Kaur, Harsheen

ABSTRACT Background Implementation of checklists has been shown to be effective in improving patient safety. This study aims to evaluate the effectiveness of implementation of a checklist for daily care processes into clinical practice of pediatric intensive care units (PICUs) with limited resources. Methods Prospective before–after study in eight PICUs from China, Congo, Croatia, Fiji, and India after implementation of a daily checklist into the ICU rounds. Results Seven hundred and thirty-five patients from eight centers were enrolled between 2015 and 2017. Baseline stage had 292 patients and post-implementation 443. The ICU length of stay post-implementation decreased significantly [9.4 (4–15.5) vs. 7.3 (3.4–13.4) days, p = 0.01], with a nominal improvement in the hospital length of stay [15.4 (8.4–25) vs. 12.6 (7.5–24.4) days, p = 0.055]. The hospital mortality and ICU mortality between baseline group and post-implementation group did not show a significant difference, 14.4% vs. 11.3%; p = 0.22 for each. There was a variable impact of checklist implementation on adherence to various processes of care recommendations. A decreased exposure in days was noticed for; mechanical ventilation from 42.6% to 33.8%, p < 0.01; central line from 31.3% to 25.3%, p < 0.01; and urinary catheter from 30.6% to 24.4%, p < 0.01. Although there was an increased utilization of antimicrobials (89.9–93.2%, p < 0.01). Conclusions Checklists for the treatment of acute illness and injury in the PICU setting marginally impacted the outcome and processes of care. The intervention led to increasing adherence with guidelines in multiple ICU processes and led to decreased length of stay.

Abstract Background We assessed glioma incidence and disparities in postglioma survival rate in the Olmsted County, Minnesota, population. Methods This population-based study assessed the incidence of pathologically confirmed primary gliomas between January 1, 1995, and December 31, 2014. Age- and sex-adjusted incidence rates per 100 000 person-years were calculated and standardized to the US white 2010 population. We compared incidence trends of glioma during our study period with previously published Olmsted County data from 1950 to 1990. We assessed postglioma survival rates among individuals with different socioeconomic status (SES), which was measured by a validated individual HOUsing-based SES index (HOUSES). Results We identified 135 incident glioma cases (93% white) with 20 pediatric (50% female) and 115 adult cases (44% female). Overall incidence rate during our study period, 5.51 per 100 000 person-years (95% CI: 4.56-6.46), showed no significant changes and was similar to that seen in 1950 to 1990, 5.5 per 100 000 person-years. The incidence of pediatric (age < 20 years) glioma was 2.49 (95% CI: 1.40-3.58), whereas adult glioma incidence was 6.47 (95% CI: 5.26-7.67). Among those with grade II to IV gliomas, individuals with lower SES (< median HOUSES) had significantly lower 5-year survival rates compared to those with higher SES, adjusted hazard ratio 1.61 (95% CI: 1.01-2.85). Conclusion In a well-defined North American population, long-term glioma incidence appears stable since 1950. Significant socioeconomic disparities exist for postglioma survival.

Objectives Literature suggests an inconsistent, but largely inverse, association between asthma and risk of glioma, which is primarily due to methodological inconsistency in sampling frame and ascertainment of asthma. The objective of the study was to clarify the association between asthma and risk of glioma by minimising methodological biases (eg, recall and detection bias). Design A population-based case–control study. Setting General population in Olmsted County, Minnesota, USA. Participants All eligible biopsy-proven incident glioma cases (1995–2014) and two sets of controls among residents matched to age and sex (first set: community controls without glioma; second set: MRI-negative controls from the same community). Methods The predetermined asthma criteria via medical record review were applied to ascertain asthma status of cases and controls. History of asthma prior to index date was compared between glioma cases and their matched controls using conditional logistic regression models. Propensity score for asthma status was adjusted for multivariate analysis. Results We enrolled 135 glioma cases (median age at index date: 53 years) and 270 controls. Of the cases, 21 had a history of asthma (16%), compared with 36 of MRI controls (27%) (OR (95% CI) 0.48 (0.26 to 0.91), p=0.03). With MRI controls, an inverse association between asthma and risk of glioma persisted after adjusting for the propensity score for asthma status, but did not reach statistical significance probably due to the lack of statistical power (OR (95% CI) 0.48 (0.21 to 1.09); p=0.08). Based on comparison of characteristics of controls and cases, community controls seem to be more susceptible to a detection bias. Conclusions While differential detection might account for the association between asthma and risk of glioma, asthma may potentially pose a protective effect on risk of glioma. Our study results need to be replicated by a larger study.

Objective Two pertussis outbreaks occurred in Olmsted County, Minnesota, during 2004–2005 and 2012 (5–10 times higher than other years), with significantly higher incidence than for the State. We aimed to assess whether there were similar spatio-temporal patterns between the two outbreaks. Setting Olmsted County, Minnesota, USA Participants We conducted a population-based retrospective cohort study of all Olmsted County residents during the 2004–2005 and 2012 outbreaks, including laboratory-positive pertussis cases. Primary outcome measure For each outbreak, we estimated (1) age-specific incidence rate using laboratory-positive pertussis cases (numerator) and the Rochester Epidemiology Project Census (denominator), a medical record-linkage system for virtually all Olmsted County residents, and (2) pertussis case density using kernel density estimation to identify areas with high case density. To account for population size, we calculated relative difference of observed density and expected density based on age-specific incidence. Results We identified 157 and 195 geocoded cases in 2004–2005 and 2012, respectively. Incidence was the highest among adolescents (ages 11 to <14 years) for both outbreaks (9.6 and 7.9 per 1000). The 2004–2005 pertussis outbreak had higher incidence in winter (52% of cases) versus summer in 2012 (53%). We identified a consistent area with higher incidence at the beginning (ie, first quartile) of two outbreaks, but it was inconsistent for later quartiles. The relative difference maps for the two outbreaks suggest a greater role of neighbourhood population size in 2012 compared with 2004–2005. Conclusions Comparing spatio-temporal patterns between two pertussis outbreaks identified a consistent geographical area with higher incidence of pertussis at the beginning of outbreaks in this community. This finding can be tested in future outbreaks, and, if confirmed, can be used for identifying epidemiological risk factors clustered in such areas for geographically targeted intervention.

Objective: No risk prediction model is currently available to measure patient’s probability for readmission to the pediatric intensive care unit (PICU). This retrospective case–control study was designed to assess the applicability of an adult risk prediction score (Stability and Workload Index for Transfer [SWIFT]) and to create a pediatric version (PRediction Of PICU Early Readmissions [PROPER]). Design: Eighty-six unplanned early (<48 hours) PICU readmissions from January 07, 2007, to June 30, 2014, were compared with 170 random controls. Patient- and disease-specific data and PICU workload factors were compared across the 2 groups. Factors statistically significant on multivariate analysis were included in the creation of the risk prediction model. The SWIFT scores were calculated for cases and controls and compared for validation. Results: Readmitted patients were younger, weighed less, and were more likely to be admitted from the emergency department. There were no differences in gender, race, or admission Pediatric Index of Mortality scores. A higher proportion of patients in the readmission group had a Pediatric Cerebral Performance Category in the moderate to severe disability category. Cases and controls did not differ with respect to staff workload at discharge or discharge day of the week; there was a much higher proportion of patients on supplemental oxygen in the readmission group. Only 2 of 5 categories in the SWIFT model were significantly different, and although the median SWIFT score was significantly higher in the readmissions group, the model discriminated poorly between cases and controls (area under the curve: 0.613). A 7-category PROPER score was created based on a multiple logistic regression model. Sensitivity of this model (score ≥12) for the detection of readmission was 81% with a positive predictive value of 0.50. Conclusion: We have created a preliminary model for predicting patients at risk of early readmissions to the PICU from the hospital floor. The SWIFT score is not applicable for predicting the risk for pediatric population.