Factors predicting long-term survival of patients with sepsis on arrival at the emergency department: A single-center, observational study

Development of a Prognostic Scoring System for Tracheostomized Patients Requiring Prolonged Ventilator Care: A Ten-Year Experience in a University-Affiliated Tertiary Hospital

Background and Objectives: This study aimed to assess the value of a novel prognostic model, based on clinical variables, comorbidities, and demographic characteristics, to predict long-term prognosis in patients who received mechanical ventilation (MV) for over 14 days and who underwent a tracheostomy during the first 14 days of MV. Materials and Methods: Data were obtained from 278 patients (66.2% male; median age: 71 years) who underwent a tracheostomy within the first 14 days of MV from February 2011 to February 2021. Factors predicting 1-year mortality after the initiation of MV were identified by binary logistic regression analysis. The resulting prognostic model, known as the tracheostomy-ProVent score, was computed by assigning points to variables based on their respective ß-coefficients. Results: The overall 1-year mortality rate was 64.7%. Six factors were identified as prognostic indicators: platelet count < 150 × 103/μL, PaO2/FiO2 < 200 mmHg, body mass index (BMI) < 23.0 kg/m2, albumin concentration < 2.8 g/dL on day 14 of MV, chronic cardiovascular diseases, and immunocompromised status at admission. The tracheostomy-ProVent score exhibited acceptable discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.786 (95% confidence interval: 0.733-0.833, p < 0.001) and acceptable calibration (Hosmer-Lemeshow chi-square: 2.753, df: 8, p = 0.949). Based on the maximum Youden index, the cut-off value for predicting mortality was set at ≥2, with a sensitivity of 67.4% and a specificity of 76.3%. Conclusions: The tracheostomy-ProVent score is a good predictive tool for estimating 1-year mortality in tracheostomized patients undergoing MV for >14 days. This comprehensive model integrates clinical variables and comorbidities, enhancing the precision of long-term prognosis in these patients.

Decrease in Mortality after the Implementation of a Hospital Model to Improve Performance in Sepsis Care: Princess Sepsis Code

Sepsis is a time-dependent disease whose prognosis is influenced by early diagnosis and therapeutic measures. Mortality from sepsis remains high, and for this reason, the guidelines of the Surviving Sepsis Campaign recommend establishing specific care programs aimed at patients with sepsis. We present the results of the application of a hospital model to improve performance in sepsis care, called Princess Sepsis Code, with the aim of reducing mortality. A retrospective study was conducted using clinical, epidemiological, and outcome variables in patients diagnosed with sepsis from 2015 to 2022. A total of 2676 patients were included, 32% of whom required admission to the intensive care unit, with the most frequent focus of the sepsis being abdominal. Mortality in 2015, at the beginning of the sepsis code program, was 24%, with a declining rate noted over the study period, with mortality reaching 17% in 2022. In the multivariate analysis, age > 70 years, respiratory rate > 22 rpm, deterioration in the level of consciousness, serum lactate > 2 mmol/L, creatinine > 1.6 mg/dL, and the focus of the sepsis were identified as variables independently related to mortality. The implementation of the Princess Sepsis Code care model reduces the mortality of patients exhibiting sepsis and septic shock.

Association of body mass index with mortality of sepsis or septic shock: an updated meta-analysis

BACKGROUND

The effects of body mass index (BMI) on mortality of sepsis remain unknown, since previous meta-analyses have reported conflicting results. Several observational studies published recently have provided new evidence. Thus, we performed this updated meta-analysis.

METHODS

PubMed, Embase, Web of Science, and Cochran Library were searched for articles published before February 10, 2023. Observational studies that assessed the association of BMIs with mortality of sepsis patients aged > 18 years were selected. We excluded studies of which data were unavailable for quantitative synthesis. Odds ratios (OR) with 95% confidence interval (CI) were the effect measure, which were combined using fixed-effect or random-effect models. The Newcastle-Ottawa Scale was applied for quality assessment. Subgroups analyses were conducted according to potential confounders.

RESULTS

Fifteen studies (105,159 patients) were included in the overall analysis, which indicated that overweight and obese BMIs were associated with lower mortality (OR: 0.79, 95% CI 0.70-0.88 and OR: 0.74, 95% CI 0.67-0.82, respectively). The association was not significant in patients aged ≤ 50 years (OR: 0.89, 95% CI 0.68-1.14 and OR: 0.77, 95% CI 0.50-1.18, respectively). In addition, the relationship between morbidly obesity and mortality was not significant (OR: 0.91, 95% CI 0.62-1.32).

CONCLUSIONS

Overweight and obese BMIs (25.0-39.9 kg/m²) are associated with reduced mortality of patients with sepsis or septic shock, although such survival advantage was not found in all crowds. Trial registration The protocol of this study was registered in PROSPERO (registration number CRD42023399559).

Prognostic performance of the REDS score, SOFA score, NEWS2 score, and the red-flag, NICE high-risk, and SIRS criteria to predict survival at 180 days, in emergency department patients admitted with suspected sepsis – An observational cohort study

Background

Patients admitted to hospital with sepsis are at persistent risk of poor outcome after discharge. Many tools are available to risk-stratify sepsis patients for in-hospital mortality. This study aimed to identify the best risk-stratification tool to prognosticate outcome 180 days after admission via the emergency department (ED) with suspected sepsis.

Methods

A retrospective observational cohort study was performed of adult ED patients who were admitted after receiving intravenous antibiotics for the treatment of a suspected sepsis, between 1st March and 31st August 2019. The Risk-stratification of ED suspected Sepsis (REDS) score, SOFA score, Red-flag sepsis criteria met, NICE high-risk criteria met, the NEWS2 score and the SIRS criteria, were calculated for each patient. Death and survival at 180 days were noted. Patients were stratified in to high and low-risk groups as per accepted criteria for each risk-stratification tool. Kaplan–Meier curves were plotted for each tool and the log-rank test performed. The tools were compared using Cox-proportional hazard regression (CPHR). The tools were studied further in those without the following specified co-morbidities: Dementia, malignancy, Rockwood Frailty score of 6 or more, long-term oxygen therapy and previous do-not-resuscitate orders.

Results

Of the 1,057 patients studied 146 (13.8%) died at hospital discharge and 284 were known to have died within 180 days. Overall survival proportion was 74.4% at 180 days and 8.6% of the population was censored before 180 days. Only the REDS and SOFA scores identified less than 50% of the population as high-risk. All tools except the SIRS criteria, prognosticated for outcome at 180 days; Log-rank tests between high and low-risk groups were: REDS score p &lt; 0.0001, SOFA score p &lt; 0.0001, Red-flag criteria p = 0.001, NICE high-risk criteria p = 0.0001, NEWS2 score p = 0.003 and SIRS criteria p = 0.98. On CPHR, the REDS [Hazard ratio (HR) 2.54 (1.92–3.35)] and SOFA [HR 1.58 (1.24–2.03)] scores out-performed the other risk-stratification tools. In patients without the specified co-morbidities, only the REDS score and the SOFA score risk-stratified for outcome at 180 days.

Conclusion

In this study, all the risk-stratification tools studied were found to prognosticate for outcome at 180 days, except the SIRS criteria. The REDS and SOFA scores outperformed the other tools.

Septic Shock: Management and Outcomes

The incidence rates of sepsis and septic shock as a complication have become more common over the past several decades. With this increase, sepsis remains the most common cause of intensive care unit (ICU) admissions and one of the most mortality factors, with a huge burden on healthcare facilities. Septic shock has devastating consequences on patients' lives, including organ failures and other long-term complications. Due to its dynamic clinical presentations, guidelines and tools have been established to improve the diagnosis and management effectively. However, there is still a need for evidence-based standardized procedures for the diagnosis, treatment, and follow-up of sepsis and septic shock patients due to the inconsistency of current guidelines and studies contrasting with each other. The standardization would help physicians better manage sepsis, minimize complications and reduce mortality. Septic shock is usually challenging to manage due to its variety of clinical characteristics and physiologic dynamics, affecting the outcomes. Therefore, this review presented the available data in the literature on septic shock diagnosis, management, and prognosis to have an overview of the updated best practice approach to septic shock.

Sepsis in a Combined Medical and Surgical High Dependency/Intensive Care Unit in Singapore: A Cohort Study and Survival Analysis

Background

Sepsis is a common indication for intensive care unit (ICU) admission and is associated with significant mortality and morbidity. The aim of our study was to first assess the incidence, severity, short-term and long-term mortality of sepsis in a combined medical and surgical high dependency/ ICU in Singapore, and to identify factors associated with increasing short-term and long-term mortality.

Methods

All admissions from July 1 to December 31, 2017 were retrospectively screened and clinical data were collected. Patients were followed up until 3 years post ICU admission.

Results

Of a total 1526 admissions, 281 had infection at ICU admission, and 254 (16.6%) fulfilled sepsis-3 criteria for sepsis. A total of 141 (9.2%) had septic shock. The 30-day, 1-year, 2-year and 3-year mortality of sepsis patients were 19.3%, 25.2%, 30.3% and 32.3%, respectively. Lung was the most common site of infection. Compared with 30-day sepsis survivors, non-survivors were older (median age 70 vs 63, P <0.001), had higher percentage of lung infection (65.3% vs 36.1%, P <0.05), higher admission Sequential Organ Failure Assessment (SOFA) score (median 9 vs 5, P <0.001), and longer ICU stay (median days: 4 vs 3, P = 0.037). In stepwise Cox regression analysis, lung infection was an independent risk factor for both increasing 30-day and 3-year mortality. Immunocompromised host, increasing age and SOFA score were associated with higher 30-day mortality. Diabetes, admission quick Sequential Organ Failure Assessment (qSOFA) score >1 and unplanned ICU re-admission were associated with increasing 3-year mortality in 30-day survivors.

Conclusion

Our retrospective cohort single center study first reported sepsis admission incidence of 16.6% in a combined medical and surgical high dependency/ICU in Singapore, with significant short-term and long-term mortality. Lung infection was an independent risk factor for both 30-day and 3-year mortality.

Prognostic tools for elderly patients with sepsis: in search of new predictive models

As a tool to support clinical decision-making, Mortality Prediction Models (MPM) can help clinicians stratify and predict patient risk. There are numerous scoring systems for patients with sepsis that predict sepsis-related mortality and the severity of sepsis. But there are currently no MPMs for adults with sepsis who meet the criteria of "good." Clinicians are unlikely to use complex MPMs that require extensive or expensive data collection to impede workflow. Machine learning applied to minimal medical records of patients diagnosed with sepsis can be a useful tool. Progress is needed in the development and validation of clinical decision support tools that can assist in patient risk stratification, prognosis, discussion of patient outcomes, and shared decision making.

Development of a prognostic scoring system in patients with pneumonia requiring ventilator care for more than 4 days: a single-center observational study

BACKGROUND

The aim of the present study was to develop a prognostic model using demographic characteristics, comorbidities, and clinical variables measured on day 4 of mechanical ventilation (MV) for patients with prolonged acute mechanical ventilation (PAMV; MV for >96 hours).

METHODS

Data from 437 patients (70.9% male; median age, 68 years) were obtained over a period of 9 years. All patients were diagnosed with pneumonia. Binary logistic regression identified factors predicting mortality at 90 days after the start of MV. A PAMV prognosis score was calculating ß-coefficient values and assigning points to variables.

RESULTS

The overall 90-day mortality rate was 47.1%. Five factors (age ≥65 years, body mass index <18.5 kg/m2, hemato-oncologic diseases as comorbidities, requirement for vasopressors on day 4 of MV and requirement for neuromuscular blocking agents on day 4 of MV) were identified as prognostic indicators. Each factor was valued as +1 point, and used to develop a PAMV prognosis score. This score showed acceptable discrimination (area under the receiver operating characteristic curve of 0.695 for mortality, 95% confidence interval 0.650-0.738, p<0.001), and calibration (Hosmer-Lemeshow chi-square=6.331, with df 7 and p=0.502). The cutoff value for predicting mortality based on the maximum Youden index was ≤2 (sensitivity, 87.5%; specificity, 41.3%). For patients with PAMV scores ≤1, 2, 3 and ≥4, the 90-day mortality rates were 29.2%, 45.7%, 67.9%, and 90.9%, respectively (P<0.001).

CONCLUSIONS

Our study developed a PAMV prognosis score for predicting 90-day mortality. Further research is needed to validate the utility of this score.

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

Sepsis is a life-threatening condition caused by an exaggerated reaction of the body to an infection, that leads to organ failure or even death. Since sepsis can kill a patient even in just one hour, survival prediction is an urgent priority among the medical community: even if laboratory tests and hospital analyses can provide insightful information about the patient, in fact, they might not come in time to allow medical doctors to recognize an immediate death risk and treat it properly. In this context, machine learning can be useful to predict survival of patients within minutes, especially when applied to few medical features easily retrievable. In this study, we show that it is possible to achieve this goal by applying computational intelligence algorithms to three features of patients with sepsis, recorded at hospital admission: sex, age, and septic episode number. We applied several data mining methods to a cohort of 110,204 admissions of patients, and obtained high prediction scores both on this complete dataset (top precision-recall area under the curve PR AUC = 0.966) and on its subset related to the recent Sepsis-3 definition (top PR AUC = 0.860). Additionally, we tested our models on an external validation cohort of 137 patients, and achieved good results in this case too (top PR AUC = 0.863), confirming the generalizability of our approach. Our results can have a huge impact on clinical settings, allowing physicians to forecast the survival of patients by sex, age, and septic episode number alone.