keyword(s):  
Intensive Care ◽  
Predictive Power ◽  
Respiratory Virus ◽  
Linear Growth ◽  
Hospital Admissions ◽  
Reproduction Number ◽  
Value Theory ◽  
Gompertz Function ◽  
Peak Loads ◽  
Spanish Flu

The Gompertz Function is an accurate model for epidemics from Cholera in 1853 to Spanish Flu in 1918 and Ebola in 2014. It also describes the acute phase of annual outbreaks of endemic influenza and in all of these instances it has significant predictive power. For Covid-19, we show that the Gompertz Function provides accurate forecasts not just for cases and deaths but, independently, for hospitalisations, intensive care admissions and other medical requirements. In particular Gompertz Function projections of healthcare requirements have been reliable enough to allow planning for: hospital admissions,intensive care admissions,ventilator usage, peak loads and duration. Analysis of data from the Spanish Flu pandemic and the endemic influenza cycle reveals alternating periods of Gompertz Function growth and linear growth in cumulative cases or deaths. Linear growth means the Reproduction Number is equal to 1 which in turn indicates endemicity. The same pattern has been observed with Covid-19. All the initial outbreaks ended in linear growth. Each new outbreak has been preceded by a period of linear growth and has ended with a transition from Gompertz Function growth to linear growth. This suggests that each of these outbreak cycles ended with a transition to endemicity for the current dominant strain and that the normal seasonal respiratory virus periods will continue to see new outbreaks. It remains to be seen if widespread vaccination will disrupt this cyclicality. Because both Gompertz Function Growth and linear growth are accurately predictable, the forecasting problem is reduced to identifying the transition between these modes and to improving the performance in the early Gompertz Function growth phase where its predictive power is lowest. The dynamics of the Gompertz Function are determined by the Gumbel probability distribution. This is an exceptional distribution with respect to the geometry determined by the affine group on the line which is the key to the Gumbel distribution's role as an Extreme Value Theory attractor. We show that this, together with the empirically observed asymmetry in epidemic data, makes the Gompertz Function growth essentially inevitable in epidemic models which agree with observations.

Neutrophil-to-lymphocyte Ratio as a Predictor of Mortality in ICU Patients: An Analysis of MIMIC-III Database

2020 ◽  
Author(s):  
Xie Wu ◽  
Zhanhao Su ◽  
Qipeng Luo ◽  
Yinan Li ◽  
Hongbai Wang ◽  
...  
Keyword(s):  
Intensive Care ◽  
Mortality Rate ◽  
Hospital Mortality ◽  
Predictive Power ◽  
Inflammatory Marker ◽  
Survival Group ◽  
Icu Patients ◽  
Lymphocyte Ratio ◽  
Saps Ii ◽  
Death Group

Abstract Background: Identifying high-risk patients in intensive care unit (ICU) is very important because of the high mortality rate. Existing scoring systems are numerous but lack effective inflammatory markers. Our objective was to identify and evaluate a low-cost, easily accessible and effective inflammatory marker that can predict mortality in ICU patients.Methods: We conducted a retrospective study using data from the Medical Information Mart for Intensive Care III database. We first divided the patients into the survival group and the death group based on in-hospital mortality. Receiver operating characteristic analyses were performed to find the best inflammatory marker (i.e. neutrophil-to-lymphocyte ratio, NLR). We then re-divided the patients into three groups based on NLR levels. Univariate and multivariate logistic regression were performed to evaluate the association between NLR and mortality. The area under the curve (AUC), Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI) were used to assess whether the incorporate of NLR can improve the predictive power of existing predictive model. Results: A total of 21,822 patients were included in this study, with an in-hospital mortality rate of 14.43%. Among all inflammatory marker in routine blood test results, NLR had the best predictive ability, with a median (interquartile range) NLR of 5.40 (2.95, 10.46) in the survival group and 8.32 (4.25, 14.75) in the death group. We then re-divided the patients into low (≤1), medium (1-6) and high (≥6) groups based on NLR levels. Compared with the median NLR group, the in-hospital mortality rates were significantly higher in the low (odds ratio [OR] = 2.09; 95% confidence interval [CI], 1.64 to 2.66) and high (OR=1.64; 95%CI, 1.50-1.80) NLR groups. The addition of NLR to Simplified Acute Physiology Score II (SAPS II) improved the AUC from 0.789 to 0.798 (P<0.001), with NRI of 16.64% (P<0.001) and IDI of 0.27% (P<0.001).Conclusion: NLR is a good predictor of mortality in ICU patients, both low and high levels of NLR are associated with elevated mortality rate. The inclusion of NLR might improve the predictive power of SAPS II.

scholarly journals The efficacy and safety of rifaximin-α: a 2-year observational study of overt hepatic encephalopathy

2019 ◽  
Vol 12 ◽  
pp. 175628481985825 ◽  
Author(s):  
Rosalie C. Oey ◽  
Lennart E.M. Buck ◽  
Nicole S. Erler ◽  
Henk R. van Buuren ◽  
Robert A. de Man
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hepatic Encephalopathy ◽  
Adverse Events ◽  
Bacterial Infections ◽  
Hospital Admissions ◽  
Emergency Department Visits ◽  
Interquartile Range ◽  
Overt Hepatic Encephalopathy ◽  
Bed Days

Background: After 5  years since the registration of rifaximin-α as a secondary prophylaxis for overt hepatic encephalopathy (HE) in the Netherlands, we aimed to evaluate the use of hospital resources and safety of rifaximin-α treatment in a real-world setting. Methods: We carried out prospective identification of all patients using rifaximin-α for overt HE. We assessed hospital resource use, bacterial infections, and adverse events during 6-month episodes before and after rifaximin-α initiation. Results: During 26 months we included 127 patients [71.7% male; median age 60.8 years (interquartile range: 56.2–66.1); median model for end-stage liver disease (MELD) score 15.0 (interquartile range: 12.1–20.4); 98% using lactulose treatment]. When comparing the first 6 months after rifaximin-α initiation with the prior 6 months, HE-related hospital admissions decreased (0.86 to 0.41 admissions/patient; p < 0.001), as well as the mean length of stay (8.85 to 3.79 bed days/admission; p < 0.001). No significant differences were found regarding HE-related intensive care unit admissions (0.09 to 0.06 admission/patient; p = 0.253), stay on the intensive care unit (0.43 to 0.57 bed days/admission; p = 0.661), emergency department visits (0.66 to 0.51 visits/patient; p = 0.220), outpatient clinic visits (2.49 to 3.30 bed visits/patient; p = 0.240), or bacterial infections (0.41 to 0.35 infections/patient; p = 0.523). Adverse events were recorded in 2.4% of patients. Conclusions: The addition of rifaximin-α to lactulose treatment was associated with a significant reduction in the number and length of HE-related hospitalizations for overt HE. Rifaximin-α treatment was well tolerated.

scholarly journals Prototype of a computerized scale for the active search for potential organ donors

2017 ◽  
Vol 25 (0) ◽  
Author(s):  
Érika Fernanda dos Santos Bezerra Ludwig ◽  
Marta Cristiane Alves Pereira ◽  
Yolanda Dora Évora Martinez ◽  
Karina Dal Sasso Mendes ◽  
Mariana Angela Rossaneis
Keyword(s):  
Intensive Care ◽  
Medical Records ◽  
Hospital Admissions ◽  
Tertiary Hospital ◽  
Wilcoxon Test ◽  
Organ Donors ◽  
Cardiorespiratory Arrest ◽  
Active Search ◽  
Death Cases ◽  
Severity Indexes

ABSTRACT Objective: to develop a prototype of a computerized scale for the active search for potential organ and tissue donors. Method: methodological study, with the analysis of 377 electronic medical records of patients who died due to encephalic death or cardiorespiratory arrest in the intensive care units of a tertiary hospital. Among the deaths due to cardiorespiratory arrest, the study aimed to identify factors indicating underreported encephalic death cases. The Acute Physiology and Chronic Health Evaluation II and Sepsis Related Organ Failure Assessment severity indexes were applied in the protocols. Based on this, a scale was built and sent to five experts for assessment of the scale content, and subsequently, it was computerized by using a prototyping model. Results: 34 underreported encephalic death cases were identified in the medical records of patients with cardiorespiratory arrest. Statistically significant differences were found in the Wilcoxon test between the scores of hospital admissions in the intensive care unit and the opening of the encephalic death protocol for both severity indexes. Conclusion: the prototype was effective for identifying potential organ donors, as well as for the identification of the degree of organ dysfunction in patients with encephalic death.

scholarly journals Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data

2020 ◽  
Vol 29 (9) ◽  
pp. 735-745 ◽  
Author(s):  
John Karlsson Valik ◽  
Logan Ward ◽  
Hideyuki Tanushi ◽  
Kajsa Müllersdorf ◽  
Anders Ternhag ◽  
...  
Keyword(s):  
Intensive Care ◽  
Medical Record ◽  
Predictive Value ◽  
Hospital Admissions ◽  
Medical Record Review ◽  
Suspected Infection ◽  
Clinical Criteria ◽  
Physician Review ◽  
Electronic Health ◽  
Record Review

BackgroundSurveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards.MethodsA rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by >2 points) and the likelihood of infection by physician medical record review.ResultsIn total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards.ConclusionsA fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards.

scholarly journals Comparative estimation of the reproduction number for pandemic influenza from daily case notification data

2006 ◽  
Vol 4 (12) ◽  
pp. 155-166 ◽  
Author(s):  
Gerardo Chowell ◽  
Hiroshi Nishiura ◽  
Luís M.A Bettencourt
Keyword(s):  
Growth Phase ◽  
Pandemic Influenza ◽  
Exponential Growth ◽  
Goodness Of Fit ◽  
Reproduction Number ◽  
Exponential Growth Phase ◽  
Initial Growth ◽  
Epidemic Curve ◽  
Spanish Flu ◽  
The City

The reproduction number, , defined as the average number of secondary cases generated by a primary case, is a crucial quantity for identifying the intensity of interventions required to control an epidemic. Current estimates of the reproduction number for seasonal influenza show wide variation and, in particular, uncertainty bounds for for the pandemic strain from 1918 to 1919 have been obtained only in a few recent studies and are yet to be fully clarified. Here, we estimate using daily case notifications during the autumn wave of the influenza pandemic (Spanish flu) in the city of San Francisco, California, from 1918 to 1919. In order to elucidate the effects from adopting different estimation approaches, four different methods are used: estimation of using the early exponential-growth rate (Method 1), a simple susceptible–exposed–infectious–recovered (SEIR) model (Method 2), a more complex SEIR-type model that accounts for asymptomatic and hospitalized cases (Method 3), and a stochastic susceptible–infectious–removed (SIR) with Bayesian estimation (Method 4) that determines the effective reproduction number at a given time t . The first three methods fit the initial exponential-growth phase of the epidemic, which was explicitly determined by the goodness-of-fit test. Moreover, Method 3 was also fitted to the whole epidemic curve. Whereas the values of obtained using the first three methods based on the initial growth phase were estimated to be 2.98 (95% confidence interval (CI): 2.73, 3.25), 2.38 (2.16, 2.60) and 2.20 (1.55, 2.84), the third method with the entire epidemic curve yielded a value of 3.53 (3.45, 3.62). This larger value could be an overestimate since the goodness-of-fit to the initial exponential phase worsened when we fitted the model to the entire epidemic curve, and because the model is established as an autonomous system without time-varying assumptions. These estimates were shown to be robust to parameter uncertainties, but the theoretical exponential-growth approximation (Method 1) shows wide uncertainty. Method 4 provided a maximum-likelihood effective reproduction number 2.10 (1.21, 2.95) using the first 17 epidemic days, which is consistent with estimates obtained from the other methods and an estimate of 2.36 (2.07, 2.65) for the entire autumn wave. We conclude that the reproduction number for pandemic influenza (Spanish flu) at the city level can be robustly assessed to lie in the range of 2.0–3.0, in broad agreement with previous estimates using distinct data.

scholarly journals Neutrophil-to-lymphocyte ratio as a predictor of mortality in intensive care unit patients: a retrospective analysis of the Medical Information Mart for Intensive Care III Database

BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e053548
Author(s):  
Xie Wu ◽  
Qipeng Luo ◽  
Zhanhao Su ◽  
Yinan Li ◽  
Hongbai Wang ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Inflammatory Markers ◽  
Predictive Power ◽  
Medical Information ◽  
Predictive Ability ◽  
Scoring Systems ◽  
Mortality Rates ◽  
Simplified Acute Physiology Score ◽  
Icu Patients

ObjectivesIdentifying high-risk patients in the intensive care unit (ICU) is important given the high mortality rate. However, existing scoring systems lack easily accessible, low-cost and effective inflammatory markers. We aimed to identify inflammatory markers in routine blood tests to predict mortality in ICU patients and evaluate their predictive power.DesignRetrospective case–control study.SettingSingle secondary care centre.ParticipantsWe analysed data from the Medical Information Mart for Intensive Care III database. A total of 21 822 ICU patients were enrolled and divided into survival and death groups based on in-hospital mortality.Primary and secondary outcome measuresThe predictive values of potential inflammatory markers were evaluated and compared using receiver operating characteristic curve analysis. After identifying the neutrophil-to-lymphocyte ratio (NLR) as having the best predictive ability, patients were redivided into low (≤1), medium (1–6) and high (>6) NLR groups. Univariate and multivariate logistic regression analyses were performed to evaluate the association between the NLR and mortality. The area under the curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to assess whether incorporating the NLR could improve the predictive power of existing scoring systems.ResultsThe NLR had the best predictive ability (AUC: 0.609; p<0.001). In-hospital mortality rates were significantly higher in the low (OR (OR): 2.09; 95% CI 1.64 to 2.66) and high (OR 1.64; 95% CI 1.50 to 1.80) NLR groups than in the medium NLR group. Adding the NLR to the Simplified Acute Physiology Score II improved the AUC from 0.789 to 0.798, with an NRI and IDI of 16.64% and 0.27%, respectively.ConclusionsThe NLR predicted mortality in ICU patients well. Both low and high NLRs were associated with elevated mortality rates, including the NLR may improve the predictive power of the Simplified Acute Physiology Score II.

scholarly journals Title High Flow Nasal cannula oxygen therapy in a general pediatric (non-intensive care) setting

2019 ◽  
Author(s):  
Louise Kooiman ◽  
Roelien Reimink ◽  
Veerle Langenhorst ◽  
Paul Brand ◽  
Jolita Bekhof
Keyword(s):  
Intensive Care ◽  
Respiratory Failure ◽  
Treatment Failure ◽  
Hospital Admissions ◽  
Work Of Breathing ◽  
High Flow ◽  
Nasal Cannula ◽  
Early Warning Score ◽  
High Flow Nasal Cannula ◽  
Vital Functions

Abstract Background: High flow nasal cannula therapy (HFNC) is being used increasingly for oxygen delivery in children with impending respiratory failure, however solid evidence of its effectiveness is sparse. Moreover, data on safety regarding its use outside of the Pediatric Intensive Care Unit (PICU), with flowrates exceeding 1 L/kg is lacking. Methods: Retrospective chart review at the pediatric ward of Isala, a general teaching hospital in Zwolle, The Netherlands, 100 km away from the nearest PICU. All children <18 years with impending respiratory failure treated with HFNC between January 2015 and May 2016 were included. A flowrate of 2 L/kg/minute for the first 10 kg was used; with 0.5 L/kg for every kg >10 kg and a maximum of 50 L/min. A pediatric early warning score (PEWS) comprising vital functions and work of breathing (0-28 points) was used to assess severity of respiratory distress. Treatment failure was defined as referral to the PICU. Results: In the 16-month study period HFNC was used during 41 hospital admissions in 39 patients (64.1% male), median age 6.3 months (interquartile range, IQR 3–20.6). Median (IQR) PEWS at the start of HFNC was 8.5 (7–10). Patients were diagnosed with bronchiolitis (70.7%), pneumonia (24.4%) or asthma (4.9%). In 18 cases (43.9%) HFNC failed, with referral to a PICU. No clinical variables (age, comorbidity, PEWS at admission or start of HFNC) nor improvement of the PEWS after 2 hours of HFNC were associated with treatment failure. We found no association between treatment failure and the start of HFNC at an earlier stage or at lower PEWS (odds ratio 1.03; 95% confidence interval 0.82-1.30; p=0.80). There were no safety issues, no cases with air leak or other complications. Conclusions: This small study suggests that HFNC can be safely used and initiated in a general pediatric department. We were unable to find clinical factors that predicted HFNC success. We recommend not to restrict evaluation of the effect of HFNC in studies to short-term (2 hours), but also after longer duration, at least 24 hours.

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