Association of septic shock definitions and standardized mortality ratio in a contemporary cohort of critically ill patients

21st century critical care medicine: An overview

Critical care medicine in the 21st century has witnessed remarkable advancements that have significantly improved patient outcomes in intensive care units (ICUs). This abstract provides a concise summary of the latest developments in critical care, highlighting key areas of innovation. Recent advancements in critical care include Precision Medicine: Tailoring treatments based on individual patient characteristics, genomics, and biomarkers to enhance the effectiveness of therapies. The objective is to describe the recent advancements in Critical Care Medicine. Telemedicine: The integration of telehealth technologies for remote patient monitoring and consultation, facilitating timely interventions. Artificial intelligence (AI): AI-driven tools for early disease detection, predictive analytics, and treatment optimization, enhancing clinical decision-making. Organ Support: Advanced life support systems, such as Extracorporeal Membrane Oxygenation and Continuous Renal Replacement Therapy provide better organ support. Infection Control: Innovative infection control measures to combat emerging pathogens and reduce healthcare-associated infections. Ventilation Strategies: Precision ventilation modes and lung-protective strategies to minimize ventilator-induced lung injury. Sepsis Management: Early recognition and aggressive management of sepsis with tailored interventions. Patient-Centered Care: A shift towards patient-centered care focusing on psychological and emotional well-being in addition to medical needs. We conducted a thorough literature search on PubMed, EMBASE, and Scopus using our tailored strategy, incorporating keywords such as critical care, telemedicine, and sepsis management. A total of 125 articles meeting our criteria were included for qualitative synthesis. To ensure reliability, we focused only on articles published in the English language within the last two decades, excluding animal studies, in vitro/molecular studies, and non-original data like editorials, letters, protocols, and conference abstracts. These advancements reflect a dynamic landscape in critical care medicine, where technology, research, and patient-centered approaches converge to improve the quality of care and save lives in ICUs. The future of critical care promises even more innovative solutions to meet the evolving challenges of modern medicine.

CHARACTERISTICS AND PREDICTORS OF PATIENTS WITH SEPSIS WHO ARE CANDIDATES FOR MINIMALLY INVASIVE APPROACH OUTSIDE OF INTENSIVE CARE UNIT

ABSTRACT

Objective: To identify and describe characteristics of patients with sepsis who could be treated with minimally invasive sepsis (MIS) approach without intensive care unit (ICU) admission and to develop a prediction model to select candidates for MIS approach. Methods: A secondary analysis of the electronic database of patients with sepsis at Mayo Clinic, Rochester, MN. Candidates for the MIS approach were adults with septic shock and less than 48 hours of ICU stay, who did not require advanced respiratory support and were alive at hospital discharge. Comparison group consisted of septic shock patients with an ICU stay of more than 48 hours without advanced respiratory support at the time of ICU admission. Results: Of 1795 medical ICU admissions, 106 patients (6%) met MIS approach criteria. Predictive variables (age >65 years, oxygen flow >4 L/min, temperature <37°C, creatinine >1.6 mg/dL, lactate >3 mmol/L, white blood cells >15 × 10 9 /L, heart rate >100 beats/min, and respiration rate >25 breaths/min) selected through logistic regression were translated into an 8-point score. Model discrimination yielded the area under the receiver operating characteristic curve of 79% and was well fitted (Hosmer-Lemeshow P = 0.94) and calibrated. The MIS score cutoff of 3 resulted in a model odds ratio of 0.15 (95% confidence interval, 0.08-0.28) and a negative predictive value of 91% (95% confidence interval, 88.69-92.92). Conclusions: This study identifies a subset of low-risk septic shock patients who can potentially be managed outside the ICU. Once validated in an independent, prospective sample our prediction model can be used to identify candidates for MIS approach.

Burden and mortality of sepsis and septic shock at a high-volume, single-center in Vietnam: a retrospective study

BACKGROUND

Sepsis and septic shock have high mortality rates and often require a prolonged hospital stay. Patient outcomes may vary according to multiple factors. We aim to determine the prevalence of antimicrobial resistance and factors associated with mortality and hospital stay.

METHODS

Clinical and microbiological data of patients with sepsis or septic shock were retrospectively collected for 15 months. Patients with negative blood cultures and patients that did not meet the SEPSIS 3 criteria were excluded.

RESULTS

We included 48 septic shock and 28 septic patients (mean APACHE II 20.32 ± 5.61 and mean SOFA 9.41 ± 3.17), with a mean age of 60.5 ± 16.8 years and 56.6% males. WBCs, neutrophils, INR, and fibrinogen levels were significantly associated with mortality. 59.5% of the cultured bacteria were gram-negative (most common E. coli) and 27.8% were gram-positive (most common S. aureus), while 7.6% were other types of bacteria and 5.1% were fungi. Resistance patterns to gram-negative were varying, and resistance to piperacillin/tazobactam, carbapenems, and aminoglycosides were from 60% to 100% (A. baumanii), while they were highly sensitive to Colistin. E. coli was also resistant to ceftriaxone (77.8%) and sulbactam/cefoperazone (44.4%). Resistance rates for Gram-positives were high, from 86% to 100% for oxacillin, while for vancomycin, teicoplanin, and linezolid, they were often low but arrived up to 42.8%. According to our logistic regression analysis, patients over 65 year-old and those who received corticosteroids had a significantly increased risk of in-hospital mortality (OR: 4.0; OR: 4.8).

CONCLUSION

Sepsis still poses a significant threat to patients' health, even when positive blood culture results allow the administration of specific antibiotic treatment.

Reintubation Summation Calculation: A Predictive Score for Extubation Failure in Critically Ill Patients

Objective

To derive and validate a multivariate risk score for the prediction of respiratory failure after extubation.

Patients and methods

We performed a retrospective cohort study of adult patients admitted to the intensive care unit from January 1, 2006, to December 31, 2015, who received mechanical ventilation for ≥48 h. Extubation failure was defined as the need for reintubation within 72 h after extubation. Multivariate logistic regression model coefficient estimates generated the Re-Intubation Summation Calculation (RISC) score.

Results

The 6,161 included patients were randomly divided into 2 sets: derivation (n = 3,080) and validation (n = 3,081). Predictors of extubation failure in the derivation set included body mass index <18.5 kg/m² [odds ratio (OR), 1.91; 95% CI, 1.12–3.26; P = 0.02], threshold of Glasgow Coma Scale of at least 10 (OR, 1.68; 95% CI, 1.31–2.16; P < 0.001), mean airway pressure at 1 min of spontaneous breathing trial <10 cmH₂O (OR, 2.11; 95% CI, 1.68–2.66; P < 0.001), fluid balance ≥1,500 mL 24 h preceding extubation (OR, 2.36; 95% CI, 1.87–2.96; P < 0.001), and total mechanical ventilation days ≥5 (OR, 3.94; 95% CI 3.04–5.11; P < 0.001). The C-index for the derivation and validation sets were 0.72 (95% CI, 0.70–0.75) and 0.72 (95% CI, 0.69–0.75). Multivariate logistic regression demonstrated that an increase of 1 in RISC score increased odds of extubation failure 1.6-fold (OR, 1.58; 95% CI, 1.47–1.69; P < 0.001).

Conclusion

RISC predicts extubation failure in mechanically ventilated patients in the intensive care unit using several clinically relevant variables available in the electronic medical record but requires a larger validation cohort before widespread clinical implementation.

Rule-Based Cohort Definitions for Acute Respiratory Distress Syndrome: A Computable Phenotyping Strategy Based on the Berlin Definition

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OBJECTIVES:

Accurate identification of acute respiratory distress syndrome is essential for understanding its epidemiology, patterns of care, and outcomes. We aimed to design a computable phenotyping strategy to detect acute respiratory distress syndrome in electronic health records of critically ill patients.

DESIGN:

This is a retrospective cohort study. Using a near real-time copy of the electronic health record, we developed a computable phenotyping strategy to detect acute respiratory distress syndrome based on the Berlin definition.

SETTING:

Twenty multidisciplinary ICUs in Mayo Clinic Health System.

SUBJECTS:

The phenotyping strategy was applied to 196,487 consecutive admissions from year 2009 to 2019.

INTERVENTIONS:

The acute respiratory distress syndrome cohort generated by this novel strategy was compared with the acute respiratory distress syndrome cohort documented by clinicians during the same period. The sensitivity and specificity of the phenotyping strategy were calculated in randomly selected patient cohort (50 patients) using the results from manual medical record review as gold standard.

MEASUREMENTS AND MAIN RESULTS:

Among the patients who did not have acute respiratory distress syndrome documented, the computable phenotyping strategy identified 3,169 adult patients who met the Berlin definition, 676 patients (21.3%) were classified to have severe acute respiratory distress syndrome (Pao₂/Fio₂ ratio ≤ 100), 1,535 patients (48.4%) had moderate acute respiratory distress syndrome (100 < Pao₂/Fio₂ ratio ≤ 200), and 958 patients (30.2%) had mild acute respiratory distress syndrome (200 < Pao₂/Fio₂ ratio ≤ 300). The phenotyping strategy achieved a sensitivity of 94.4%, specificity of 96.9%, positive predictive value of 94.4%, and negative predictive value of 96.9% in a randomly selected patient cohort. The clinicians documented acute respiratory distress syndrome in 1,257 adult patients during the study period. The clinician documentation rate of acute respiratory distress syndrome was 28.4%. Compared with the clinicians’ documentation, the phenotyping strategy identified a cohort that had higher acuity and complexity of illness suggested by higher Sequential Organ Failure Assessment score (9 vs 7; p < 0.0001), higher Acute Physiology and Chronic Health Evaluation score (76 vs 63; p < 0.0001), higher rate of requiring invasive mechanical ventilation (99.1% vs 71.8%; p < 0.0001), higher ICU mortality (20.6% vs 16.8%; p < 0.0001), and longer ICU length of stay (5.1 vs 4.2 d; p < 0.0001).

CONCLUSIONS:

Our rule-based computable phenotyping strategy can accurately detect acute respiratory distress syndrome in critically ill patients in the setting of high clinical complexity. This strategy can be applied to enhance early recognition of acute respiratory distress syndrome and to facilitate best-care delivery and clinical research in acute respiratory distress syndrome.

Analysis of structure indicators influencing 3-h and 6-h compliance with the surviving sepsis campaign guidelines in China: a systematic review

Background

Compliance with the surviving sepsis campaign (SSC) guidelines (C_ssc) is a key factor affecting the effects of sepsis treatment. We designed this study to investigate the relationships of the structure indicators of ICU on 3 and 6-h C_ssc in China.

Methods

A total of 1854 hospitals were enrolled in a survey, led by the China National Critical Care Quality Control Center (China-NCCQC) from January 1, 2018, through December 31, 2018. We investigated the 1854 hospitals’ 3 and 6-h C_ssc, including compliance with each specific measure of the 3-h and 6-h SSC bundles. We also investigated the actual level of the structure indicators of ICU, released by China-NCCQC in 2015.The outcomes were in adherence with the SSC guidelines (2016). Monitoring indicators included 3 and 6-h C_ssc.

Results

In the subgroup, the rate of broad-spectrum antibiotic therapy was the highest, and the rate of CVP and ScvO2 measurement was the lowest among the items of 3 and 6-h C_ssc. Structure indicators related to 3 and 6-h C_ssc include the predicted mortality rate and the standardized mortality ratio (SMR). The relationships between 3 and 6-h C_ssc and the proportion of ICU in total inpatient bed occupancy, the proportion of acute physiology and chronic health evaluation (APACHE) II score ≥ 15 in all ICU patients were uncertain. There was no relationship of 3 and 6-h C_ssc with the proportion of ICU patients among total inpatients.

Conclusions

Structure indicators influencing 3 and 6-h C_ssc in China are the predicted mortality rate and the standardized mortality rate.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-021-00498-7.

Validation of a Retrospective Computing Model for Mortality Risk in the Intensive Care Unit

Objective

To compare the predictive performance of Epic Systems Corporation’s proprietary intensive care unit (ICU) mortality risk model (IMRM) with that of the Acute Physiology and Chronic Health Evaluation (APACHE) IV score.

Methods

This is a retrospective cohort study of patients treated from January 1, 2008, through January 1, 2018. This single-center study was performed at Mayo Clinic (Rochester, MN), a tertiary care teaching and referral center. The primary outcome was death in the ICU. Discrimination of each risk model for hospital mortality was assessed by comparing area under the receiver operating characteristic curve (AUROC).

Results

The cohort mostly comprised older patients (median age, 64 years) and men (56.7%). The mortality rate of the cohort was 3.5% (2251 of 63,775 patients). The AUROC for mortality prediction was 89.7% (95% CI, 89.5% to 89.9%) for the IMRM, which was significantly greater than the AUROC of 88.2% (95% CI, 87.9% to 88.4%) for APACHE IV (P<.001).

Conclusion

The IMRM was superior to the commonly used APACHE IV score and may be easily integrated into electronic health records at any hospital using Epic software.

Identification and Quantitative Determination of Lactate Using Optical Spectroscopy-Towards a Noninvasive Tool for Early Recognition of Sepsis

Uninterrupted monitoring of serum lactate levels is a prerequisite in the critical care of patients prone to sepsis, cardiogenic shock, cardiac arrest, or severe lung disease. Yet there exists no device to continuously measure blood lactate in clinical practice. Optical spectroscopy together with multivariate analysis is proposed as a viable noninvasive tool for estimation of lactate in blood. As an initial step towards this goal, we inspected the plausibility of predicting the concentration of sodium lactate (NaLac) from the UV/visible, near-infrared (NIR), and mid-infrared (MIR) spectra of 37 isotonic phosphate-buffered saline (PBS) samples containing NaLac ranging from 0 to 20 mmol/L. UV/visible (300–800 nm) and NIR (800–2600 nm) spectra of PBS samples were collected using the PerkinElmer Lambda 1050 dual-beam spectrophotometer, while MIR (4000–500 cm⁻¹) spectra were collected using the Spectrum two FTIR spectrometer. Absorption bands in the spectra of all three regions were identified and functional groups were assigned. The concentration of lactate in samples was predicted using the Partial Least-Squares (PLS) regression analysis and leave-one-out cross-validation. The regression analysis showed a correlation coefficient (R²) of 0.926, 0.977, and 0.992 for UV/visible, NIR, and MIR spectra, respectively, between the predicted and reference samples. The RMSECV of UV/visible, NIR, and MIR spectra was 1.59, 0.89, and 0.49 mmol/L, respectively. The results indicate that optical spectroscopy together with multivariate models can achieve a superior technique in assessing lactate concentrations.

Sepsis surveillance: an examination of parameter sensitivity and alert reliability

Objective

To examine performance of a sepsis surveillance system in a simulated environment where modifications to parameters and settings for identification of at-risk patients can be explored in-depth.

Materials and Methods

This was a multiple center observational cohort study. The study population comprised 14 917 adults hospitalized in 2016. An expert-driven rules algorithm was applied against 15.1 million data points to simulate a system with binary notification of sepsis events. Three system scenarios were examined: a scenario as derived from the second version of the Consensus Definitions for Sepsis and Septic Shock (SEP-2), the same scenario but without systolic blood pressure (SBP) decrease criteria (near SEP-2), and a conservative scenario with limited parameters. Patients identified by scenarios as being at-risk for sepsis were assessed for suspected infection. Multivariate binary logistic regression models estimated mortality risk among patients with suspected infection.

Results

First, the SEP-2-based scenario had a hyperactive, unreliable parameter SBP decrease >40 mm Hg from baseline. Second, the near SEP-2 scenario demonstrated adequate reliability and sensitivity. Third, the conservative scenario had modestly higher reliability, but sensitivity degraded quickly. Parameters differed in predicting mortality risk and represented a substitution effect between scenarios.

Discussion

Configuration of parameters and alert criteria have implications for patient identification and predicted outcomes.

Conclusion

Performance of scenarios was associated with scenario design. A single hyperactive, unreliable parameter may negatively influence adoption of the system. A trade-off between modest improvements in alert reliability corresponded to a steep decline in condition sensitivity in scenarios explored.

Frequency and mortality of septic shock in Europe and North America: a systematic review and meta-analysis

Background

Septic shock is the most severe form of sepsis, in which profound underlying abnormalities in circulatory and cellular/metabolic parameters lead to substantially increased mortality. A clear understanding and up-to-date assessment of the burden and epidemiology of septic shock are needed to help guide resource allocation and thus ultimately improve patient care. The aim of this systematic review and meta-analysis was therefore to provide a recent evaluation of the frequency of septic shock in intensive care units (ICUs) and associated ICU and hospital mortality.

Methods

We searched MEDLINE, Embase, and the Cochrane Library from 1 January 2005 to 20 February 2018 for observational studies that reported on the frequency and mortality of septic shock. Four reviewers independently selected studies and extracted data. Disagreements were resolved via consensus. Random effects meta-analyses were performed to estimate pooled frequency of septic shock diagnosed at admission and during the ICU stay and to estimate septic shock mortality in the ICU, hospital, and at 28 or 30 days.

Results

The literature search identified 6291 records of which 71 articles met the inclusion criteria. The frequency of septic shock was estimated at 10.4% (95% CI 5.9 to 16.1%) in studies reporting values for patients diagnosed at ICU admission and at 8.3% (95% CI 6.1 to 10.7%) in studies reporting values for patients diagnosed at any time during the ICU stay. ICU mortality was 37.3% (95% CI 31.5 to 43.5%), hospital mortality 39.0% (95% CI 34.4 to 43.9%), and 28-/30-day mortality 36.7% (95% CI 32.8 to 40.8%). Significant between-study heterogeneity was observed.

Conclusions

Our literature review reaffirms the continued common occurrence of septic shock and estimates a high mortality of around 38%. The high level of heterogeneity observed in this review may be driven by variability in defining and applying the diagnostic criteria, as well as differences in treatment and care across settings and countries.

Electronic supplementary material

The online version of this article (10.1186/s13054-019-2478-6) contains supplementary material, which is available to authorized users.