Comparison of End-of-Life Care Practices Between Children With Complex Chronic Conditions and Neonates Dying in an ICU Versus Non-ICUs: A Substudy of the Pediatric End-of-LIfe CAre Needs in Switzerland (PELICAN) Project.

OBJECTIVES: To describe and compare characteristics of care provided at the end of life for children with chronic complex conditions and neonates who died in an ICU with those who died outside an ICU. DESIGN: Substudy of a nation-wide retrospective chart review. SETTING: Thirteen hospitals, including 14 pediatric and neonatal ICUs, two long-term institutions, and 10 community-based organizations in the three language regions of Switzerland. PATIENTS: One hundred forty-nine children (0-18 yr) who died in the years 2011 or 2012. Causes of death were related to cardiac, neurologic, oncological, or neonatal conditions. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Demographic and clinical characteristics, therapeutic procedures, circumstances of death, and patterns of decisional processes were extracted from the medical charts. Ninety-three (62%) neonates (median age, 4 d) and children (median age, 23 mo) died in ICU, and 56 (38%) with a median age of 63 months outside ICU. Generally, ICU patients had more therapeutic and invasive procedures, compared with non-ICU patients. Changes in treatment plan in the last 4 weeks of life, such as do-not-resuscitate orders occurred in 40% of ICU patients and 25% of non-ICU patients (p < 0.001). In the ICU, when decision to withdraw life-sustaining treatment was made, time to death in children and newborns was 4:25 and 3:00, respectively. In institutions where it was available, involvement of specialized pediatric palliative care services was recorded in 15 ICU patients (43%) and in 18 non-ICU patients (78%) (p = 0.008). CONCLUSIONS: This nation-wide study demonstrated that patients with a complex chronic condition who die in ICU, compared with those who die outside ICU, are characterized by fast changing care situations, including when to withdraw life-sustaining treatment. This highlights the importance of early effective communication and shared decision making among clinicians and families.

Intra-rater and inter-rater reliability of a medical record abstraction study on transition of care after childhood cancer.

BACKGROUND: The abstraction of data from medical records is a widespread practice in epidemiological research. However, studies using this means of data collection rarely report reliability. Within the Transition after Childhood Cancer Study (TaCC) which is based on a medical record abstraction, we conducted a second independent abstraction of data with the aim to assess a) intra-rater reliability of one rater at two time points; b) the possible learning effects between these two time points compared to a gold-standard; and c) inter-rater reliability. METHOD: Within the TaCC study we conducted a systematic medical record abstraction in the 9 Swiss clinics with pediatric oncology wards. In a second phase we selected a subsample of medical records in 3 clinics to conduct a second independent abstraction. We then assessed intra-rater reliability at two time points, the learning effect over time (comparing each rater at two time-points with a gold-standard) and the inter-rater reliability of a selected number of variables. We calculated percentage agreement and Cohen's kappa. FINDINGS: For the assessment of the intra-rater reliability we included 154 records (80 for rater 1; 74 for rater 2). For the inter-rater reliability we could include 70 records. Intra-rater reliability was substantial to excellent (Cohen's kappa 0-6-0.8) with an observed percentage agreement of 75%-95%. In all variables learning effects were observed. Inter-rater reliability was substantial to excellent (Cohen's kappa 0.70-0.83) with high agreement ranging from 86% to 100%. CONCLUSIONS: Our study showed that data abstracted from medical records are reliable. Investigating intra-rater and inter-rater reliability can give confidence to draw conclusions from the abstracted data and increase data quality by minimizing systematic errors.