Predicting 30-day mortality in patients with sepsis: An exploratory analysis of process of care and patient characteristics.
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ABSTRACT:
SUBMITTER: Sanderson M
PROVIDER: S-EPMC6259088 | biostudies-other | 2018 Nov
REPOSITORIES: biostudies-other
Publications
Predicting 30-day mortality in patients with sepsis: An exploratory analysis of process of care and patient characteristics.
Sanderson Miriam M Chikhani Marc M Blyth Esme E Wood Sally S Moppett Iain K IK McKeever Tricia T Simmonds Mark Jr MJ
Journal of the Intensive Care Society 20180219 4
<h4>Background</h4>Sepsis represents a significant public health burden, costing the NHS £2.5 billion annually, with 35% mortality in 2006. The aim of this exploratory study was to investigate risk factors predictive of 30-day mortality amongst patients with sepsis in Nottingham.<h4>Methods</h4>Data were collected prospectively from adult patients with sepsis in Nottingham University Hospitals NHS Trust as part of an on-going quality improvement project between November 2011 and March 2014. Pati ...[more]
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Project description:Background: Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality.Methods: We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data.Results: Of 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835-0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790-0.860), random forest (0.848; 95% CI: 0.815-0.882), support vector machine (0.836; 95% CI: 0.802-870), and logistic regression (0.837; 95% CI: 0.803-0.871).Conclusions: A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.
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Project description:PURPOSE:Sepsis is a common acute life-threatening condition that emergency physicians routinely face. Diagnostic options within the Emergency Department (ED) are limited due to lack of infrastructure, consequently limiting the use of invasive hemodynamic monitoring or imaging tests. The mortality rate due to sepsis can be assessed via multiple scoring systems, for example, mortality in emergency department sepsis (MEDS) score and sepsis patient evaluation in the emergency department (SPEED) score, both of which quantify the variation of mortality rates according to clinical findings, laboratory data, or therapeutic interventions. This study aims to improve the management processes of sepsis patients by comparing SPEED score and MEDS score for predicting the 28-day mortality in cases of emergency sepsis. METHODS:The study is a cross-sectional, prospective study including 61 sepsis patients in ED in Suez Canal University Hospital, Egypt, from August 2017 to June 2018. Patients were selected by two steps: (1) suspected septic patients presenting with at least one of the following abnormal clinical findings: (a) body temperature higher than 38 °C or lower than 36 °C, (b) heart rate higher than 90 beats/min, (c) hyperventilation evidenced by respiratory rate higher than 20 breaths/min or PaCO2 lower than 32 mmHg, and (d) white blood cell count higher than 12,000/μL or lower than 4000/μL; (2) confirmed septic patients with at least a 2-point increase from the baseline total sequential organ failure assessment (SOFA) score following infection. Other inclusion criteria included adult patients with an age ≥18 years regardless of gender and those who had either systemic inflammatory response syndrome or suspected/confirmed infection. Patients were shortly follow-up for the 28-day mortality. Each patient was subject to SPEED score and MEDS score and then the results were compared to detect which of them was more effective in predicting outcome. The receiver operating characteristic curves were also done for MEDS and SPEED scores. RESULTS:Among the 61 patients, 41 died with the mortality rate of 67.2%. The mortality rate increased with a higher SPEED and MEDS scores. Both SPEED and MEDS scores revealed significant difference between the survivors and nonsurvivors (p = 0.004 and p < 0.001, respectively), indicating that both the two systems are effective in predicting the 28-day mortality of sepsis patients. Thereafter, the receiver operating characteristic curves were plotted, which showed that SPEED was better than the MEDS score when applied to the complete study population with an area under the curve being 0.87 (0.788-0.963) as compared with 0.75 (0.634-0.876) for MEDS. Logistic regression analysis revealed that the best fitting predictor of 28-day mortality for sepsis patients was the SPEED scoring system. For every one unit increase in SPEED score, the odds of 28-day mortality increased by 37%. CONCLUSION:SPEED score is more useful and accurate than MEDS score in predicting the 28-day mortality among sepsis patients. Therefore SPEED rather than MEDS should be more widely used in the ED for sepsis patients.
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Project description:BackgroundStroke patients admitted to an intensive care unit (ICU) follow a particular survival pattern with a high short-term mortality, but if they survive the first 30 days, a relatively favourable subsequent survival is observed.ObjectivesThe development and validation of two prognostic models predicting 30-day mortality for ICU patients with ischaemic stroke and for ICU patients with intracerebral haemorrhage (ICH), analysed separately, based on parameters readily available within 24 h after ICU admission, and with comparison with the existing Acute Physiology and Chronic Health Evaluation IV (APACHE-IV) model.DesignObservational cohort study.SettingAll 85 ICUs participating in the Dutch National Intensive Care Evaluation database.PatientsAll adult patients with ischaemic stroke or ICH admitted to these ICUs between 2010 and 2019.Main outcome measuresModels were developed using logistic regressions and compared with the existing APACHE-IV model. Predictive performance was assessed using ROC curves, calibration plots and Brier scores.ResultsWe enrolled 14 303 patients with stroke admitted to ICU: 8422 with ischaemic stroke and 5881 with ICH. Thirty-day mortality was 27% in patients with ischaemic stroke and 41% in patients with ICH. Important factors predicting 30-day mortality in both ischaemic stroke and ICH were age, lowest Glasgow Coma Scale (GCS) score in the first 24 h, acute physiological disturbance (measured using the Acute Physiology Score) and the application of mechanical ventilation. Both prognostic models showed high discrimination with an AUC 0.85 [95% confidence interval (CI), 0.84 to 0.87] for patients with ischaemic stroke and 0.85 (0.83 to 0.86) in ICH. Calibration plots and Brier scores indicated an overall good fit and good predictive performance. The APACHE-IV model predicting 30-day mortality showed similar performance with an AUC of 0.86 (95% CI, 0.85 to 0.87) in ischaemic stroke and 0.87 (0.86 to 0.89) in ICH.ConclusionWe developed and validated two prognostic models for patients with ischaemic stroke and ICH separately with a high discrimination and good calibration to predict 30-day mortality within 24 h after ICU admission.Trial registrationTrial registration: Dutch Trial Registry ( https://www.trialregister.nl/ ); identifier: NTR7438.
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Project description:BackgroundEarly risk stratification for guiding treatment priority in the emergency department (ED) is becoming increasingly important. Existing prediction models typically use demographics, vital signs and laboratory parameters. Laboratory-based models require blood testing, which may cause substantial delay. However, these delays can be prevented by the use of point-of-care testing (POCT), where results are readily available. We aimed to externally validate a laboratory-based model for mortality and subsequently assessed whether a POCT model yields comparable performance.MethodsAll adult patients visiting the ED of a university hospital between January 1st, 2012 and December 31st, 2016 were retrospectively reviewed for inclusion. Primary outcome was defined as 30-day mortality after ED presentation. We externally validated one existing prediction model including age, glucose, urea, sodium, haemoglobin, platelet count and white blood cell count. We assessed the predictive performance by discrimination, expressed as Area under the Curve (AUC). We compared the existing model to an equivalent model using predictors that are available with POCT (i.e. glucose, urea, sodium and haemoglobin). Additionally, we internally validated these models with bootstrapping.ResultsWe included 34,437 patients of whom 1,942 (5.6%) died within 30 days. The AUC of the laboratory-based model was 0.794. We refitted this model to our ED population and found an AUC of 0.812, which decreased only slightly to 0.790 with only POCT parameters.ConclusionsOur POCT-model performs similar to existing laboratory-based models in identifying patients at high risk for mortality, with results available within minutes. Although the model needs further validation and evaluation, it shows the potential of POCT for early risk stratification in the ED.
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Project description:BackgroundGiven the increased attention to sepsis at the population level there is a need to assess hospital performance in the care of sepsis patients using widely-available administrative data. The goal of this study was to develop an administrative risk-adjustment model suitable for profiling hospitals on their 30-day mortality rates for patients with sepsis.MethodsWe conducted a retrospective cohort study using hospital discharge data from general acute care hospitals in Pennsylvania in 2012 and 2013. We identified adult patients with sepsis as determined by validated diagnosis and procedure codes. We developed an administrative risk-adjustment model in 2012 data. We then validated this model in two ways: by examining the stability of performance assessments over time between 2012 and 2013, and by examining the stability of performance assessments in 2012 after the addition of laboratory variables measured on day one of hospital admission.ResultsIn 2012 there were 115,213 sepsis encounters in 152 hospitals. The overall unadjusted mortality rate was 18.5%. The final risk-adjustment model had good discrimination (C-statistic = 0.78) and calibration (slope and intercept of the calibration curve = 0.960 and 0.007, respectively). Based on this model, hospital-specific risk-standardized mortality rates ranged from 12.2 to 24.5%. Comparing performance assessments between years, correlation in risk-adjusted mortality rates was good (Pearson's correlation = 0.53) and only 19.7% of hospitals changed by more than one quintile in performance rankings. Comparing performance assessments after the addition of laboratory variables, correlation in risk-adjusted mortality rates was excellent (Pearson's correlation = 0.93) and only 2.6% of hospitals changed by more than one quintile in performance rankings.ConclusionsA novel claims-based risk-adjustment model demonstrated wide variation in risk-standardized 30-day sepsis mortality rates across hospitals. Individual hospitals' performance rankings were stable across years and after the addition of laboratory data. This model provides a robust way to rank hospitals on sepsis mortality while adjusting for patient risk.
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Project description:1) To develop a cumulative perioperative model (CPM) using the hospital clinical course of abdominal surgery cancer patients that predicts 30 and 90-day mortality risk; 2) To compare the predictive ability of this model to ten existing other models. We constructed a multivariate logistic regression model of 30 (90)-day mortality, which occurred in 106 (290) of the cases, using 13,877 major abdominal surgical cases performed at the University of Texas MD Anderson Cancer Center from January 2007 to March 2014. The model includes race, starting location (home, inpatient ward, intensive care unit or emergency center), Charlson Comorbidity Index, emergency status, ASA-PS classification, procedure, surgical Apgar score, destination after surgery (hospital ward location) and delayed intensive care unit admit within six days. We computed and compared the model mortality prediction ability (C-statistic) as we accumulated features over time. We were able to predict 30 (90)-day mortality with C-statistics from 0.70 (0.71) initially to 0.87 (0.84) within six days postoperatively. We achieved a high level of model discrimination. The CPM enables a continuous cumulative assessment of the patient's mortality risk, which could then be used as a decision support aid regarding patient care and treatment, potentially resulting in improved outcomes, decreased costs and more informed decisions.
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Project description:BackgroundThe relationship between platelet count and sepsis outcomes in intensive care units (ICUs) requires comprehensive investigation through large-scale multicenter studies.MethodsIn this multicenter retrospective cohort study, we analyzed 17,977 sepsis patients from 208 U.S. hospitals (2014-2015) using the eICU Collaborative Research Database v2.0. Analyses were adjusted for demographics, clinical parameters, comorbidities, and treatments. Generalized additive models and two-piecewise linear regression were used to assess the relationship between platelet count and mortality.ResultsA U-shaped relationship was identified with an inflection point at 176 × 10⁹/L. Below this threshold, each 10 × 10⁹/L increase in platelet count was associated with a 6% decrease in mortality risk (adjusted OR 0.94, 95% CI 0.93-0.95, p < 0.0001), while above it, each 10 × 10⁹/L increase was associated with a 1% increase in mortality risk (adjusted OR 1.01, 95% CI 1.00-1.01, p = 0.0153).ConclusionThis large-scale, multicenter retrospective study has made a significant contribution to understanding the association between platelet count and mortality in patients with sepsis in intensive care units. We identified a critical threshold of 176 × 109/L for platelet count and demonstrated a distinct U-shaped relationship with 30-day in-hospital mortality, providing valuable reference criteria for clinical risk stratification.
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Project description:Heparin-binding protein (HBP) has been shown to be a robust predictor of the progression to organ dysfunction from sepsis, and we hypothesized that dynamic changes in HBP may reflect the severity of sepsis. We therefore aim to investigate the predictive value of baseline HBP, 24-h, and 48-h HBP change for prediction of 30-day mortality in adult patients with sepsis. This is a prospective observational study in an intensive care unit of a tertiary center. Patients aged 20 years or older who met SEPSIS-3 criteria were prospectively enrolled from August 2019 to January 2020. Plasma levels of HBP were measured at admission, 24 h, and 48 h and dynamic changes in HBP were calculated. The Primary endpoint was 30-day mortality. We tested whether the biomarkers could enhance the predictive accuracy of a multivariable predictive model. A total of 206 patients were included in the final analysis. 48-h HBP change (HBPc-48 h) had greater predictive accuracy of area under the curve (AUC: 0.82), followed by baseline HBP (0.79), PCT (0.72), lactate (0.71), and CRP (0.65), and HBPc-24 h (0.62). Incorporation of HBPc-48 h into a clinical prediction model significantly improved the AUC from 0.85 to 0.93. HBPc-48 h may assist clinicians with clinical outcome prediction in critically ill patients with sepsis and can improve the performance of a prediction model including age, SOFA score and Charlson comorbidity index.
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