Prior sepsis studies evaluating antibiotic timing have shown mixed results.
To evaluate the association between antibiotic timing and mortality among patients with sepsis receiving antibiotics within ...6 hours of emergency department registration.
Retrospective study of 35,000 randomly selected inpatients with sepsis treated at 21 emergency departments between 2010 and 2013 in Northern California. The primary exposure was antibiotics given within 6 hours of emergency department registration. The primary outcome was adjusted in-hospital mortality. We used detailed physiologic data to quantify severity of illness within 1 hour of registration and logistic regression to estimate the odds of hospital mortality based on antibiotic timing and patient factors.
The median time to antibiotic administration was 2.1 hours (interquartile range, 1.4-3.1 h). The adjusted odds ratio for hospital mortality based on each hour of delay in antibiotics after registration was 1.09 (95% confidence interval CI, 1.05-1.13) for each elapsed hour between registration and antibiotic administration. The increase in absolute mortality associated with an hour's delay in antibiotic administration was 0.3% (95% CI, 0.01-0.6%; P = 0.04) for sepsis, 0.4% (95% CI, 0.1-0.8%; P = 0.02) for severe sepsis, and 1.8% (95% CI, 0.8-3.0%; P = 0.001) for shock.
In a large, contemporary, and multicenter sample of patients with sepsis in the emergency department, hourly delays in antibiotic administration were associated with increased odds of hospital mortality even among patients who received antibiotics within 6 hours. The odds increased within each sepsis severity strata, and the increased odds of mortality were greatest in septic shock.
This case series characterizes the demographics, health services use, and vital status and discharge dispositions of patients with polymerase chain reaction–confirmed coronavirus disease 2019 ...(COVID-19) hospitalized in the Kaiser Permanente Northern California health system in March 2020.
Hospitalizations for severe sepsis are common, and a growing number of patients survive to hospital discharge. Nonetheless, little is known about survivors' post-discharge healthcare use.
To measure ...inpatient healthcare use of severe sepsis survivors compared with patients' own presepsis resource use and the resource use of survivors of otherwise similar nonsepsis hospitalizations.
This is an observational cohort study of survivors of severe sepsis and nonsepsis hospitalizations identified from participants in the Health and Retirement Study with linked Medicare claims, 1998-2005. We matched severe sepsis and nonsepsis hospitalizations by demographics, comorbidity burden, premorbid disability, hospitalization length, and intensive care use.
Using Medicare claims, we measured patients' use of inpatient facilities (hospitals, long-term acute care hospitals, and skilled nursing facilities) in the 2 years surrounding hospitalization. Severe sepsis survivors spent more days (median, 16 interquartile range, 3-45 vs. 7 0-29; P < 0.001) and a higher proportion of days alive (median, 9.6% interquartile range, 1.4-33.8% vs. 1.9% 0.0-7.9%; P < 0.001) admitted to facilities in the year after hospitalization, compared with the year prior. The increase in facility-days was similar for nonsepsis hospitalizations. However, the severe sepsis cohort experienced greater post-discharge mortality (44.2% 95% confidence interval, 41.3-47.2% vs. 31.4% 95% confidence interval, 28.6-34.2% at 1 year), a steeper decline in days spent at home (difference-in-differences, -38.6 d 95% confidence interval, -50.9 to 26.3; P < 0.001), and a greater increase in the proportion of days alive spent in a facility (difference-in-differences, 5.4% 95% confidence interval, 2.8-8.1%; P < 0.001).
Healthcare use is markedly elevated after severe sepsis, and post-discharge management may be an opportunity to reduce resource use.
The authors used a validated model with electronic-medical-record data to identify hospitalized patients at high risk for clinical deterioration. The intervention, which involved remote monitoring by ...nurses who reviewed records of high-risk patients and communicated with in-hospital rapid-response teams, was associated with decreased 30-day mortality.
To define a quantitative stratification algorithm for the risk of early-onset sepsis (EOS) in newborns ≥ 34 weeks' gestation.
We conducted a retrospective nested case-control study that used split ...validation. Data collected on each infant included sepsis risk at birth based on objective maternal factors, demographics, specific clinical milestones, and vital signs during the first 24 hours after birth. Using a combination of recursive partitioning and logistic regression, we developed a risk classification scheme for EOS on the derivation dataset. This scheme was then applied to the validation dataset.
Using a base population of 608,014 live births ≥ 34 weeks' gestation at 14 hospitals between 1993 and 2007, we identified all 350 EOS cases <72 hours of age and frequency matched them by hospital and year of birth to 1063 controls. Using maternal and neonatal data, we defined a risk stratification scheme that divided the neonatal population into 3 groups: treat empirically (4.1% of all live births, 60.8% of all EOS cases, sepsis incidence of 8.4/1000 live births), observe and evaluate (11.1% of births, 23.4% of cases, 1.2/1000), and continued observation (84.8% of births, 15.7% of cases, incidence 0.11/1000).
It is possible to combine objective maternal data with evolving objective neonatal clinical findings to define more efficient strategies for the evaluation and treatment of EOS in term and late preterm infants. Judicious application of our scheme could result in decreased antibiotic treatment in 80,000 to 240,000 US newborns each year.
IMPORTANCE: The Third International Consensus Definitions Task Force defined sepsis as “life-threatening organ dysfunction due to a dysregulated host response to infection.” The performance of ...clinical criteria for this sepsis definition is unknown. OBJECTIVE: To evaluate the validity of clinical criteria to identify patients with suspected infection who are at risk of sepsis. DESIGN, SETTINGS, AND POPULATION: Among 1.3 million electronic health record encounters from January 1, 2010, to December 31, 2012, at 12 hospitals in southwestern Pennsylvania, we identified those with suspected infection in whom to compare criteria. Confirmatory analyses were performed in 4 data sets of 706 399 out-of-hospital and hospital encounters at 165 US and non-US hospitals ranging from January 1, 2008, until December 31, 2013. EXPOSURES: Sequential Sepsis-related Organ Failure Assessment (SOFA) score, systemic inflammatory response syndrome (SIRS) criteria, Logistic Organ Dysfunction System (LODS) score, and a new model derived using multivariable logistic regression in a split sample, the quick Sequential Sepsis-related Organ Failure Assessment (qSOFA) score (range, 0-3 points, with 1 point each for systolic hypotension ≤100 mm Hg, tachypnea ≥22/min, or altered mentation). MAIN OUTCOMES AND MEASURES: For construct validity, pairwise agreement was assessed. For predictive validity, the discrimination for outcomes (primary: in-hospital mortality; secondary: in-hospital mortality or intensive care unit ICU length of stay ≥3 days) more common in sepsis than uncomplicated infection was determined. Results were expressed as the fold change in outcome over deciles of baseline risk of death and area under the receiver operating characteristic curve (AUROC). RESULTS: In the primary cohort, 148 907 encounters had suspected infection (n = 74 453 derivation; n = 74 454 validation), of whom 6347 (4%) died. Among ICU encounters in the validation cohort (n = 7932 with suspected infection, of whom 1289 16% died), the predictive validity for in-hospital mortality was lower for SIRS (AUROC = 0.64; 95% CI, 0.62-0.66) and qSOFA (AUROC = 0.66; 95% CI, 0.64-0.68) vs SOFA (AUROC = 0.74; 95% CI, 0.73-0.76; P < .001 for both) or LODS (AUROC = 0.75; 95% CI, 0.73-0.76; P < .001 for both). Among non-ICU encounters in the validation cohort (n = 66 522 with suspected infection, of whom 1886 3% died), qSOFA had predictive validity (AUROC = 0.81; 95% CI, 0.80-0.82) that was greater than SOFA (AUROC = 0.79; 95% CI, 0.78-0.80; P < .001) and SIRS (AUROC = 0.76; 95% CI, 0.75-0.77; P < .001). Relative to qSOFA scores lower than 2, encounters with qSOFA scores of 2 or higher had a 3- to 14-fold increase in hospital mortality across baseline risk deciles. Findings were similar in external data sets and for the secondary outcome. CONCLUSIONS AND RELEVANCE: Among ICU encounters with suspected infection, the predictive validity for in-hospital mortality of SOFA was not significantly different than the more complex LODS but was statistically greater than SIRS and qSOFA, supporting its use in clinical criteria for sepsis. Among encounters with suspected infection outside of the ICU, the predictive validity for in-hospital mortality of qSOFA was statistically greater than SOFA and SIRS, supporting its use as a prompt to consider possible sepsis.
IMPORTANCE: Current algorithms for management of neonatal early-onset sepsis (EOS) result in medical intervention for large numbers of uninfected infants. We developed multivariable prediction models ...for estimating the risk of EOS among late preterm and term infants based on objective data available at birth and the newborn’s clinical status. OBJECTIVES: To examine the effect of neonatal EOS risk prediction models on sepsis evaluations and antibiotic use and assess their safety in a large integrated health care system. DESIGN, SETTING, AND PARTICIPANTS: The study cohort includes 204 485 infants born at 35 weeks’ gestation or later at a Kaiser Permanente Northern California hospital from January 1, 2010, through December 31, 2015. The study compared 3 periods when EOS management was based on (1) national recommended guidelines (baseline period January 1, 2010, through November 31, 2012), (2) multivariable estimates of sepsis risk at birth (learning period December 1, 2012, through June 30, 2014), and (3) the multivariable risk estimate combined with the infant’s clinical condition in the first 24 hours after birth (EOS calculator period July 1, 2014, through December 31, 2015). MAIN OUTCOMES AND MEASURES: The primary outcome was antibiotic administration in the first 24 hours. Secondary outcomes included blood culture use, antibiotic administration between 24 and 72 hours, clinical outcomes, and readmissions for EOS. RESULTS: The study cohort included 204 485 infants born at 35 weeks’ gestation or later: 95 343 in the baseline period (mean SD age, 39.4 1.3 weeks; 46 651 male 51.0%; 37 007 white, non-Hispanic 38.8%), 52 881 in the learning period (mean SD age, 39.3 1.3 weeks; 27 067 male 51.2%; 20 175 white, non-Hispanic 38.2%), and 56 261 in the EOS calculator period (mean SD age, 39.4 1.3 weeks; 28 575 male 50.8%; 20 484 white, non-Hispanic 36.4%). In a comparison of the baseline period with the EOS calculator period, blood culture use decreased from 14.5% to 4.9% (adjusted difference, −7.7%; 95% CI, −13.1% to −2.4%). Empirical antibiotic administration in the first 24 hours decreased from 5.0% to 2.6% (adjusted difference, −1.8; 95% CI, −2.4% to −1.3%). No increase in antibiotic use occurred between 24 and 72 hours after birth; use decreased from 0.5% to 0.4% (adjusted difference, 0.0%; 95% CI, −0.1% to 0.2%). The incidence of culture-confirmed EOS was similar during the 3 periods (0.3% in the baseline period, 0.3% in the learning period, and 0.2% in the EOS calculator period). Readmissions for EOS (within 7 days of birth) were rare in all periods (5.2 per 100 000 births in the baseline period, 1.9 per 100 000 births in the learning period, and 5.3 per 100 000 births in the EOS calculator period) and did not differ statistically (P = .70). Incidence of adverse clinical outcomes, including need for inotropes, mechanical ventilation, meningitis, and death, was unchanged after introduction of the EOS calculator. CONCLUSIONS AND RELEVANCE: Clinical care algorithms based on individual infant estimates of EOS risk derived from a multivariable risk prediction model reduced the proportion of newborns undergoing laboratory testing and receiving empirical antibiotic treatment without apparent adverse effects.
Automated alerts in obstetrics Escobar, Gabriel J.; Schuler, Alejandro; Lee, Catherine
American journal of obstetrics and gynecology,
08/2021, Letnik:
225, Številka:
2
Journal Article
An increasing number of delivering women experience major morbidity and mortality. Limited work has been done on automated predictive models that could be used for prevention. Using only routinely ...collected obstetrical data, this study aimed to develop a predictive model suitable for real-time use with an electronic medical record. We used a retrospective cohort study design with split validation. The denominator consisted of women admitted to a delivery service. The numerator consisted of women who experienced a composite outcome that included both maternal (eg, uterine rupture, postpartum hemorrhage), fetal (eg, stillbirth), and neonatal (eg, hypoxic ischemic encephalopathy) adverse events. We employed machine learning methods, assessing model performance using the area under the receiver operator characteristic curve and number needed to evaluate. A total of 303,678 deliveries took place at 15 study hospitals between January 1, 2010, and March 31, 2018, and 4130 (1.36%) had ≥1 obstetrical complication. We employed data from 209,611 randomly selected deliveries (January 1, 2010, to March 31, 2017) as a derivation dataset and validated our findings on data from 52,398 randomly selected deliveries during the same time period (validation 1 dataset). We then applied our model to data from 41,669 deliveries from the last year of the study (April 1, 2017, to March 31, 2018 validation 2 dataset). Our model included 35 variables (eg, demographics, vital signs, laboratory tests, progress of labor indicators). In the validation 2 dataset, a gradient boosted model (area under the receiver operating characteristic curve or c statistic, 0.786) was slightly superior to a logistic regression model (c statistic, 0.778). Using an alert threshold of 4.1%, our final model would flag 16.7% of women and detect 52% of adverse outcomes, with a number needed to evaluate of 20.9 and 0.455 first alerts per day per 1000 annual deliveries. In conclusion, electronic medical record data can be used to predict obstetrical complications. The clinical utility of these automated models has not yet been demonstrated. To conduct interventions to assess whether using these models results in patient benefit, future work will need to focus on the development of clinical protocols suitable for use in interventions.
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