Israel Medical Association Journal

Background: The coronavirus disease 2019 (COVID-19) pandemic resulted in repeated surges of patients, sometimes challenging triage protocols and appropriate control of patient flow. Available models, such as the National Early Warning Score (NEWS), have shown significant limitations. Still, they are used by some centers to triage COVID-19 patients due to the lack of better tools.

Objectives: To establish a practical and automated triage tool based on readily available clinical data to rapidly determine a distinction between patients who are prone to respiratory failure.

Methods: The electronic medical records of COVID-19 patients admitted to the Sheba Medical Center March–April 2020 were analyzed. Population data extraction and exploration were conducted using a MDClone (Israel) big data platform. Patients were divided into three groups: non-intubated, intubated within 24 hours, and intubated after 24 hours. The NEWS and our model where applied to all three groups and a best fit prediction model for the prediction of respiratory failure was established.

Results: The cohort included 385 patients, 42 of whom were eventually intubated, 15 within 24 hours or less. The NEWS score was significantly lower for the non-intubated patients compared to the two other groups. Our improved model, which included NEWS elements combined with other clinical data elements, showed significantly better performance. The model's receiver operating characteristic curve had area under curve (AUC) of 0.92 with of sensitivity 0.81, specificity 0.89, and negative predictive value (NPV) 98.4% compared to AUC of 0.63 with NEWS. As patients deteriorate and require further support with supplemental O₂, the need for re-triage emerges. Our model was able to identify those patients on supplementary O₂ prone to respiratory failure with an AUC of 0.86 sensitivity 0.95, and specificity 0.7 NPV 98.9%, whereas NEWS had an AUC of 0.76. For both groups positive predictive value was approximately 35%.

Conclusions: Our model, based on readily available and simple clinical parameters, showed an excellent ability to predict negative outcome among patients with COVID-19 and therefore might be used as an initial screening tool for patient triage in emergency departments and other COVID-19 specific areas of the hospital.

Background: Anastomotic leakage (AL) is a major complication following colorectal surgery, with many risk factors established to date. The incidence of AL varies in the medical literature and is dependent on research inclusion criteria and diagnostic criteria.

Objectives: To determine the incidence of and the potential risk factors for AL following colorectal surgery at a single academic medical center.

Methods: We retrospectively reviewed all operative reports of colorectal procedures that included bowel resection and primary bowel anastomosis performed at Sheba Medical Center during 2012. AL was defined according to the 1991 United Kingdom Surgical Infection Study Group criteria. Data were assessed for leak incidence within 30 days. In addition, 17 possible risk factors for leakage were analyzed. A literature review was conducted.

Results: This cohort study comprised 260 patients, and included 261 procedures performed during the study period. The overall leak rate was 8.4%. In a univariate analysis, male sex (odds ratio [OR] 3.37, 95% confidence interval [95%CI] 1.21–9.43), pulmonary disease (OR 3.99, 95%CI 1.49–10.73), current or past smoking (OR 2.93, 95%CI 1.21–7.10), and American Society of Anesthesiologist score ≥ 3 (OR 3.08, 95%CI 1.16–8.13) were associated with an increased risk for anastomotic leakage. In a multivariate analysis, male gender (OR 3.62, 95%CI 1.27–10.33) and pulmonary disease (OR 4.37, 95%CI 1.58–12.10) were associated with a greater risk.

Conclusions: The incidence of AL in the present study is similar to that found in comparable series. Respiratory co-morbidity and male sex were found to be the most significant risk factors.

Background: Adverse drug events (ADEs) are a major cause of morbidity and mortality worldwide. Hence, identifying and monitoring ADEs is of utmost importance. The Trigger Tool introduced by the Institute of Healthcare Improvement in the United States has been used in various countries worldwide, but has yet to be validated in Israel.

Objective: To validate the international Trigger Tool in Israel and to compare the results with those generated in various countries.

Methods: A retrospective descriptive correlative analysis surveying four general hospitals in Israel from different geographical regions was conducted. Patient medical charts (n=960) were screened for 17 established triggers and confirmed for the presence of an ADE. Trigger incidence was compared to the actual ADE rate. Further comparison among countries was conducted using published literature describing Trigger Tool validation in various countries.

Results: A total of 421 triggers in 279 hospitalizations were identified, of which 75 ADEs in 72 hospitalizations (7.5%) were confirmed. In addition, two ADEs were identified by chart review only. Mean positive predictive value was 17.81% and overall sensitivity was 97%. We found 1.54 ADEs for every 100 hospitalization days, 7.8 ADEs per 100 admissions, and 1.81 ADEs for every 1000 doses of medication. Of the 77 ADEs identified, 22.7% were defined as preventable.

Conclusions: Our results support the Trigger Tool validity in Israel as a standardized method. Further studies should evaluate between hospital and region differences in ADE rate, in particular for the preventable events.

Response to the authors of the open letter to the people in Gaza.