Code blue outcomes: Relation to the modified early warning score

Background: The Modified Early Warning Score (MEWS) is a physiological scoring system developed to identify patients in early stages of clinical deterioration and prevent delays in proper care. It consists of systolic blood pressure, heart rate, respiratory rate, temperature and level of consciousness. Higher MEWS are associated with greater mortality and need for intensive care. The relation of the MEWS to outcomes of in-hospital patient arrests, or codes, has not been extensively evaluated. If a clear relationship is established, it would provide a crucial tool in assisting doctors, patients, and families in goals of care discussions and clinical decisions. The intent of this study was to assess the relationship between the pre-code MEWS and the post-code outcome of patients on general inpatient units. Methods: This study sample included all adult general practice unit inpatients sustaining an arrest, classified as requiring CPR and/or intubation at a 802-bed tertiary care, urban, teaching hospital from July 2014 through June 2015. Data extracted included MEWS variables at 4 hours (hr) and 24hr before an event. Time windows of 2-6hr and 20-28hr were used for the 4hr and 24hr windows respectively. For missing points, the closest retrospective value was taken, even if outside the desired range. Level of consciousness (Alert, Verbal, Pain, Unresponsive) was inferred based on the nursing, therapy, nutritional notes or if the recorded pain score was \u3e0. The primary outcome was survival to hospital discharge. Univariate and multiple binary logistic regression models were used to assess the relationship between patient status at discharge and pre-code MEWS, adjusted for age, Charlson Comorbidity Index (CCI), and gender. Results: A total of 216 patients experienced arrests during the study period. At discharge, 53.1% of patients survived. Baseline demographics and MEWS are summarized in Table 1 for each outcome group. The odds ratio of death for the MEWS at 4hr was 0.89 (95% CI 0.75-1.07; p-value 0.24) (Figure 1). In a binary logistic regression model using 4hr MEWS, CCI, age, and gender, the outcome of death was not associated with the 4hr MEWS, but was with age (OR 0.98, 95% CI 0.96-1, p = 0.041). Conclusions: The MEWS has been associated with acute patient decompensation in the hospital, but in our study population, we did not find a relationship between the MEWS and patient survival at discharge following a code, even when adjusting for age and comorbidities. Events during the actual code event are known to affect survival, but were not evaluated in our study, which may contribute the negative result

Effect of follow-up appointments and admission unit on readmissions in patient with cancer on chemotherapy: A tertiary center experience.

Background: Readmissions are a huge burden on patients and organizations, especially in light of the newly emerging bundled payment systems. Many interventions have been proposed to reduce readmissions, including admitting patients to a cancer-specific unit (CSU) and scheduling follow-up appointments after discharge. Methods: We conducted a retrospective cohort study to identify the effect of admission unit and follow-up appointments on readmissions within 30 days among cancer patients receiving outpatient chemotherapy. We included unplanned admissions between July and October 2016 at Henry Ford Hospital. Results: There were 232 inpatient admissions. Of those, 73 (31%) were readmitted. The number of admissions to the CSU was 100 (43%) compared to 132 admissions (57%) to other general practice units (GPUs). Mean length of stay was 5.8 (1-29) days and 3.8 (1-30) days, respectively. Most common malignancies were hematological (27% and 22%) and gastrointestinal (26% and 19%). The most common reasons for admissions were infections (29% and 28%) and pain management (19% and 12%). Of patients who were admitted to the CSU, 24 (24%) were readmitted compared to 49 (37%) for other GPUs, OR 0.53 (95% CI:0.3-0.95, p = 0.033). Readmission rates were also calculated based on the type of appointment scheduled within 30 days of discharge (Primary care (PCP) and oncology, PCP only, Oncology only and neither). Odds ratios of readmission were calculated for the last three categories in comparison to having both appointments. Of admissions that had both appointments, 15 (23%) had a readmission within 30 days. Of admissions that had oncology only appointments, 39 (33%) had a readmission; OR 1.6 (95% CI: 0.8 to 3.3, p = 0.15). Of admissions that had a PCP only appointment, 6 (42%) had a readmission; OR 2.5 (95% CI: 0.7 to 8.3, p = 0.13) and of admissions that had neither appointments, 12 (57%) had a readmission; OR 4.4 (95% CI 1.6 to 12.6, p = 0.0049). Conclusions: Care for cancer patients is challenging as they carry a high risk of readmission, admitting cancer patients to cancer-specific units significantly reduces that risk. Having no follow-up appointments carries a high risk of readmission

Validation of the Arabic Version of the Edmonton Symptom Assessment System

Quality cancer care is a team effort. In addition, patients’ symptoms change over the course of treatment. As such, the Edmonton Symptom Assessment System (ESAS) is a simple tool designed to quickly monitor symptom change. Here, we present the results from a two-phase study aimed at validating the Arabic version of the ESAS (ESAS-A). Phase one involved the creation of two versions of the ESAS with both reverse and forward translations by bilingual, native Arabic speakers as well as evaluation by an expert panel. The reconciled version was then administered to 20 patients as a pilot from which to create the final version, which was then used with 244 patients. Phase two for the ESAS—involved an ESAS-based validation of 244 adults aged 18 years and older who were diagnosed with advanced cancer; then, further validation was completed in conjunction with two other symptom survey tools, the EORTC-Pal 15 and the HADS. The ESAS-A items possessed good internal consistency with an average Cronbach’s alpha of 0.84, ranging from 0.82 to 0.85. Moreover, the results of ESAS-A showed good agreement with those of EORTC QLQ- 15 PAL (r = 0.36 to 0.69) and HADS (r = 0.60 and 0.57) regarding anxiety and depression. We found the ESAS-A to be responsive to symptom change and a median time to completion of 3.73 min. The results of our study demonstrate that the ESAS-A is a reliable, valid, and feasible tool for the purposes of monitoring symptom change over the course of cancer treatment