Importance of vital signs to the early diagnosis and severity of sepsis: association between vital signs and sequential organ failure assessment score in patients with sepsis

A transformer-based diffusion probabilistic model for heart rate and blood pressure forecasting in Intensive Care Unit

BACKGROUND AND OBJECTIVE

Vital sign monitoring in the Intensive Care Unit (ICU) is crucial for enabling prompt interventions for patients. This underscores the need for an accurate predictive system. Therefore, this study proposes a novel deep learning approach for forecasting Heart Rate (HR), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) in the ICU.

METHODS

We extracted 24,886 ICU stays from the MIMIC-III database which contains data from over 46 thousand patients, to train and test the model. The model proposed in this study, Transformer-based Diffusion Probabilistic Model for Sparse Time Series Forecasting (TDSTF), merges Transformer and diffusion models to forecast vital signs. The TDSTF model showed state-of-the-art performance in predicting vital signs in the ICU, outperforming other models' ability to predict distributions of vital signs and being more computationally efficient. The code is available at https://github.com/PingChang818/TDSTF.

RESULTS

The results of the study showed that TDSTF achieved a Standardized Average Continuous Ranked Probability Score (SACRPS) of 0.4438 and a Mean Squared Error (MSE) of 0.4168, an improvement of 18.9% and 34.3% over the best baseline model, respectively. The inference speed of TDSTF is more than 17 times faster than the best baseline model.

CONCLUSION

TDSTF is an effective and efficient solution for forecasting vital signs in the ICU, and it shows a significant improvement compared to other models in the field.

Predicting septic shock in patients with sepsis at emergency department triage using systolic and diastolic shock index

INTRODUCTION

Identifying patients with at a high risk of progressing to septic shock is essential. Due to systemic vasodilation in the pathophysiology of septic shock, the use of diastolic blood pressure (DBP) has emerged. We hypothesized that the initial shock index (SI) and diastolic SI (DSI) at the emergency department (ED) triage can predict septic shock.

METHOD

This observational study used the prospectively collected sepsis registry. The primary outcome was progression to septic shock. Secondary outcomes were the time to vasopressor requirement, vasopressor dose, and severity according to SI and DSI. Patients were classified by tertiles according to the first principal component of shock index and diastolic shock index.

RESULTS

A total of 1267 patients were included in the analysis. The area under the receiver operating characteristic curve (AUC) for predicting progression to septic shock for DSI was 0.717, while that for SI was 0.707. The AUC for predicting progression to septic shock for DSI and SI were significantly higher than those for conventional early warning scores. Middle tertile showed adjusted Odd ratio (aOR) of 1.448 (95% CI 1.074-1.953), and that of upper tertile showed 3.704 (95% CI 2.299-4.111).

CONCLUSION

The SI and DSI were significant predictors of progression to septic shock. Our findings suggest an association between DSI and vasopressor requirement. We propose stratifying lower tertile as being at low risk, middle tertile as being at intermediate risk, and upper tertile as being at high risk of progression to septic shock. This system can be applied simply at the ED triage.

Impact of continuous and wireless monitoring of vital signs on clinical outcomes: a propensity-matched observational study of surgical ward patients

BACKGROUND

Continuous and wireless vital sign monitoring is superior to intermittent monitoring in detecting vital sign abnormalities; however, the impact on clinical outcomes has not been established.

METHODS

We performed a propensity-matched analysis of data describing patients admitted to general surgical wards between January 2018 and December 2019 at a single, tertiary medical centre in the USA. The primary outcome was a composite of in-hospital mortality or ICU transfer during hospitalisation. Secondary outcomes were the odds of individual components of the primary outcome, and heart failure, myocardial infarction, acute kidney injury, and rapid response team activations. Data are presented as odds ratios (ORs) with 95% confidence intervals (CIs) and n (%).

RESULTS

We initially screened a population of 34,636 patients (mean age 58.3 (Range 18-101) yr, 16,456 (47.5%) women. After propensity matching, intermittent monitoring (n=12 345) was associated with increased risk of a composite of mortality or ICU admission (OR 3.42, 95% CI 3.19-3.67; P<0.001), and heart failure (OR 1.48, 95% CI 1.21-1.81; P<0.001), myocardial infarction (OR 3.87, 95% CI 2.71-5.71; P<0.001), and acute kidney injury (OR 1.32, 95% CI 1.09-1.57; P<0.001) compared with continuous wireless monitoring (n=7955). The odds of rapid response team intervention were similar in both groups (OR 0.86, 95% CI 0.79-1.06; P=0.726).

CONCLUSIONS

Patients who received continuous ward monitoring were less likely to die or be admitted to ICU than those who received intermittent monitoring. These findings should be confirmed in prospective randomised trials.

The use of successive systolic differences in photoplethysmographic (PPG) signals for respiratory rate estimation

Most PPG-based methods for extracting the respiratory rate (RR) rely on changes in the PPG signal's amplitude, baseline, or frequency. However, several other parameters may provide more valuable information for accurate RR computation. In this study, we explored the capabilities of the respiratory-induced variations in successive systolic differences (RISSDV) of PPG signals to estimate RR. We partitioned fifty-three publicly available recordings into eight 1-min segments and identified peaks and troughs of the PPG signals to quantify respiratory-induced variations in amplitude (RIAV), baseline (RIIV), frequency (RIFV), and peak-to-peak amplitude differences (RISSDV). RR values were extracted by determining the peak frequency of the power spectral density of the four variations and the reference respiratory signal. We assessed each feature's performance by computing the root-mean-squared (RMSE) and mean absolute errors (MAE). RISSDV errors were significantly lower than those of RIAV (RMSE and MAE: p < 0.001), RIIV (RMSE: p < 0.01; MAE p < 0.05), and RIFV (RMSE and MAE: p < 0.001), and it appeared less sensitive to absent or missed PPG pulses than respiratory-induced frequency variations. Further research is necessary to extrapolate these findings to subjects under ambulatory rather than stationary conditions, including pediatric and neonatal populations.

Evaluation of Sepsis-1 and Sepsis-3 Diagnostic Criteria in Patients with Sepsis in Intensive Care Unit

Background

The use of SIRS and SOFA criteria in diagnosing sepsis among patients has been characterized by increasingly growing criticism. Indeed, the definition of sepsis has attracted significant controversy in history across medical and academic realms.

Methods

The study used the Medical Information Mart for Intensive Care-III (MIMIC-III) database in assessing the effectiveness of the SIRS and SOFA diagnostic criteria. It ascertained the severity and specificity of sepsis infection in ICU patients. The Medical Information Mart for Intensive Care-III (MIMIC-III) database was established by the Beth Israel Deaconess Medical Center (BIDMC) and MIT's Computational Physiology Laboratory. The database is a voluminous single-center database containing information pertaining to 38,000 adults who were admitted to the BIDMC in the 11 years leading up to 2012. The identification of patients with sepsis was conducted using the International Classification of Diseases (ICD-10-CM) diagnosis codes.

Results

The analysis of data for this study was based on the chi-square test, which is significant in comparing the specificity, mortality, and sensitivity of the data. The process of screening the MIMIC-III database resulted in the identification of 21,368 patients with infections from the hospital admissions in the database. The results also indicate a significantly higher mortality rate within 28 days of admission in sepsis-3 patients compared with sepsis-1. In this experiment, we limited the study period to 28 days to restrict the potential of mortality caused by other factors. Additionally, we evaluated the clinical factors associated with the sepsis-1 or sepsis-3 and found out similar results in the analysis for sepsis-1 and sepsis-3.

Conclusions

The study results also portray numerous challenges in using the sepsis-3 criteria as a diagnostic tool. In particular, the ICD-10-CM diagnosis approach was limiting because it inhibited the measure of uncertainty of infection present at the beginning of the two diagnostic criteria of sepsis-1 and sepsis-3.

Transferability and interpretability of the sepsis prediction models in the intensive care unit

BACKGROUND

We aimed to develop an early warning system for real-time sepsis prediction in the ICU by machine learning methods, with tools for interpretative analysis of the predictions. In particular, we focus on the deployment of the system in a target medical center with small historical samples.

METHODS

Light Gradient Boosting Machine (LightGBM) and multilayer perceptron (MLP) were trained on Medical Information Mart for Intensive Care (MIMIC-III) dataset and then finetuned on the private Historical Database of local Ruijin Hospital (HDRJH) using transfer learning technique. The Shapley Additive Explanations (SHAP) analysis was employed to characterize the feature importance in the prediction inference. Ultimately, the performance of the sepsis prediction system was further evaluated in the real-world study in the ICU of the target Ruijin Hospital.

RESULTS

The datasets comprised 6891 patients from MIMIC-III, 453 from HDRJH, and 67 from Ruijin real-world data. The area under the receiver operating characteristic curves (AUCs) for LightGBM and MLP models derived from MIMIC-III were 0.98 - 0.98 and 0.95 - 0.96 respectively on MIMIC-III dataset, and, in comparison, 0.82 - 0.86 and 0.84 - 0.87 respectively on HDRJH, from 1 to  5 h preceding. After transfer learning and ensemble learning, the AUCs of the final ensemble model were enhanced to 0.94 - 0.94 on HDRJH and to 0.86 - 0.9 in the real-world study in the ICU of the target Ruijin Hospital. In addition, the SHAP analysis illustrated the importance of age, antibiotics, net balance, and ventilation for sepsis prediction, making the model interpretable.

CONCLUSIONS

Our machine learning model allows accurate real-time prediction of sepsis within 5-h preceding. Transfer learning can effectively improve the feasibility to deploy the prediction model in the target cohort, and ameliorate the model performance for external validation. SHAP analysis indicates that the role of antibiotic usage and fluid management needs further investigation. We argue that our system and methodology have the potential to improve ICU management by helping medical practitioners identify at-sepsis-risk patients and prepare for timely diagnosis and intervention.

TRIAL REGISTRATION

NCT05088850 (retrospectively registered).

Advanced Triage Protocol: The Role of an Automated Lactate Order in Expediting Rapid Identification of Patients at Risk of Sepsis in the Emergency Department

We undertook a process improvement initiative to expedite rapid identification of potential sepsis patients based on triage chief complaint, vital signs, and initial lactate level.

Outcomes of Vital Sign Monitoring of an Acute Surgical Cohort With Wearable Sensors and Digital Alerting Systems: A Pragmatically Designed Cohort Study and Propensity-Matched Analysis

Background: The implementation and efficacy of wearable sensors and alerting systems in acute secondary care have been poorly described.

Objectives: to pragmatically test one such system and its influence on clinical outcomes in an acute surgical cohort.

Methods: In this pragmatically designed, pre-post implementation trial, participants admitted to the acute surgical unit at our institution were recruited. In the pre-implementation phase (September 2017 to May 2019), the SensiumVitals™ monitoring system, which continuously measures temperature, heart, and respiratory rates, was used for monitoring alongside usual care (intermittent monitoring in accordance with the National Early Warning Score 2 [NEWS 2] protocol) without alerts being generated. In the post-implementation phase (May 2019 to March 2020), alerts were generated when pre-established thresholds for vital parameters were breached, requiring acknowledgement from healthcare staff on provided mobile devices. Hospital length of stay, intensive care use, and 28-days mortality were measured. Balanced cohorts were created with 1:1 ‘optimal’ propensity score logistic regression models.

Results: The 1:1 matching method matched the post-implementation group (n = 141) with the same number of subjects from the pre-implementation group (n = 141). The median age of the entire cohort was 52 (range: 18–95) years and the median duration of wearing the sensor was 1.3 (interquartile range: 0.7–2.0) days. The median alert acknowledgement time was 111 (range: 1–2,146) minutes. There were no significant differences in critical care admission (planned or unplanned), hospital length of stay, or mortality.

Conclusion: This study offered insight into the implementation of digital health technologies within our institution. Further work is required for optimisation of digital workflows, particularly given their more favourable acceptability in the post pandemic era. Clinical trials registration information: ClinicalTrials.gov Identifier: NCT04638738.

Remote investigation and assessment of vital signs (RIA-VS)-proof of concept for contactless estimation of blood pressure, pulse, respiratory rate, and oxygen saturation in patients with suspicion of COVID-19

BACKGROUND

Vital signs are critical in assessing the severity and prognosis of infections, for example, COVID-19, influenza, sepsis, and pneumonia. This study aimed to evaluate a new method for rapid camera-based non-contact measurement of heart rate, blood oxygen saturation, respiratory rate, and blood pressure.

METHODS

Consecutive adult patients attending a hospital emergency department for suspected COVID-19 infection were invited to participate. Vital signs measured with a new camera-based method were compared to the corresponding standard reference methods. The camera device observed the patient's face for 30 s from ∼1 m.

RESULTS

Between 1 April and 1 October 2020, 214 subjects were included in the trial, 131 female (61%) and 83 male (39%). The mean age was 44 years (range 18-81 years). The new camera-based device's vital signs measurements were, on average, very close to the gold standard but the random variation was larger than the reference methods.

CONCLUSIONS

The principle of contactless measurement of blood pressure, pulse, respiratory rate, and oxygen saturation works, which is very promising. However, technical improvements to the equipment used in this study to reduce its random variability is required before clinical implementation. This will likely be a game changer once this is sorted out.

CLINICAL TRIAL REGISTRATION

Universal Trial Number (UTN) U1111-1251-4114 and the ClinicalTrials.gov Identifier NCT04383457.

Comparison of 4 Different Threshold Values of Shock Index in Predicting Mortality of COVID-19 Patients

OBJECTIVE

The object of this study was to examine the accuracy in prehospital shock index (SI) for predicting intensive care unit (ICU) requirement and 30-d mortality among from coronavirus disease 2019 (COVID-19) patients transported to the hospital by ambulance.

METHODS

All consecutive patients who were the age ≥18 y, transported to the emergency department (ED) by ambulance with a suspected or confirmed COVID-19 in the prehospital frame were included in the study. Four different cutoff points were compared (0.7, 0.8, 0.9, and 1.0) to examine the predictive performance of both the mortality and ICU requirement of the SI. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) was used to evaluate each cut-off value discriminatory for predicting 30-d mortality and ICU admission.

RESULTS

The total of 364 patients was included in this study. The median age in the study population was 69 y (range, 55-80 y), of which 196 were men and 168 were women. AUC values for 30-d mortality outcome were calculated as 0.672, 0.674, 0.755, and 0.626, respectively, for threshold values of 0.7, 0.8, 0.9 and 1.0. ICU admission was more likely for the patients with prehospital SI > 0.9. Similarly, the mortality rate was higher in patients with prehospital SI > 0.9.

CONCLUSIONS

Early triage of COVID-19 patients will ensure efficient use of health-care resources. The SI could be a helpful, fast, and powerful tool for predicting mortality status and ICU requirements of adult COVID-19 patients. It was concluded that the most useful threshold value for the shock index in predicting the prognosis of COVID-19 patients is 0.9.

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.

Initial assessment in emergency departments by chief complaint and respiratory rate

BACKGROUND

Understanding heterogeneity of the respiratory rate (RR) as a risk stratification marker across chief complaints is important to reduce misinterpretation of the risk posed by outcome events and to build accurate risk stratification tools. This study was conducted to investigate the associations between RR and clinical outcomes according to the five most frequent chief complaints in an emergency department (ED): fever, shortness of breath, altered mental status, chest pain, and abdominal pain.

METHODS

This retrospective cohort study examined ED data of all adult patients who visited the ED of a tertiary medical center during April 2018-September 2019. The primary exposure was RR at the ED visit. Outcome measures were hospitalization and mechanical ventilation use. We used restrictive cubic spline and logistic regression models to assess the association of interest.

RESULTS

Of 16 956 eligible ED patients, 4926 (29%) required hospitalization; 448 (3%) required mechanical ventilation. Overall, U-shaped associations were found between RR and the risk of hospitalization (eg, using RR = 16 as the reference, the odds ratio [OR] of RR = 32, 6.57 [95% CI 5.87-7.37]) and between RR and the risk of mechanical ventilation. This U-shaped association was driven by patients' association with altered mental status (eg, OR of RR = 12, 2.63 [95% CI 1.25-5.53]). For patients who have fever or shortness of breath, the risk of hospitalization increased monotonously with increased RR.

CONCLUSIONS

U-shaped associations of RR with the risk of overall clinical outcomes were found. These associations varied across chief complaints.

Implementation of Wearable Sensors and Digital Alerting Systems in Secondary Care: Protocol for a Real-World Prospective Study Evaluating Clinical Outcomes

BACKGROUND

Advancements in wearable sensors have caused a resurgence in their use, particularly because their miniaturization offers ambulatory advantages while performing continuous vital sign monitoring. Digital alerts can be generated following early recognition of clinical deterioration through breaches of set parameter thresholds, permitting earlier intervention. However, a systematic real-world evaluation of these alerting systems has yet to be conducted, and their efficacy remains unknown.

OBJECTIVE

The aim of this study is to implement wearable sensors and digital alerting systems in acute general wards to evaluate the resultant clinical outcomes.

METHODS

Participants on acute general wards will be screened and recruited into a trial with a pre-post implementation design. In the preimplementation phase, the SensiumVitals monitoring system, which continuously measures temperature, heart, and respiratory rates, will be used for monitoring alongside usual care. In the postimplementation phase, alerts will be generated from the SensiumVitals system when pre-established thresholds for vital parameters have been crossed, requiring acknowledgement from health care staff; subsequent clinical outcomes will be analyzed.

RESULTS

Enrolment is currently underway, having started in September 2017, and is anticipated to end shortly. Data analysis is expected to be completed in 2021.

CONCLUSIONS

This study will offer insight into the implementation of digital health technologies within a health care trust and aims to describe the effectiveness of wearable sensors for ambulatory continuous monitoring and digital alerts on clinical outcomes in acute general ward settings.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04638738; https://clinicaltrials.gov/ct2/show/NCT04638738.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/26240.

Performance of a pre-administration infection screening questionnaire in patients with rheumatoid arthritis administered biological disease-modifying antirheumatic drugs

AIM

Pre-administration screening of active infections is imperative for the safe use of biological disease-modifying antirheumatic drugs (bDMARDs) in patients with rheumatoid arthritis (RA). However, a standardized screening method is lacking. We therefore implemented a novel systematic screening method with a simple predetermined questionnaire on infections and assessed its effectiveness.

METHODS

We retrospectively reviewed medical records of individuals for whom intravenous bDMARDs were administered for RA from January 2016 to April 2019. We evaluated the performance of the new screening method based on physicians' assessments. In addition, a survey was administered to nurses, regarding their assessment of the usefulness of this new screening. The incidence of infections was also assessed.

RESULTS

A total of 1636 cases underwent this new screening. The new screening method showed high sensitivity (0.97) and specificity (0.89) with a negative predictive value of 99.9%, as determined based on the physician's decision. Administration of bDMARDs was postponed in 37 (2.5%) patients, and there was only one case in which the screening failed to note an active infection. The nurses' survey demonstrated high agreement (87.5%) about the usefulness of this screening on the grounds of clarity, simplicity, ease, and time-saving effects. There was no significant increase in infections after implementation of this method.

CONCLUSIONS

Systematic screening with a predetermined simple questionnaire is effective as an infection screening method, with a high negative predictive value. This approach contributes to high satisfaction of nurses and a time-efficient practice by focusing on screen-positive cases without increasing infections.

The pilot, proof of concept REMOTE-COVID trial: remote monitoring use in suspected cases of COVID-19 (SARS-CoV 2)

BACKGROUND

SARS-CoV-2 has ever-increasing attributed deaths. Vital sign trends are routinely used to monitor patients with changes in these parameters preceding an adverse event. Wearable sensors can measure vital signs continuously and remotely, outside of hospital facilities, recognising early clinical deterioration. We aim to determine the feasibility & acceptability of remote monitoring systems for quarantined individuals in a hotel suspected of COVID-19.

METHODS

A pilot, proof-of-concept, feasibility trial was conducted in engineered hotels near London airports (May-June 2020). Individuals arriving to London with mild suspected COVID-19 symptoms requiring quarantine, as recommended by Public Health England, or healthcare professionals with COVID-19 symptoms unable to isolate at home were eligible. The SensiumVitals™ patch, measuring temperature, heart & respiratory rates, was applied on arrival for the duration of their stay. Alerts were generated when pre-established thresholds were breeched; trained nursing staff could consequently intervene.

RESULTS

Fourteen individuals (M = 7, F = 7) were recruited; the mean age was 34.9 (SD 11) years. Mean length of stay was 3 (SD 1.8) days. In total, 10 vital alerts were generated across 4 participants, resulting in telephone contact, reassurance, or adjustment of the sensor. No individuals required hospitalisation or virtual general practitioner review.

DISCUSSION

This proof-of-concept trial demonstrated the feasibility of a rapidly implemented model of healthcare delivery through remote monitoring during a pandemic at a hotel, acting as an extension to a healthcare trust. Benefits included reduced viral exposure to healthcare staff, with recognition of clinical deterioration through ambulatory, continuous, remote monitoring using a discrete wearable sensor.

CONCLUSION

Remote monitoring systems can be applied to hotels to deliver healthcare safely in individuals suspected of COVID-19. Further work is required to evaluate this model on a larger scale.

TRIAL REGISTRATION

Clinical trials registration information: ClinicalTrials.gov Identifier: NCT04337489 (07/04/2020).

Design of the pilot, proof of concept REMOTE-COVID trial: remote monitoring use in suspected cases of COVID-19 (SARS-CoV-2)

Background

The outbreak of SARS-CoV-2 (coronavirus, COVID-19), declared a pandemic by the World Health Organization (WHO), is a global health problem with ever-increasing attributed deaths. Vital sign trends are routinely used to monitor patients with changes in these parameters often preceding an adverse event. Wearable sensors can measure vital signs continuously (e.g. heart rate, respiratory rate, temperature) remotely and can be utilised to recognise early clinical deterioration.

Methods

We describe the protocol for a pilot, proof-of-concept, observational study to be conducted in an engineered hotel near London airports, UK. The study is set to continue for the duration of the pandemic. Individuals arriving to London with mild symptoms suggestive of COVID-19 or returning from high-risk areas requiring quarantine, as recommended by the Public Health England, or healthcare professionals with symptoms suggestive of COVID-19 unable to isolate at home will be eligible for a wearable patch to be applied for the duration of their stay. Notifications will be generated should deterioration be detected through the sensor and displayed on a central monitoring hub viewed by nursing staff, allowing for trend deterioration to be noted. The primary objective is to determine the feasibility of remote monitoring systems in detecting clinical deterioration for quarantined individuals in a hotel.

Discussion

This trial should prove the feasibility of a rapidly implemented model of healthcare delivery through remote monitoring during a global pandemic at a hotel, acting as an extension to a healthcare trust. Potential benefits would include reducing infection risk of COVID-19 to healthcare staff, with earlier recognition of clinical deterioration through ambulatory, continuous, remote monitoring using a discrete wearable sensor. We hope our results can power future, robust randomised trials.

Trial registration

ClinicalTrials.gov Identifier: NCT04337489.

Quantitative systematic review: Sources of inaccuracy in manually measured adult respiratory rate data

AIMS

To identify the potential sources of inaccuracy in manually measured adult respiratory rate (RR) data and quantify their effects.

DESIGN

Quantitative systematic review with meta-analyses where appropriate.

DATA SOURCES

Medline, CINAHL, and Cochrane Library (from database inception to 31 July 2019).

REVIEW METHODS

Studies presenting data on individual sources of inaccuracy in the manual measurement of adult RR were analysed, assessed for quality, and grouped according to the source of inaccuracy investigated. Quantitative data were extracted and synthesized and meta-analyses performed where appropriate.

RESULTS

Included studies (N = 49) identified five sources of inaccuracy. The awareness effect creates an artefactual reduction in actual RR, and observation methods involving shorter counts cause systematic underscoring. Individual RR measurements can differ substantially in either direction between observations due to inter- or intra-observer variability. Value bias, where particular RRs are over-represented (suggesting estimation), is a widespread problem. Recording omission is also widespread, with higher average rates in inpatient versus triage/admission contexts.

CONCLUSION

This review demonstrates that manually measured RR data are subject to several potential sources of inaccuracy.

IMPACT

RR is an important indicator of clinical deterioration and commonly included in track-and-trigger systems. However, the usefulness of RR data depends on the accuracy of the observations and documentation, which are subject to five potential sources of inaccuracy identified in this review. A single measurement may be affected by several factors. Hence, clinicians should interpret recorded RR data cautiously unless systems are in place to ensure its accuracy. For nurses, this includes counting rather than estimating RRs, employing 60-s counts whenever possible, ensuring patients are unaware that their RR is being measured, and documenting the resulting value. For any given site, interventions to improve measurement should take into account the local organizational and cultural context, available resources, and the specific measurement issues that need to be addressed.

Prediction of Mortality Associated with Cardiac and Radiological Findings in Patients with Pulmonary Embolism

Background: In this study, we aimed to compare echocardiography, electrocardiography (ECG) abnormalities, Doppler ultrasonography (USG), and computed tomography pulmonary angiography (CTPA) results in predicting 3-month mortality in patients with acute pulmonary embolism (PE).

Methods: This retrospective cohort study included 124 patients (72 females, 52 males) with acute PE. Demographics, symptoms, clinical signs, comorbidities, history of surgery, arterial blood gas, liver-renal functions, complete blood count, echocardiography, ECG, Doppler USG, and CTPA results, as well as 3-month mortality were recorded.

Results: pH (z = –2.623; p <0.01), hemoglobin (z = –3.112; p <0.01), and oxygen saturation (z = –2.165; p <0. 01) were significantly higher in survivors. White blood cell (z = –2.703; p <0.01), blood urea nitrogen (z = –3.840; p <0.01), creatinine (z = –3.200; p <0.01), respiratory rate (z = –2.759; p <0.01), and heart rate (z = –2.313; p <0.01) were significantly higher in non-survivors. Nonspecific ST changes (AUC 0.52, 95% CI 0.43–0.61), p pulmonale (AUC 0.52, 95% CI 0.43–0.61), normal axis (AUC 0.61), right axis deviation (AUC 0.56), right ventricle strain pattern (AUC 0.59), and right pulmonary artery embolism (AUC 0.54) on CTPA showed the highest mortality prediction.

Conclusions: Nonspecific ST changes, p pulmonale, normal axis and right axis deviation in ECG, RV strain in echocardiography, and right pulmonary artery embolism on CTPA are associated with a higher mortality in patients with PE.

Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

BACKGROUND

Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset.

METHODS

Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset.

RESULTS

270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001; DOR 4.358), SOFA (0.716; P < .001; DOR 3.720), and SIRS (0.655; P < .001; DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset.

CONCLUSIONS

The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.

Risk factors and outcomes of sepsis-associated delirium in intensive care unit patients: A secondary data analysis

OBJECTIVE

To identify the risk factors of sepsis-associated delirium and determine their effect on intensive care unit adult patient outcomes.

DESIGN

A secondary analysis of data from system development studies.

SETTING

Korean intensive care unit patients in a university hospital who were diagnosed with sepsis.

METHODS

The risk factors for sepsis-associated delirium were classified into patient factors and sepsis clinical features and were analysed using hierarchical logistic regression analysis. Outcomes included in-hospital mortality, 30-day in-hospital mortality, duration of mechanical ventilation, length of stay in the intensive care unit, length of hospital stay, total medical expenses, discharge placement, re-hospitalisation and visits to the emergency department after discharge.

RESULTS

The risk factor for sepsis-associated delirium including patients aged 65 ≥years, dependent activity and high nursing needs (patient factors), low level of consciousness, tachypnoea, and thrombocytopaenia (clinical features of sepsis). Use of vasopressors/inotropes and albumin decreased the risk of sepsis-associated delirium. Mechanical ventilation duration was prolonged and discharge to skilled nursing facilities was increased by sepsis-associated delirium.

CONCLUSIONS

The risk factors for sepsis-associated delirium increased as the severity of condition for patients with sepsis increased. Early identification of risk factors associated with sepsis-associated delirium may improve patient outcomes.

Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals

BACKGROUND

Severe sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat.

OBJECTIVE

The purpose of this study was to evaluate the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay and 30-day readmission.

DESIGN

Prospective clinical outcomes evaluation.

SETTING

Evaluation was performed on a multiyear, multicentre clinical data set of real-world data containing 75 147 patient encounters from nine hospitals across the continental USA, ranging from community hospitals to large academic medical centres.

PARTICIPANTS

Analyses were performed for 17 758 adult patients who met two or more systemic inflammatory response syndrome criteria at any point during their stay ('sepsis-related' patients).

INTERVENTIONS

Machine learning algorithm for severe sepsis prediction.

OUTCOME MEASURES

In-hospital mortality, length of stay and 30-day readmission rates.

RESULTS

Hospitals saw an average 39.5% reduction of in-hospital mortality, a 32.3% reduction in hospital length of stay and a 22.7% reduction in 30-day readmission rate for sepsis-related patient stays when using the machine learning algorithm in clinical outcomes analysis.

CONCLUSIONS

Reductions of in-hospital mortality, hospital length of stay and 30-day readmissions were observed in real-world clinical use of the machine learning-based algorithm. The predictive algorithm may be successfully used to improve sepsis-related outcomes in live clinical settings.

TRIAL REGISTRATION NUMBER

NCT03960203.

Risk Factors, Etiologies, and Screening Tools for Sepsis in Pregnant Women: A Multicenter Case-Control Study

BACKGROUND

Given the significant morbidity and mortality of maternal sepsis, early identification is key to improve outcomes. This study aims to evaluate the performance characteristics of the systemic inflammatory response syndrome (SIRS), quick Sequential [Sepsis-related] Organ Failure Assessment (qSOFA), and maternal early warning (MEW) criteria for identifying cases of impending sepsis in parturients. The secondary objective of this study is to identify etiologies and risk factors for maternal sepsis and to assess timing of antibiotics in patients diagnosed with sepsis.

METHODS

Validated maternal sepsis cases during the delivery hospitalization from 1995 to 2012 were retrospectively identified at 7 academic medical centers in the United States and Israel. Control patients were matched by date of delivery in a 1:4 ratio. The sensitivity and specificity of SIRS, qSOFA, and MEW criteria for identifying sepsis were calculated. Data including potential risk factors, vital signs, laboratory values, and clinical management were collected for cases and controls.

RESULTS

Eighty-two sepsis cases during the delivery hospitalization were identified and matched to 328 controls. The most common causes of sepsis were the following: chorioamnionitis 20 (24.4%), endometritis 19 (23.2%), and pneumonia 9 (11.0%). Escherichia coli 12 (14.6%), other Gram-negative rods 8 (9.8%), and group A Streptococcus 6 (7.3%) were the most commonly found pathogens. The sensitivities and specificities for meeting criteria for screening tools were as follows: (1) SIRS (0.93, 0.63); (2) qSOFA (0.50, 0.95); and (3) MEW criteria for identifying sepsis (0.82, 0.87). Of 82 women with sepsis, 10 (12.2%) died. The mortality rate for those who received antibiotics within 1 hour of diagnosis was 8.3%. The mortality rate was 20% for the patients who received antibiotics after >1 hour.

CONCLUSIONS

Chorioamnionitis and endometritis were the most common causes of sepsis, together accounting for about half of cases. Notable differences were observed in the sensitivity and specificity of sepsis screening tools with the highest to lowest sensitivity being SIRS, MEW, and qSOFA criteria, and the highest to lowest specificity being qSOFA, MEW, and SIRS. Mortality was doubled in the cohort of patients who received antibiotics after >1 hour. Clinicians need to be vigilant to identify cases of peripartum sepsis early in its course and prioritize timely antibiotic therapy.

Measuring diaphragm movement and respiratory frequency using a novel ultrasound device in healthy volunteers

PURPOSE

To evaluate the ability of a novel ultrasound (US) device, DiaMon, to monitor diaphragm movement via its proxy liver movement, and compare it with the respired flow measured with a flowmeter, in awake and healthy volunteers. We wanted to (1) establish the optimal anatomical position for attaching the DiaMon device to the abdominal wall, and (2) evaluate the accuracy of continuous monitoring of respiratory frequency.

METHODS

Thirty healthy subjects were recruited. The DiaMon probe was applied subcostally in four different positions with the subjects in five different postures. The subjects breathed tidal volumes into a spirometer for 30-60 s with the DiaMon recording simultaneously.

RESULTS

The device detected a readable signal in 83-100% of the position/posture-combinations. The technical correlation between the two signals was highest in the anterior axillary-supine position (mean ± SD: 0.95 ± 0.03), followed by paramidline-supine (0.90 ± 0.09) and midclavicular-supine (0.89 ± 0.12). The frequency measurements yielded a mean difference of 0.03 (95% limits of agreement - 0.11, 0.16) breaths per minute in the anterior axillary-supine position.

CONCLUSION

The DiaMon device is able to detect liver movement in most subjects, and it measures breathing frequency accurately.

Incorporating Observed Physiological Data to Personalize Pediatric Vital Sign Alarm Thresholds

Bedside monitors are intended as a safety net in patient care, but their management in the inpatient setting is a significant patient safety concern. The low precision of vital sign alarm systems leads to clinical staff becoming desensitized to the sound of the alarm, a phenomenon known as alarm fatigue. Alarm fatigue has been shown to increase response time to alarms or result in alarms being ignored altogether and has negative consequences for patient safety. We present methods to establish personalized thresholds for heart rate and respiratory rate alarms. These thresholds are first chosen based on patient characteristics available at the time of admission and are then adapted to incorporate vital signs observed in the first 2 hours of monitoring. We demonstrate that the adapted thresholds are similar to those chosen by clinicians for individual patients and would result in fewer alarms than the currently used age-based thresholds. Personalized vital sign alarm thresholds can help to alleviate the problem of alarm fatigue in an inpatient setting while ensuring that all critical vital signs are detected.

Wearable sensors to improve detection of patient deterioration

INTRODUCTION

Monitoring a patient's vital signs forms a basic component of care, enabling the identification of deteriorating patients and increasing the likelihood of improving patient outcomes. Several paper-based track and trigger warning scores have been developed to allow clinical evaluation of a patient and guidance on escalation protocols and frequency of monitoring. However, evidence suggests that patient deterioration on hospital wards is still missed, and that patients are still falling through the safety net. Wearable sensor technology is currently undergoing huge growth, and the development of new light-weight wireless wearable sensors has enabled multiple vital signs monitoring of ward patients continuously and in real time.

AREAS COVERED

In this paper, we aim to closely examine the benefits of wearable monitoring applications that measure multiple vital signs; in the context of improving healthcare and delivery. A review of the literature was performed.

EXPERT COMMENTARY

Findings suggest that several sensor designs are available with the potential to improve patient safety for both hospital patients and those at home. Larger clinical trials are required to ensure both diagnostic accuracy and usability.

Frequency of vital sign measurement among intubated patients in the general ward and nurses' attitudes toward vital sign measurement

Purpose

The lack of recognition of respiratory distress may result in emergency tracheal intubation in the general ward. However, few studies have examined the differences in the frequency of vital sign measurement between patients with and without emergency tracheal intubation in the general ward. Thus, this study aimed to investigate the differences in the frequency of vital sign measurements between patients with and without emergency tracheal intubation.

Patients and methods

This is a single-center, retrospective, observational study of unplanned intensive care unit (ICU) admissions from the general wards between December 2015 and February 2017. This study included patients with emergency medical intubations in the general ward who were then transferred to the ICU. Vital signs measured within 24 hours prior to ICU admission were compared between patients who did and did not require emergency tracheal intubation in the general ward. A survey was also conducted to explore the nurses' attitudes toward vital sign measurements.

Results

Compared with other vital signs, the respiratory rate was significantly less frequently measured. Moreover, the frequency of respiratory rate measurement was lower in the 38 patients who were intubated than in the 102 patients who were not intubated in the general ward (P=0.07). The survey revealed that 54% of the participating nurses considered assessment of the respiratory rate as the most troublesome nursing task and ~15% of nurses did not routinely measure respiratory rates.

Conclusion

Respiratory rate was less frequently assessed in deteriorating patients in the general ward, possibly because it was considered a troublesome task.

The golden hour of sepsis: An in-depth analysis of sepsis-related maternal mortality in middle-income country Suriname

Background

Sepsis was the main cause of maternal mortality in Suriname, a middle-income country. Objective of this study was to perform a qualitative analysis of the clinical and management aspects of sepsis-related maternal deaths with a focus on the ‘golden hour’ principle of antibiotic therapy.

Methods

A nationwide reproductive age mortality survey was performed from 2010 to 2014 to identify and audit all maternal deaths in Suriname. All sepsis-related deaths were reviewed by a local expert committee to assess socio-demographic characteristics, clinical aspects and substandard care.

Results

Of all 65 maternal deaths in Suriname 29 (45%) were sepsis-related. These women were mostly of low socio-economic class (n = 23, 82%), of Maroon ethnicity (n = 14, 48%) and most deaths occurred postpartum (n = 21, 72%). Underlying causes were pneumonia (n = 14, 48%), wound infections (n = 3, 10%) and endometritis (n = 3, 10%). Bacterial growth was detected in 10 (50%) of the 20 available blood cultures. None of the women with sepsis as underlying cause of death received antibiotic treatment within the first hour, although most women fulfilled the diagnostic criteria of sepsis upon admission. In 27 (93%) of the 29 women from which sufficient information was available, substandard care factors were identified: delay in monitoring in 16 (59%) women, in diagnosis in 17 (63%) and in treatment in 21 (78%).

Conclusion

In Suriname, a middle-income country, maternal mortality could be reduced by improving early recognition and timely diagnosis of sepsis, vital signs monitoring and immediate antibiotic infusion (within the golden hour).

Patients with head trauma: A study on initial prehospital assessment and care

BACKGROUND

Best evidence guidelines are intended to standardise assessment and treatment of patients with head trauma and improve outcomes for TBI patients. The primary aim was to explore pre-hospital emergency care nurses' (PECNs') documented assessment and care of patients with head trauma and the secondary aim to study gender differences in the documented care and interventions given by the PECNs.

METHODS

A retrospective observational study was conducted by evaluating 2750 prehospital medical records.

RESULTS

The results showed that 25.2% of the patients were assessed according to all four core-elements in the guidelines and 78.6% of the patients underwent at least one intervention by the PECNs. Male patients were to a higher extent assessed according to guidelines and were given higher transport priority while females were more often assessed for vital parameters and received significantly more analgesics. The assessment documented by the PECNs was not optimal concerning documentation using the Glasgow Coma Scale, but the documented assessment of circulation and, especially, respiratory rate was high (77.2%) compared to previous studies.

CONCLUSION

The findings of this study showed a low degree of documented assessment of parameters related to head trauma and might also indicate some gender differences in the assessment and management.

Make vital signs great again - A call for action

Vital signs are the simplest, cheapest and probably the most important information gathered on patients in hospital. In this narrative review we present a large amount of evidence that vital signs are currently little valued, not regularly or accurately recorded, and frequently not acted on appropriately. It is probable that few hospitals would keep their accreditation with regulatory bodies if they collected and acted on their laboratory results in the same way that they collect and act on vital signs. Professional societies and regulatory bodies need to address this issue: if vital signs were more accurately and frequently measured, and acted on promptly and appropriately hospital care would be safer, better and cheaper.

Sepsis in Internal Medicine wards: current knowledge, uncertainties and new approaches for management optimization

Sepsis represents a global health problem in terms of morbidity, mortality, social and economic costs. Although usually managed in Intensive Care Units, sepsis showed an increased prevalence among Internal Medicine wards in the last decade. This is substantially due to the ageing of population and to multi-morbidity. These characteristics represent both a risk factor for sepsis and a relative contra-indication for the admission to Intensive Care Units. Although there is a lack of literature on the management of sepsis in Internal Medicine, the outcome of these patients seems to be gradually improving. This is due to Internists' increased adherence to guidelines and "bundles". The routine use of SOFA score helps physicians in the definition of septic patients, even if the optimal score has still to come. Point-of-care ultrasonography, lactates, procalcitonin and beta-d-glucan are of help for treatment optimization. The purpose of this narrative review is to focus on the management of sepsis in Internal Medicine departments, particularly on crucial concepts regarding diagnosis, risk assessment and treatment. Key Messages Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. The prevalence of sepsis is constantly increasing, affecting more hospital patients than any other disease. At least half of patients affected by sepsis are admitted to Internal Medicine wards. Adherence to guidelines, routine use of clinical and lab scores and point-of-care ultrasonography are of help for early recognition of septic patients and treatment optimization.

Importance of respiratory rate for the prediction of clinical deterioration after emergency department discharge: a single-center, case-control study

Aim

The purpose of the present study was to investigate the predictors of clinical deterioration soon after emergency department (ED) discharge.

Methods

We undertook a case–control study using the ED database of the Nagano Municipal Hospital (Nagano, Japan) from January 2012 to December 2013. We selected adult patients with medical conditions who revisited the ED with deterioration within 2 days of ED discharge (deterioration group). The deterioration group was compared with a control group.

Results

During the study period, 15,724 adult medical patients were discharged from the ED. Of these, 170 patients revisited the ED because of clinical deterioration within 2 days. Among the initial vital signs, respiratory rate was less frequently recorded than other vital signs (P < 0.001 versus all other vital signs in each group). The frequency of recording each vital sign did not differ significantly between the groups. Overall, patients in the deterioration group had significantly higher respiratory rates than those in the control group (21 ± 5/min versus 18 ± 5/min, respectively; P = 0.002). A binary logistic regression analysis revealed that respiratory rate was an independent risk factor for clinical deterioration (unadjusted odds ratio, 1.15; 95% confidence interval, 1.04−1.26; adjusted odds ratio, 1.15; 95% confidence interval, 1.01−1.29).

Conclusions

An increased respiratory rate is a predictor of early clinical deterioration after ED discharge. Vital signs, especially respiratory rate, should be carefully evaluated when making decisions about patient disposition in the ED.

Early identification of sepsis in hospital inpatients by ward nurses increases 30-day survival

BACKGROUND

Systemic inflammatory response syndrome (SIRS) and sepsis are now frequently identified by observations of vital signs and detection of organ failure during triage in the emergency room. However, there is less focus on the effect on patient outcome with better observation and treatment at the ward level.

METHODS

This was a before-and-after intervention study in one emergency and community hospital within the Mid-Norway Sepsis Study catchment area. All patients with confirmed bloodstream infection have been prospectively registered continuously since 1994. Severity of sepsis, observation frequency of vital signs, treatment data, length of stay (LOS) in high dependency and intensive care units, and mortality were retrospectively registered from the patients' medical journals.

RESULTS

The post-intervention group (n = 409) were observed better and had higher odds of surviving 30 days (OR 2.7, 95 % CI 1.6, 4.6), lower probability of developing severe organ failure (0.7, 95 % CI 0.4, 0.9), and on average, 3.7 days (95 % CI 1.5, 5.9 days) shorter LOS than the pre-intervention group (n = 472).

CONCLUSIONS

In a cohort with stable mortality rates, early sepsis recognition by ward nurses may have reduced progression of disease and improved survival for patients in hospital with sepsis.

How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments

Background

Vital sign data are important for clinical decision making in emergency care. Clinical Decision Support Systems (CDSS) have been advocated to increase patient safety and quality of care. However, the efficiency of CDSS depends on the quality of the underlying vital sign data. Therefore, possible factors affecting vital sign data quality need to be understood.

This study aims to explore the factors affecting vital sign data quality in Swedish emergency departments and to determine in how far clinicians perceive vital sign data to be fit for use in clinical decision support systems. A further aim of the study is to provide recommendations on how to improve vital sign data quality in emergency departments.

Methods

Semi-structured interviews were conducted with sixteen physicians and nurses from nine hospitals and vital sign documentation templates were collected and analysed. Follow-up interviews and process observations were done at three of the hospitals to verify the results. Content analysis with constant comparison of the data was used to analyse and categorize the collected data.

Results

Factors related to care process and information technology were perceived to affect vital sign data quality. Despite electronic health records (EHRs) being available in all hospitals, these were not always used for vital sign documentation. Only four out of nine sites had a completely digitalized vital sign documentation flow and paper-based triage records were perceived to provide a better mobile workflow support than EHRs. Observed documentation practices resulted in low currency, completeness, and interoperability of the vital signs. To improve vital sign data quality, we propose to standardize the care process, improve the digital documentation support, provide workflow support, ensure interoperability and perform quality control.

Conclusions

Vital sign data quality in Swedish emergency departments is currently not fit for use by CDSS. To address both technical and organisational challenges, we propose five steps for vital sign data quality improvement to be implemented in emergency care settings.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-016-0305-4) contains supplementary material, which is available to authorized users.

Maternal Deaths Due to Sepsis in the State of Michigan, 1999-2006

OBJECTIVE

To identify maternal deaths due to sepsis in the state of Michigan, review the events leading to diagnosis, and evaluate treatment to identify areas for improvement.

METHODS

A case series was collected for maternal deaths due to sepsis from a cohort of maternal deaths in the state of Michigan. The study period was 1999-2006 and included deaths during pregnancy and up to 42 days postpartum. Cases were identified using Maternal Mortality Surveillance records from the Michigan Department of Community Health. Each case was reviewed by all authors.

RESULTS

Maternal sepsis was the cause of death in 15% (22/151) of pregnancy-related deaths. Of 22 deaths, 13 women presented to the hospital with sepsis, two developed sepsis during hospitalization, and seven developed sepsis at home without admission to the hospital for care. Review of available hospital records (n=15) revealed delays in initial appropriate antibiotic treatment occurred in 73% (11/15) of patients. Delay in escalation of care also occurred and was identified in 53% (8/15) of patients.

CONCLUSION

Common elements in these deaths illustrate three key delays that may have contributed to the deaths: in recognition of sepsis, in administration of appropriate antibiotics, and in escalation of care.

LEVEL OF EVIDENCE

III.

Systemic symptoms predict presence or development of severe sepsis and septic shock

BACKGROUND

Severe sepsis is a major cause of mortality and morbidity globally. As the time to adequate treatment is directly linked to outcome, early recognition is of critical importance. Early, accessible markers for severe sepsis are desirable. The systemic inflammatory response in sepsis leads to changes in vital signs and biomarkers and to symptoms unrelated to the focus of infection. This study investigated whether the occurrence of any of six systemic symptoms could predict severe sepsis in a cohort of patients admitted to hospital for suspected bacterial infections.

METHODS

A retrospective, consecutive study was conducted. All adult patients admitted during 1 month to a 550-bed secondary care hospital in western Sweden and given intravenous antibiotics for suspected community-acquired infection were included (n = 289). Symptoms (fever/chills, muscle weakness, localised pain, dyspnea, altered mental status and gastrointestinal symptoms) were registered along with age, sex, vital signs and laboratory values. Patients who fulfilled criteria of severe sepsis within 48 h were compared with patients who did not. Odds ratios for severe sepsis were calculated, adjusted for age, sex and comorbidities.

RESULTS

Criteria for severe sepsis were fulfilled by 90/289 patients (31.1%). Altered mental status (OR = 4.29, 95% CI = 2.03-9.08), dyspnea (OR = 2.92, 95% CI = 1.69-5.02), gastrointestinal symptoms (OR = 2.31, 95% CI = 1.14-4.69) and muscle weakness (OR = 2.24, 95% CI = 1.06-4.75) were more common in patients who had or later developed severe sepsis.

CONCLUSIONS

Systemic symptoms in combination with other signs of infection should be considered warning signs of severe sepsis.

Utility of the shock index in patients with sepsis

The shock index (SI) equals the heart rate/systolic blood pressure and has been used to predict clinical outcomes, especially in trauma and surgery patients. The authors reviewed the literature to determine its utility in the management of patients with sepsis and in the prediction of adverse outcomes in these patients. The medical literature was searched using PubMed, Scopus, Web of Science and Google Scholar databases to identify articles in English on the SI in humans. These studies demonstrated that the SI could help evaluate the adequacy of fluid resuscitation and the potential response to additional fluid. It can predict the presence of lactic acidosis. The SI also helps predict the development of organ failure and mortality. Consequently, this easily available bedside measurement has utility in the identification, management and prediction of prognosis in patients with sepsis.

Maternal physiologic parameters in relationship to systemic inflammatory response syndrome criteria: a systematic review and meta-analysis

OBJECTIVE

To establish the normal maternal range in healthy pregnant women for each component of the systemic inflammatory response syndrome (SIRS) criteria and compare these ranges with existing SIRS criteria.

DATA SOURCES

PubMed, Embase, and ClinicalTrials.gov databases were searched to identify studies of healthy parturients from the first trimester through 12 weeks postpartum that reported maternal temperature, respiratory rate, PaCO2, heart rate, white blood cell count data, or a combination of these.

METHODS OF STUDY SELECTION

Data were extracted from studies providing maternal values for components of SIRS criteria. The mean, standard deviation, and two standard deviations from the mean for all criteria parameters published in the literature were reported.

TABULATION, INTEGRATION, AND RESULTS

Eighty-seven studies met inclusion criteria and included 8,834 patients and 15,237 data points: temperature (10 studies and 2,367 patients), respiratory rate (nine studies and 312 patients), PaCO2 (12 studies and 441 patients), heart rate (39 studies and 1,374 patients), and white blood cell count (23 studies and 4,553 patients). Overlap with SIRS criteria occurred in healthy pregnant women during the second trimester, third trimester, and labor for each of the SIRS criteria except temperature. Every mean value for PaCO2 during pregnancy (and up to 48 hours postpartum) was below 32 mm Hg. Two standard deviations above the mean for temperature, respiratory rate, and heart rate were 38.1°C, 25 breaths per minute, and 107 beats per minute, respectively.

CONCLUSION

Current SIRS criteria often overlap with normal physiologic parameters during pregnancy and the immediate postpartum period; thus, alternative criteria must be developed to diagnose maternal sepsis.

Sepsis outside intensive care unit: the other side of the coin

INTRODUCTION

A growing body of evidence points out that a large amount of patients with sepsis are admitted and treated in medical ward (MW). With most of the sepsis studies conducted in intensive care unit (ICU), these patients, older and with more comorbidities have received poor attention. Provided the differences between the two groups of patients, results of diagnostic and therapeutic trials from ICU should not be routinely transferred to MW, where sepsis seems to be at least as common as in ICU.

METHODS

We analyzed clinical trials on novel tools for an early diagnosis of sepsis published in the last two year adopting strict research criteria. Moreover we conducted a target review of the literature on non-invasive monitoring of severe sepsis and septic shock.

RESULTS AND CONCLUSIONS

The combination of innovative and non-invasive tools for sepsis rule in/out, as quick alternatives to blood cultures (gold standard) with bedside integrated ultrasonography could impact triage, diagnosis and prognosis of septic patients managed in MW, preventing ICU admissions, poor outcomes and costly complications, especially in elderly that are usually highly vulnerable to invasive procedures.

How to improve the performance of intraoperative risk models: an example with vital signs using the surgical apgar score

BACKGROUND

Computerized reviews of patient data promise to improve patient care through early and accurate identification of at-risk and well patients. The significance of sampling strategy for patient vital signs data is not known. In the instance of the surgical Apgar score (SAS), we hypothesized that larger sampling intervals would improve the specificity and overall predictive ability of this tool.

METHODS

We used electronic intraoperative data from general and vascular surgical patients in a single-institution registry of the American College of Surgeons National Surgical Quality Improvement Program. The SAS, consisting of lowest heart rate, lowest mean arterial blood pressure, and estimated blood loss between incision and skin closure, was calculated using 5 methods: instantaneously and using intervals of of 5 and 10 minutes with and without interval overlap. Major complications including death were assessed at 30 days postoperatively.

RESULTS

Among 3000 patients, 272 (9.1%) experienced major complications or death. As the sampling interval increased from instantaneous (shortest) to 10 minutes without overlap (largest), the sensitivity, positive predictive value, and negative predictive value did not change significantly, but significant improvements were noted for specificity (79.5% to 82.9% across methods, P for trend <0.001) and accuracy (76.0% to 79.3% across methods, P for trend <0.01). In multivariate modeling, the predictive utility of the SAS as measured by the c-statistic nearly increased from Δc = +0.012 (P = 0.038) to Δc = +0.021 (P < 0.002) between the shortest and largest sampling intervals, respectively. Compared with a preoperative risk model, the net reclassification improvement and integrated discrimination improvement for the shortest versus largest sampling intervals of the SAS were net reclassification improvement 0.01 (P = 0.8) vs 0.06 (P = 0.02), and for integrated discrimination improvement, they were 0.008 (P < 0.01) vs 0.015 (P < 0.001).

CONCLUSIONS

When vital signs data are recorded in compliance with American Society of Anesthesiologists' standards, the sampling strategy for vital signs significantly influences performance of the SAS. Computerized reviews of patient data are subject to the choice of sampling methods for vital signs and may have the potential to be optimized for safe, efficient patient care.