Prevalence of ICU delirium in postoperative pediatric cardiac surgery patients

OBJECTIVES: The objective of this study was to determine the prevalence of ICU delirium in children less than 18 years old that underwent cardiac surgery within the last 30 days. The secondary aim of the study was to identify risk factors associated with ICU delirium in postoperative pediatric cardiac surgical patients. DESIGN: A 1-day, multicenter point-prevalence study of delirium in pediatric postoperative cardiac surgery patients. SETTING: Twenty-seven pediatric cardiac and general critical care units caring for postoperative pediatric cardiac surgery patients in North America. PATIENTS: All children less than 18 years old hospitalized in the cardiac critical care units at 06:00 on a randomly selected, study day. INTERVENTIONS: Eligible children were screened for delirium using the Cornell Assessment of Pediatric Delirium by the study team in collaboration with the bedside nurse. MEASUREMENT AND MAIN RESULTS: Overall, 181 patients were enrolled and 40% (n = 73) screened positive for delirium. There were no statistically significant differences in patient demographic information, severity of defect or surgical procedure, past medical history, or postoperative day between patients screening positive or negative for delirium. Our bivariate analysis found those patients screening positive had a longer duration of mechanical ventilation (12.8 vs 5.1 d; p = 0.02); required more vasoactive support (55% vs 26%; p = 0.0009); and had a higher number of invasive catheters (4 vs 3 catheters; p = 0.001). Delirium-positive patients received more total opioid exposure (1.80 vs 0.36 mg/kg/d of morphine equivalents; p \u3c 0.001), did not have an ambulation or physical therapy schedule (p = 0.02), had not been out of bed in the previous 24 hours (p \u3c 0.0002), and parents were not at the bedside at time of data collection (p = 0.008). In the mixed-effects logistic regression analysis of modifiable risk factors, the following variables were associated with a positive delirium screen: 1) pain score, per point increase (odds ratio, 1.3; 1.06-1.60); 2) total opioid exposure, per mg/kg/d increase (odds ratio, 1.35; 1.06-1.73); 3) SBS less than 0 (odds ratio, 4.01; 1.21-13.27); 4) pain medication or sedative administered in the previous 4 hours (odds ratio, 3.49; 1.32-9.28); 5) no progressive physical therapy or ambulation schedule in their medical record (odds ratio, 4.40; 1.41-13.68); and 6) parents not at bedside at time of data collection (odds ratio, 2.31; 1.01-5.31). CONCLUSIONS: We found delirium to be a common problem after cardiac surgery with several important modifiable risk factors

Automated syndrome diagnosis by three-dimensional facial imaging.

PurposeDeep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.MethodsWe analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.ResultsUnrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.ConclusionDeep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance

Automated syndrome diagnosis by three-dimensional facial imaging.

PurposeDeep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30-40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.MethodsWe analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.ResultsUnrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.ConclusionDeep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of "unaffected" relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance

Families as Partners in Hospital Error and Adverse Event Surveillance

ImportanceMedical errors and adverse events (AEs) are common among hospitalized children. While clinician reports are the foundation of operational hospital safety surveillance and a key component of multifaceted research surveillance, patient and family reports are not routinely gathered. We hypothesized that a novel family-reporting mechanism would improve incident detection.ObjectiveTo compare error and AE rates (1) gathered systematically with vs without family reporting, (2) reported by families vs clinicians, and (3) reported by families vs hospital incident reports.Design, setting, and participantsWe conducted a prospective cohort study including the parents/caregivers of 989 hospitalized patients 17 years and younger (total 3902 patient-days) and their clinicians from December 2014 to July 2015 in 4 US pediatric centers. Clinician abstractors identified potential errors and AEs by reviewing medical records, hospital incident reports, and clinician reports as well as weekly and discharge Family Safety Interviews (FSIs). Two physicians reviewed and independently categorized all incidents, rating severity and preventability (agreement, 68%-90%; κ, 0.50-0.68). Discordant categorizations were reconciled. Rates were generated using Poisson regression estimated via generalized estimating equations to account for repeated measures on the same patient.Main outcomes and measuresError and AE rates.ResultsOverall, 746 parents/caregivers consented for the study. Of these, 717 completed FSIs. Their median (interquartile range) age was 32.5 (26-40) years; 380 (53.0%) were nonwhite, 566 (78.9%) were female, 603 (84.1%) were English speaking, and 380 (53.0%) had attended college. Of 717 parents/caregivers completing FSIs, 185 (25.8%) reported a total of 255 incidents, which were classified as 132 safety concerns (51.8%), 102 nonsafety-related quality concerns (40.0%), and 21 other concerns (8.2%). These included 22 preventable AEs (8.6%), 17 nonharmful medical errors (6.7%), and 11 nonpreventable AEs (4.3%) on the study unit. In total, 179 errors and 113 AEs were identified from all sources. Family reports included 8 otherwise unidentified AEs, including 7 preventable AEs. Error rates with family reporting (45.9 per 1000 patient-days) were 1.2-fold (95% CI, 1.1-1.2) higher than rates without family reporting (39.7 per 1000 patient-days). Adverse event rates with family reporting (28.7 per 1000 patient-days) were 1.1-fold (95% CI, 1.0-1.2; P = .006) higher than rates without (26.1 per 1000 patient-days). Families and clinicians reported similar rates of errors (10.0 vs 12.8 per 1000 patient-days; relative rate, 0.8; 95% CI, .5-1.2) and AEs (8.5 vs 6.2 per 1000 patient-days; relative rate, 1.4; 95% CI, 0.8-2.2). Family-reported error rates were 5.0-fold (95% CI, 1.9-13.0) higher and AE rates 2.9-fold (95% CI, 1.2-6.7) higher than hospital incident report rates.Conclusions and relevanceFamilies provide unique information about hospital safety and should be included in hospital safety surveillance in order to facilitate better design and assessment of interventions to improve safety

Families as Partners in Hospital Error and Adverse Event Surveillance

ImportanceMedical errors and adverse events (AEs) are common among hospitalized children. While clinician reports are the foundation of operational hospital safety surveillance and a key component of multifaceted research surveillance, patient and family reports are not routinely gathered. We hypothesized that a novel family-reporting mechanism would improve incident detection.ObjectiveTo compare error and AE rates (1) gathered systematically with vs without family reporting, (2) reported by families vs clinicians, and (3) reported by families vs hospital incident reports.Design, setting, and participantsWe conducted a prospective cohort study including the parents/caregivers of 989 hospitalized patients 17 years and younger (total 3902 patient-days) and their clinicians from December 2014 to July 2015 in 4 US pediatric centers. Clinician abstractors identified potential errors and AEs by reviewing medical records, hospital incident reports, and clinician reports as well as weekly and discharge Family Safety Interviews (FSIs). Two physicians reviewed and independently categorized all incidents, rating severity and preventability (agreement, 68%-90%; κ, 0.50-0.68). Discordant categorizations were reconciled. Rates were generated using Poisson regression estimated via generalized estimating equations to account for repeated measures on the same patient.Main outcomes and measuresError and AE rates.ResultsOverall, 746 parents/caregivers consented for the study. Of these, 717 completed FSIs. Their median (interquartile range) age was 32.5 (26-40) years; 380 (53.0%) were nonwhite, 566 (78.9%) were female, 603 (84.1%) were English speaking, and 380 (53.0%) had attended college. Of 717 parents/caregivers completing FSIs, 185 (25.8%) reported a total of 255 incidents, which were classified as 132 safety concerns (51.8%), 102 nonsafety-related quality concerns (40.0%), and 21 other concerns (8.2%). These included 22 preventable AEs (8.6%), 17 nonharmful medical errors (6.7%), and 11 nonpreventable AEs (4.3%) on the study unit. In total, 179 errors and 113 AEs were identified from all sources. Family reports included 8 otherwise unidentified AEs, including 7 preventable AEs. Error rates with family reporting (45.9 per 1000 patient-days) were 1.2-fold (95% CI, 1.1-1.2) higher than rates without family reporting (39.7 per 1000 patient-days). Adverse event rates with family reporting (28.7 per 1000 patient-days) were 1.1-fold (95% CI, 1.0-1.2; P = .006) higher than rates without (26.1 per 1000 patient-days). Families and clinicians reported similar rates of errors (10.0 vs 12.8 per 1000 patient-days; relative rate, 0.8; 95% CI, .5-1.2) and AEs (8.5 vs 6.2 per 1000 patient-days; relative rate, 1.4; 95% CI, 0.8-2.2). Family-reported error rates were 5.0-fold (95% CI, 1.9-13.0) higher and AE rates 2.9-fold (95% CI, 1.2-6.7) higher than hospital incident report rates.Conclusions and relevanceFamilies provide unique information about hospital safety and should be included in hospital safety surveillance in order to facilitate better design and assessment of interventions to improve safety