Citations

Objective Analyzing pressure injury (PI) risk factors is complex because of multiplicity of associated factors and the multidimensional nature of this injury. The main objective of this study was to identify patients at risk of developing PI. Method Prediction performances of multiple popular supervised learning were tested. Together with the typical steps of a machine learning project, steps to prevent bias were carefully conducted, in which analysis of correlation covariance, outlier removal, confounding analysis, and cross-validation were used. Result The most accurate model reached an area under receiver operating characteristic curve of 99.7%. Ten-fold cross-validation was used to ensure that the results were generalizable. Random forest and decision tree had the highest prediction accuracy rates of 98%. Similar accuracy rate was obtained on the validation cohort. Conclusions We developed a prediction model using advanced analytics to predict PI in at-risk hospitalized patients. This will help address appropriate interventions before the patients develop a PI.

Objective: To evaluate the impact of health information technology (HIT) for early detection of patient deterioration on patient mortality and length of stay (LOS) in acute care hospital settings. Data Sources: We searched MEDLINE and Epub Ahead of Print, In-Process & Other Non-Indexed Citations and Daily, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Scopus from 1990 to January 19, 2021. Study Selection: We included studies that enrolled patients hospitalized on the floor, in the ICU, or admitted through the emergency department. Eligible studies compared HIT for early detection of patient deterioration with usual care and reported at least one end point of interest: hospital or ICU LOS or mortality at any time point. Data Extraction: Study data were abstracted by two independent reviewers using a standardized data extraction form. Data Synthesis: Random-effects meta-analysis was used to pool data. Among the 30 eligible studies, seven were randomized controlled trials (RCTs) and 23 were pre-post studies. Compared with usual care, HIT for early detection of patient deterioration was not associated with a reduction in hospital mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT interventions demonstrated a significant association with improved hospital mortality for the entire study cohort (odds ratio, 0.78 [95% CI, 0.70–0.87]) and reduced hospital LOS overall. Conclusions: HIT for early detection of patient deterioration in acute care settings was not significantly associated with improved mortality or LOS in the meta-analyses of RCTs. In the meta-analyses of pre-post studies, HIT was associated with improved hospital mortality and LOS; however, these results should be interpreted with caution. The differences in patient outcomes between the findings of the RCTs and pre-post studies may be secondary to confounding caused by unmeasured improvements in practice and workflow over time.

Background: It is known that hypokalemia, hypocalcemia and hypomagnesemia cause a prolonged QTc interval and are associated with an increased risk of ventricular arrhythmias. However, there are limited data on optimal serum electrolytes for patients admitted to the coronary care unit (CCU). Objective: Here, we aimed to evaluate the relationship between serum potassium, ionized calcium and magnesium levels and in-hospital mortality in the CCU setting. Methods: This retrospective cohort study included 8,498 consecutive patients admitted to the CCU at an academic tertiary medical center from 2004 through 2013. The mean of serum electrolytes was categorized as <3.5, 3.5-<4.0, 4.0-<4.5 (reference), 4.5-<5.0, and ≥5.0 mEq/L for potassium; <4.4, 4.4-<4.6, 4.6-<4.8, 4.8-<5.0 (reference), and ≥5.0 mg/dL for ionized calcium; and <1.8, 1.8-<2.0, 2.0-<2.2 (reference), 2.2-<2.4, and ≥2.4 mg/dL for magnesium. Multivariate logistic regression analysis was used to determine the association between the mean of serum electrolytes and in-hospital mortality. Results: Of the 8,498 patients, 599 (7%) died during hospitalization. Relationship between mean serum potassium (U-shaped), ionized calcium (inverted J-shape) and magnesium (J-shaped) and in-hospital mortality was present in Figure. Serum potassium levels <3.5, 4.5-<5.0 and ≥5.0 mEq/L were independently associated with an increased risk of in-hospital mortality (adjusted OR 1.7 95% CI 1.1-2.5, OR 1.7 95% CI 1.3-2.2, and OR 3.4 95% CI 2.4-4.8, respectively), compared to the reference group. As for serum ionized calcium, the levels of <4.4, 4.4-<4.6, and 4.6-<4.8 mg/dL independently increased risk of mortality (adjusted OR 3.0 95% CI 2.3-4.0, OR 2.0 95% CI 1.5-2.6, and OR 1.4 95% CI, 1.1-1.8, respectively). As for serum magnesium, an only level above 2.4 mg/dL was associated with an increased mortality risk (OR 1.6 95% CI, 1.1-2.2). Conclusions: In the CCU setting, serum potassium 3.5-<4.5 mEq/L, ionized calcium ≥5.0 mg/dL and magnesium <2.4 mg/dL are associated with the lowest in-hospital mortality. These findings do not support supratherapeutic repletion of potassium and magnesium.

OBJECTIVES: Administrative and clinical efforts to improve hospital mortality and intensive care utilization commonly focus on patient rescue, where deteriorating patients are systematically identified and intervened upon. Patient rescue is known to depend on hospital context inclusive of technologic environment, structural features, and hospital organizational behavioral features. With widespread adoption of electronic medical records, early warning score (EWS) systems, which assign points to clinical data elements, are increasingly promoted as a tool for timely patient rescue by referencing their prediction of patient deterioration. We describe the extent to which EWS intervention studies describe the hospital environment of the intervention—details that would be critical for hospital leaders attempting to determine the real-world utility of EWSs in their own hospitals. DATA SOURCES: We searched CINAHL, PubMed, and Scopus databases for English language EWS implementation research published between 2009 and 2021 in adult medical-surgical inpatients. STUDY SELECTION: Studies including pediatric, obstetric, psychiatric, prehospital, outpatient, step-down, or ICU patients were excluded. DATA EXTRACTION: Two investigators independently reviewed titles/abstracts for eligibility based on prespecified exclusion criteria. DATA SYNTHESIS: We identified 1,434 studies for title/abstract screening. In all, 352 studies underwent full-text review and 21 studies were summarized. The 21 studies (18 before-and-after, three randomized trials) detailed 1,107,883 patients across 54 hospitals. Twelve reported the staff composition of an EWS response team. Ten reported the proportion of surgical patients. One reported nursing ratios; none reported intensive care staffing with in-house critical-care physicians. None measured changes in bed utilization or availability. While 16 qualitatively described resources for education/technologic implementation, none estimated costs. None described workforce composition such as team stability or culture of safety in the hospitals. CONCLUSIONS: Despite hundreds of EWS-related publications, most do not report details of hospital context that would inform decisions about real-world EWS adoption. To make informed decisions about whether EWS implementation improves hospital quality, decision-makers may require alternatives such as peer networks and implementation pilots nested within local health systems.

Abstract Background The amount of time that health care clinicians (physicians and nurses) spend interacting with the electronic health record is not well understood. Objective This study aimed to evaluate the time that health care providers spend interacting with electronic health records (EHR). Methods Data are retrieved from Ovid MEDLINE(R) and Epub Ahead of Print, In-Process and Other Non-Indexed Citations and Daily, (Ovid) Embase, CINAHL, and SCOPUS. Study Eligibility Criteria Peer-reviewed studies that describe the use of EHR and include measurement of time either in hours, minutes, or in the percentage of a clinician's workday. Papers were written in English and published between 1990 and 2021. Participants All physicians and nurses involved in inpatient and outpatient settings. Study Appraisal and Synthesis Methods A narrative synthesis of the results, providing summaries of interaction time with EHR. The studies were rated according to Quality Assessment Tool for Studies with Diverse Designs. Results Out of 5,133 de-duplicated references identified through database searching, 18 met inclusion criteria. Most were time-motion studies (50%) that followed by logged-based analysis (44%). Most were conducted in the United States (94%) and examined a clinician workflow in the inpatient settings (83%). The average time was nearly 37% of time of their workday by physicians in both inpatient and outpatient settings and 22% of the workday by nurses in inpatient settings. The studies showed methodological heterogeneity. Conclusion This systematic review evaluates the time that health care providers spend interacting with EHR. Interaction time with EHR varies depending on clinicians' roles and clinical settings, computer systems, and users' experience. The average time spent by physicians on EHR exceeded one-third of their workday. The finding is a possible indicator that the EHR has room for usability, functionality improvement, and workflow optimization.

ABSTRACT Objectives The objectives of this study were to test in real time a Trauma Triage, Treatment, and Training Decision Support (4TDS) machine learning (ML) model of shock detection in a prospective silent trial, and to evaluate specificity, sensitivity, and other estimates of diagnostic performance compared to the gold standard of electronic medical records (EMRs) review. Design We performed a single-center diagnostic performance study. Patients and setting A prospective cohort consisted of consecutive patients aged 18 years and older who were admitted from May 1 through September 30, 2020 to six Mayo Clinic intensive care units (ICUs) and five progressive care units. Measurements and main results During the study time, 5,384 out of 6,630 hospital admissions were eligible. During the same period, the 4TDS shock model sent 825 alerts and 632 were eligible. Among 632 hospital admissions with alerts, 287 were screened positive and 345 were negative. Among 4,752 hospital admissions without alerts, 78 were screened positive and 4,674 were negative. The area under the receiver operating characteristics curve for the 4TDS shock model was 0.86 (95% CI 0.85-0.87%). The 4TDS shock model demonstrated a sensitivity of 78.6% (95% CI 74.1-82.7%) and a specificity of 93.1% (95% CI 92.4-93.8%). The model showed a positive predictive value of 45.4% (95% CI 42.6-48.3%) and a negative predictive value of 98.4% (95% CI 98-98.6%). Conclusions We successfully validated an ML model to detect circulatory shock in a prospective observational study. The model used only vital signs and showed moderate performance compared to the gold standard of clinician EMR review when applied to an ICU patient cohort.