Individualized prediction of critical illness in older adults: Validation of an elders risk assessment model.

BACKGROUND: The electronic health record (EHR) presents new opportunities for the timely identification of patients at high risk of critical illness and the implementation of preventive strategies. This study aims to externally validate an EHR-based Elders Risk Assessment (ERA) score to identify older patients at high risk of future critical illness during a primary care visit. METHODS: This historical cohort study included patients aged ≥65 years who had primary care visits at Mayo Clinic Rochester, MN, between July 2019 and December 2021. The ERA score at the time of the primary care visit was used to predict critical illness, defined as death or ICU admission within 1 year of the visit. RESULTS: A total of 12,885 patients were included in the analysis. The median age at the time of the primary care visit was 75 years, with 44.6% being male. 93.7% of participants were White, and 64.2% were married. The median (25th, 75th percentile) ERA score was 4 (0, 9). 11.3% of study participants were admitted to the ICU or died within 1 year of the visit. The ERA score predicted critical illness within 1 year of a primary care visit with an area under the receiver operating characteristic curve of 0.84 (95% CI 0.83-0.85), which indicates good discrimination. An ERA score of 9 was identified as optimal for implementing and testing potential preventive strategies, with the odds ratio of having the primary outcome in patients with ERA score ≥9 being 11.33 (95%CI 9.98-12.87). CONCLUSIONS: This simple EHR-based risk assessment model can predict critical illness within 1 year of primary care visits in older patients. The findings of this study can serve as a basis for testing and implementation of preventive strategies to promote the well-being of older adults at risk of critical illness and its consequences.

Multifaceted Intervention to Improve Patient Safety Incident Reporting in Intensive Care Units.

OBJECTIVES: Patient safety incident reporting in our institution's intensive care units (ICUs) had fallen 30% below national benchmarks during the COVID-19 pandemic. Underreporting diminishes awareness of risks and precludes organizational learning from near misses. We aimed to increase the ICU number of patient safety incident reports by 30% from 27 to 35 reports/1000 patient-days without negatively impacting culture of safety as measured by patient-care staff surveys. METHODS: Single-institution prospective interventional study with 9 ICUs receiving a multifaceted intervention developed using quality improvement methodology during February-April 2022. Study intervention involved creation of patient safety peer-leadership role, feedback process, interactive dashboards for patient safety data, and education resources accessible via quick response codes. Primary outcome was patient safety incident reports/1000 patient-days. Intensive care unit patient-care staff culture of safety was assessed with surveys. RESULTS: Intensive care unit patient safety incident reporting increased by 48% after intervention (40 versus 27 reports/1000 patient-days [ P = 0.136]). Near misses were the most common incident report. Intensive care unit patient-care staff ratings of patient safety did not change; 80% rated patient safety as good or better after intervention versus 78% at baseline ( P = 0.465). However, significant improvement was observed for subcomponents related to learning culture and support for staff involved in patient safety incidents. Most reports (>80%) were submitted by nurses. CONCLUSIONS: This multifaceted quality improvement intervention increased patient safety incident reporting in the ICUs. Increases in ratings of learning culture and support for staff underline the importance of a well-functioning patient safety incident reporting system in an institutional culture of safety.

Comparative costs for critically ill patients with limited English proficiency versus English proficiency.

OBJECTIVES: To conduct comparative cost analysis of hospital care for critically ill patients with Limited English Proficiency (LEP) versus patients with English proficiency (controls). PATIENTS AND METHODS: We conducted a historical cohort study using propensity matching at Mayo Clinic Rochester, a quaternary care academic center. We included hospitalized patients who had at least one admission to ICU during a 10-year period between 1/1/2008-12/31/2017. RESULTS: Due to substantial differences in baseline characteristics of the groups, propensity matching for the covariates age, sex, race, ethnicity, APACHE 3 score, and Charlson Comorbidity score was used, and we achieved the intended balance. The final cohort included 80,404 patients, 4,246 with LEP and 76,158 controls. Patients with LEP had higher costs during hospital admission to discharge, with a mean cost difference of $3861 (95% CI $822 to $6900, p = 0.013) and also higher costs during index ICU admission to hospital discharge, with a mean cost difference of $3166 (95% CI $231 to $6101, p = 0.035). A propensity matched cohort including only those that survived showed those with LEP had significantly greater mean costs for all outcomes. Sensitivity analysis revealed that international patients with LEP had significantly greater overall hospital costs of $9,240 than patients with LEP who resided in the US (95% CI $3341 to $15,140, p = 0.002). CONCLUSION: This is the first study to demonstrate significantly higher costs for patients with LEP experiencing a critical illness. The causes for this may be increased healthcare utilization secondary to communication deficiencies that impede timely decision making about care.

A novel multimodal needs assessment to inform the longitudinal education program for an international interprofessional critical care team.

BACKGROUND: The current global pandemic has caused unprecedented strain on critical care resources, creating an urgency for global critical care education programs. Learning needs assessment is a core element of designing effective, targeted educational interventions. In theory, multimodal methods are preferred to assess both perceived and unperceived learning needs in diverse, interprofessional groups, but a robust design has rarely been reported. Little is known about the best approach to determine the learning needs of international critical care professionals. METHOD: We conducted multimodal learning needs assessment in a pilot group of critical care professionals in China using combined quantitative and qualitative methods. The assessments consisted of three phases: 1) Twenty statements describing essential entrustable professional activities (EPAs) were generated by a panel of critical care education experts using a Delphi method. 2) Eleven Chinese critical care professionals participating in a planned education program were asked to rank-order the statements according to their perceived learning priority using Q methodology. By-person factor analysis was used to study the typology of the opinions, and post-ranking focus group interviews were employed to qualitatively explore participants' reasoning of their rankings. 3) To identify additional unperceived learning needs, daily practice habits were audited using information from medical and nursing records for 3 months. RESULTS: Factor analysis of the rank-ordered statements revealed three learning need patterns with consensual and divergent opinions. All participants expressed significant interest in further education on organ support and disease management, moderate interest in quality improvement topics, and relatively low interest in communication skills. Interest in learning procedure/resuscitation skills varied. The chart audit revealed suboptimal adherence to several evidence-based practices and under-perceived practice gaps in patient-centered communication, daily assessment of antimicrobial therapy discontinuation, spontaneous breathing trial, and device discontinuation. CONCLUSIONS: We described an effective mixed-methods assessment to determine the learning needs of an international, interprofessional critical care team. The Q survey and focus group interviews prioritized and categorized perceived learning needs. The chart audit identified additional practice gaps that were not identified by the learners. Multimodal methods can be employed in cross-cultural scenarios to customize and better target medical education curricula.

Timing of ARDS Resolution (TARU): A Pragmatic Clinical Assessment of ARDS Resolution in the ICU.

PURPOSE: Lack of a pragmatic outcome measures for acute respiratory distress syndrome (ARDS) resolution is a barrier to meaningful interventional trials of novel treatments. We evaluated a pragmatic, electronic health record (EHR)-based approach toward the clinical assessment of a novel outcome measure: ICU ARDS resolution. METHODS: We conducted a retrospective observational cohort study evaluating adult patients with moderate-severe ARDS admitted to the medical intensive care unit (ICU) at Mayo Clinic in Rochester, MN, from January 2001 through December 2010. We compared the association of ICU ARDS resolution vs non-resolution with mortality. ICU ARDS resolution was defined as improvement in P/F > 200 for at least 48 h or (if arterial blood gas unavailable) SpO2:FiO2 (S/F) > 235, or discharge prior to 48 h from first P/F > 200 without subsequent decline in P/F, as documented in EHR. RESULTS: Of the 254 patients included, ICU ARDS resolution was achieved in 179 (70%). Hospital mortality was lower in patients who met ICU ARDS resolution criteria as compared to those who did not (23% vs. 41%, p < 0.01). After adjusting for age, gender, and illness severity, the patients who met ICU ARDS resolution criteria had lower odds of hospital mortality [odds ratio 0.47, 95% CI 0.25-0.86; p = 0.015]. CONCLUSION: The electronic health record-based pragmatic measure of ICU ARDS resolution is associated with patient outcomes and may serve as an intermediate outcome assessing novel mechanistic treatments.

Checklist for Early Recognition and Treatment of Acute Illness and Injury: An Exploratory Multicenter International Quality-Improvement Study in the ICUs With Variable Resources.

OBJECTIVES: To determine whether the "Checklist for Early Recognition and Treatment of Acute Illness and Injury" decision support tool during ICU admission and rounding is associated with improvements in nonadherence to evidence-based daily care processes and outcomes in variably resourced ICUs. DESIGN, SETTINGS, PATIENTS: This before-after study was performed in 34 ICUs (15 countries) from 2013 to 2017. Data were collected for 3 months before and 6 months after Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation. INTERVENTIONS: Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation using remote simulation training. MEASUREMENTS AND MAIN RESULTS: The coprimary outcomes, modified from the original protocol before data analysis, were nonadherence to 10 basic care processes and ICU and hospital length of stay. There were 1,447 patients in the preimplementation phase and 2,809 patients in the postimplementation phase. After adjusting for center effect, Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation was associated with reduced nonadherence to care processes (adjusted incidence rate ratio [95% CI]): deep vein thrombosis prophylaxis (0.74 [0.68-0.81), peptic ulcer prophylaxis (0.46 [0.38-0.57]), spontaneous breathing trial (0.81 [0.76-0.86]), family conferences (0.86 [0.81-0.92]), and daily assessment for the need of central venous catheters (0.85 [0.81-0.90]), urinary catheters (0.84 [0.80-0.88]), antimicrobials (0.66 [0.62-0.71]), and sedation (0.62 [0.57-0.67]). Analyses adjusted for baseline characteristics showed associations of Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation with decreased ICU length of stay (adjusted ratio of geometric means [95% CI]) 0.86 [0.80-0.92]), hospital length of stay (0.92 [0.85-0.97]), and hospital mortality (adjusted odds ratio [95% CI], 0.81 (0.69-0.95). CONCLUSIONS: A quality-improvement intervention with remote simulation training to implement a decision support tool was associated with decreased nonadherence to daily care processes, shorter length of stay, and decreased mortality.

An Innovative Individual-Level Socioeconomic Measure Predicts Critical Care Outcomes in Older Adults: A Population-Based Study.

BACKGROUND: Little is known about the impact of socioeconomic status (SES) as a key element of social determinants of health on intensive care unit (ICU) outcomes for adults. OBJECTIVE: We assessed whether a validated individual SES index termed HOUSES (HOUsing-based SocioEconomic status index) derived from housing features was associated with short-term outcomes of critical illness including ICU mortality, ICU-free days, hospital-free days, and ICU readmission. METHODS: We performed a population-based cohort study of adult patients living in Olmsted County, Minnesota, admitted to 7 intensive care units at Mayo Clinic from 2011 to 2014. We compared outcomes between the lowest SES group (HOUSES quartile 1 [Q1]) and the higher SES group (HOUSES Q2-4). We stratified the cohort based on age (<50 years old and ≥50 years old). RESULTS: Among 4134 eligible patients, 3378 (82%) patients had SES successfully measured by the HOUSES index. Baseline characteristics, severity of illness, and reason for ICU admission were similar among the different SES groups as measured by HOUSES except for larger number of intoxications and overdoses in younger patients from the lowest SES. In all adult patients, there were no overall differences in mortality, ICU-free days, hospital-free days, or ICU readmissions in patients with higher SES compared to lower SES. Among older patients (>50 years), those with higher SES (HOUSES Q2-4) compared to those with lower SES (HOUSES Q1) had lower mortality rates (hazard ratio = 0.72; 95% CI: 0.56-0.93; adjusted P = .01), increased ICU-free days (mean 1.08 days; 95% CI: 0.34-1.84; adjusted P = .004), and increased hospital-free days (mean 1.20 days; 95% CI: 0.45-1.96; adjusted P = .002). There were no differences in ICU readmission rates (OR = 0.74; 95% CI: 0.55-1.00; P = .051). CONCLUSION: Individual-level SES may be an important determinant or predictor of critical care outcomes in older adults. Housing-based socioeconomic status may be a useful tool for enhancing critical care research and practice.

Severity of illness assessment with application of the APACHE IV predicted mortality and outcome trends analysis in an academic cardiac intensive care unit.

PURPOSE: To assess trends in life support interventions and performance of the automated Acute Physiology and Chronic Health Evaluation (APACHE) IV model at mortality prediction compared with Oxford Acute Severity of Illness Score (OASIS) in a contemporary cardiac intensive care unit (CICU). METHODS AND MATERIALS: Retrospective analysis of adults (age ≥ 18 years) admitted to CICU from January 1, 2007, through December 31, 2015. Temporal trends were assessed with linear regression. Discrimination of each risk score for hospital mortality was assessed with use of area under the receiver operating characteristic curve (AUROC) values. Calibration was assessed with Hosmer-Lemeshow goodness-of-fit test. RESULTS: The study analyzed 10,004 patients. CICU and hospital mortality rates were 5.7% and 9.1%. APACHE IV predicted death had an AUROC of 0.82 (0.81-0.84) for hospital death, compared with 0.79 for OASIS (P < .05). Calibration was better for OASIS than APACHE IV. Increases were observed in CICU and hospital lengths of stay (both P < .001), APACHE IV predicted mortality (P = .007), Charlson Comorbidity Index (P < .001), noninvasive ventilation use (P < .001), and noninvasive ventilation days (P = .02). CONCLUSIONS: Contemporary CICU patients are increasingly ill, observed in upward trends in comorbid conditions and life support interventions. APACHE IV predicted death and OASIS showed good discrimination in predicting death in this population. APACHE IV and OASIS may be useful for benchmarking and quality improvement initiatives in the CICU, the former having better discrimination.

An Individual Housing-Based Socioeconomic Status Measure Predicts Advance Care Planning and Nursing Home Utilization.

BACKGROUND:: Socioeconomic status (SES) is an important determinant of disparities in health care and may play a role in end-of-life care and decision-making. The SES is difficult to retrospectively abstract from current electronic medical records and data sets. OBJECTIVE:: Using a validated SES measuring tool derived from home address, the HOUsing-based SocioEconomic Status index, termed HOUSES we wanted to determine whether SES is associated with differences in end-of-life care and decision-making. DESIGN/SETTING/PARTICIPANTS:: This cross-sectional study utilized a cohort of Olmsted County adult residents admitted to 7 intensive care units (ICUs) at Mayo Rochester between June 1, 2011, and May 31, 2014. MEASUREMENTS:: Multiple variables that reflect decision-making and care at end of life and during critical illness were evaluated, including presence of advance directives and discharge disposition. The SES was measured by individual housing-based SES index (HOUSES index; a composite index derived from real property as a standardized z-score) at the date of admission to the ICU which was then divided into 4 quartiles. The greater HOUSES, the higher SES, outcomes were adjusted for age, 24-hour Acute Physiology and Chronic Health Evaluation III score, sex, race/ethnicity, and insurance. RESULTS:: Among the eligible 4134 participants, the addresses of 3393 (82%) were successfully geocoded and formulated into HOUSES. The adjusted odds ratios comparing HOUSES 1 versus 2, 3, and 4 demonstrated lower likelihood of advance directives -0.77(95% CI: 0.63-0.93) and lower likelihood of discharge to home -0.60(95% CI: 1.0.5-0.72). CONCLUSION:: Lower SES, derived from a composite index of housing attributes, was associated with lower rates of advance directives and lower likelihood of discharge to home.

When to incorporate point-of-care ultrasound (POCUS) into the initial assessment of acutely ill patients: a pilot crossover study to compare 2 POCUS-assisted simulation protocols.

BACKGROUND: The purpose of this study was to determine the ideal timing for providers to perform point-of-care ultrasound (POCUS) with the least increase in workload. METHODS: We conducted a pilot crossover study to compare 2 POCUS-assisted evaluation protocols for acutely ill patients: sequential (physical examination followed by POCUS) vs parallel (POCUS at the time of physical examination). Participants were randomly assigned to 2 groups according to which POCUS-assisted protocol (sequential vs parallel) was used during simulated scenarios. Subsequently, the groups were crossed over to complete assessment by using the other POCUS-assisted protocol in the same patient scenarios. Providers' workloads, measured with the National Aeronautics and Space Administration Task Load Index (NASA-TLX) and time to complete patient evaluation, were compared between the 2 protocols. RESULTS: Seven providers completed 14 assessments (7 sequential and 7 parallel). The median (IQR) total NASA-TLX score was 30 (30-50) in the sequential and 55 (50-65) in the parallel protocol (P = .03), which suggests a significantly lower workload in the sequential protocol. When individual components of the NASA-TLX score were evaluated, mental demand and frustration level were significantly lower in the sequential than in the parallel protocol (40 [IQR, 30-60] vs 50 [IQR, 40-70]; P = .03 and 25 [IQR, 20-35] vs 60 [IQR, 45-85]; P = .02, respectively). The time needed to complete the assessment was similar between the sequential and parallel protocols (8.7 [IQR, 6-9] minutes vs 10.1 [IQR, 7-11] minutes, respectively; P = .30). CONCLUSIONS: A sequential POCUS-assisted protocol posed less workload to POCUS operators than the parallel protocol.

The Incremental Burden of Acute Respiratory Distress Syndrome: Long-term Follow-up of a Population-Based Nested Case-Control Study.

OBJECTIVE: To evaluate the long-term survival of patients at similar risk for hospital-acquired acute respiratory distress syndrome (ARDS) who did and did not develop ARDS. METHODS: We conducted long-term follow-up of a population-based nested case-control study in a consecutive cohort of adult Olmsted County, Minnesota, patients admitted from January 1, 2001, through December 31, 2010. Patients in whom ARDS developed during their hospital stay (cases) were matched to similar-risk patients without ARDS (controls) by 6 characteristics: age, sex, sepsis, high-risk surgery, ratio of oxygen saturation to fraction of inspired oxygen, and ARDS risk according to the Lung Injury Prediction Score. Hospital mortality, discharge disposition, and long-term survival were compared. RESULTS: Patients who developed hospital-acquired ARDS (n=400) had higher hospital mortality than at-risk controls (n=400) (35% vs 5%; P<.001). Among hospital survivors (252 matched pairs), ARDS cases were more likely to be discharged to rehabilitation (13% vs 4%) and long-term care (30% vs 15%) facilities, whereas more controls were discharged home (71% vs 41%). After discharge, differences in survival persisted beyond 90 days (adjusted hazard ratio [HR], 1.76; 95% CI, 1.2-2.5; P=.002) and 6 months (adjusted HR, 1.73; 95% CI, 1.2-2.6; P<.001). CONCLUSION: These results suggest that in a population-based matched case-control study of patients with similar characteristics at the time of hospital admission, those who developed hospital-acquired ARDS had worse long-term survival.

Predictive Value of the Sequential Organ Failure Assessment Score for Mortality in a Contemporary Cardiac Intensive Care Unit Population.

BACKGROUND: Optimal methods of mortality risk stratification in patients in the cardiac intensive care unit (CICU) remain uncertain. We evaluated the ability of the Sequential Organ Failure Assessment (SOFA) score to predict mortality in a large cohort of unselected patients in the CICU. METHODS AND RESULTS: Adult patients admitted to the CICU from January 1, 2007, to December 31, 2015, at a single tertiary care hospital were retrospectively reviewed. SOFA scores were calculated daily, and Acute Physiology and Chronic Health Evaluation (APACHE)-III and APACHE-IV scores were calculated on CICU day 1. Discrimination of hospital mortality was assessed using area under the receiver-operator characteristic curve values. We included 9961 patients, with a mean age of 67.5±15.2 years; all-cause hospital mortality was 9.0%. Day 1 SOFA score predicted hospital mortality, with an area under the receiver-operator characteristic curve value of 0.83; area under the receiver-operator characteristic curve values were similar for the APACHE-III score, and APACHE-IV predicted mortality (P>0.05). Mean and maximum SOFA scores over multiple CICU days had greater discrimination for hospital mortality (P<0.01). Patients with an increasing SOFA score from day 1 and day 2 had higher mortality. Patients with day 1 SOFA score <2 were at low risk of mortality. Increasing tertiles of day 1 SOFA score predicted higher long-term mortality (P<0.001 by log-rank test). CONCLUSIONS: The day 1 SOFA score has good discrimination for short-term mortality in unselected patients in the CICU, which is comparable to APACHE-III and APACHE-IV. Advantages of the SOFA score over APACHE include simplicity, improved discrimination using serial scores, and prediction of long-term mortality.

Prediction of critical illness in elderly outpatients using elder risk assessment: a population-based study.

RATIONALE: Identifying patients at high risk of critical illness is necessary for the development and testing of strategies to prevent critical illness. The aim of this study was to determine the relationship between high elder risk assessment (ERA) score and critical illness requiring intensive care and to see if the ERA can be used as a prediction tool to identify elderly patients at the primary care visit who are at high risk of critical illness. METHODS: A population-based historical cohort study was conducted in elderly patients (age >65 years) identified at the time of primary care visit in Rochester, MN, USA. Predictors including age, previous hospital days, and comorbid health conditions were identified from routine administrative data available in the electronic medical record. The main outcome was critical illness, defined as sepsis, need for mechanical ventilation, or death within 2 years of initial visit. Patients with an ERA score of 16 were considered to be at high risk. The discrimination of the ERA score was assessed using area under the receiver operating characteristic curve. RESULTS: Of the 13,457 eligible patients, 9,872 gave consent for medical record review and had full information on intensive care unit utilization. The mean age was 75.8 years (standard deviation ±7.6 years), and 58% were female, 94% were Caucasian, 62% were married, and 13% were living in nursing homes. In the overall group, 417 patients (4.2%) suffered from critical illness. In the 1,134 patients with ERA >16, 154 (14%) suffered from critical illness. An ERA score ≥16 predicted critical illness (odds ratio 6.35; 95% confidence interval 3.51-11.48). The area under the receiver operating characteristic curve was 0.75, which indicated good discrimination. CONCLUSION: A simple model based on easily obtainable administrative data predicted critical illness in the next 2 years in elderly outpatients with up to 14% of the highest risk population suffering from critical illness. This model can facilitate efficient enrollment of patients into clinical programs such as care transition programs and studies aimed at the prevention of critical illness. It also can serve as a reminder to initiate advance care planning for high-risk elderly patients. External validation of this tool in different populations may enhance its generalizability.

Clinical calculators in hospital medicine: Availability, classification, and needs.

OBJECTIVE: Clinical calculators are widely used in modern clinical practice, but are not generally applied to electronic health record (EHR) systems. Important barriers to the application of these clinical calculators into existing EHR systems include the need for real-time calculation, human-calculator interaction, and data source requirements. The objective of this study was to identify, classify, and evaluate the use of available clinical calculators for clinicians in the hospital setting. METHODS: Dedicated online resources with medical calculators and providers of aggregated medical information were queried for readily available clinical calculators. Calculators were mapped by clinical categories, mechanism of calculation, and the goal of calculation. Online statistics from selected Internet resources and clinician opinion were used to assess the use of clinical calculators. RESULTS: One hundred seventy-six readily available calculators in 4 categories, 6 primary specialties, and 40 subspecialties were identified. The goals of calculation included prediction, severity, risk estimation, diagnostic, and decision-making aid. A combination of summation logic with cutoffs or rules was the most frequent mechanism of computation. Combined results, online resources, statistics, and clinician opinion identified 13 most utilized calculators. CONCLUSION: Although not an exhaustive list, a total of 176 validated calculators were identified, classified, and evaluated for usefulness. Most of these calculators are used for adult patients in the critical care or internal medicine settings. Thirteen of 176 clinical calculators were determined to be useful in our institution. All of these calculators have an interface for manual input.

Cost effectiveness of intensive care in a low resource setting: A prospective cohort of medical critically ill patients.

AIM: To calculate cost effectiveness of the treatment of critically ill patients in a medical intensive care unit (ICU) of a middle income country with limited access to ICU resources. METHODS: A prospective cohort study and economic evaluation of consecutive patients treated in a recently established medical ICU in Sarajevo, Bosnia and Herzegovina. A cost utility analysis of the intensive care of critically ill patients compared to the hospital ward treatment from the perspective of the health care system was subsequently performed. Incremental cost effectiveness was calculated using estimates of ICU vs non-ICU treatment effectiveness based on a formal systematic review of published studies. Decision analytic modeling was used to compare treatment alternatives. Sensitivity analyses of the key model parameters were performed. RESULTS: Out of 148 patients, seventy patients (47.2%) survived to one year after critical illness with a median quality of life index 0.64 [interquartile range(IQR) 0.49-0.76]. Median number of life years gained per patient was 30 (IQR 16-40) or 18 quality adjusted life years (QALYs) (IQR 7-28). The cost of treatment of critically ill patients varied between 1820 dollar and 20109 dollar per hospital survivor and between 100 dollar and 2514 dollar per QALY saved. Mean factors that influenced costs were: Age, diagnostic category, ICU and hospital length of stay and number and type of diagnostic and therapeutic interventions. The incremental cost effectiveness ratio for ICU treatment was estimated at 3254 dollar per QALY corresponding to 35% of per capita GDP or a Very Cost Effective category according to World Health Organization criteria. CONCLUSION: The ICU treatment of critically ill medical patients in a resource poor country is cost effective and compares favorably with other medical interventions. Public health authorities in low and middle income countries should encourage development of critical care services.

How to improve assessment of balance in baseline characteristics of clinical trial participants-example from PROSEVA trial data?

The randomization process is expected to balance assignment between the groups, independent to the participant and/or investigator, and as such avoids systematic error. However, it is recognized that groups assigned through the randomization process are not completely the same. Generally, a table with baseline characteristics is provided, where investigators report demographic and pertinent clinical variables based on the random group assignment and P values for the each variable in attempt to either support the balanced assignment or to indicate that the balance between groups was not ideal. The recently published PROSEVA trial showed more than 50% relative risk reduction of 28-day mortality among ARDS patients in the prone group compared to the supine group. In order to demonstrate a novel approach and exemplify how imbalance in baseline characteristics between groups could have potentially contributed to the large observed effect, we pooled pertinent baseline clinical variables from the trial in a meta-analysis-like manner. In addition to the quantification, we assigned the variable's "quality" of probable effect on the outcome as likely beneficial or harmful. After pooling pertinent dichotomous variables by the probability of their effect on the outcome, it appeared that approximately 37% (18% to 60%) of the observed PROSEVA trial effect could have been due to differences in baseline clinical characteristics. The main limitation of this approach is that all variables are assumed to have similar weights on the outcome. Interestingly, the weights of beneficial and harmful effects on the outcome were very similar. The proposed method of assessment of potential imbalance between the intervention groups assesses not only the magnitude of the difference, but rather the pooled probability of beneficial or harmful effect towards outcome, as well. As such, it could be useful as a secondary measure for the assessment of imbalance in the trials with the unexpectedly large observed effects.

Development and validation of clinical performance assessment in simulated medical emergencies: an observational study.

BACKGROUND: Critical illness is a time-sensitive process which requires practitioners to process vast quantities of data and make decisions rapidly. We have developed a tool, the Checklist for Early Recognition and Treatment of Acute Illness (CERTAIN), aimed at enhancing care delivery in such situations. To determine the efficacy of CERTAIN and similar cognitive aids, we developed rubric for evaluating provider performance in a simulated medical resuscitation environments. METHODS: We recruited 18 clinicians with current valid ACLS certification for evaluation in three simulated medical scenarios designed to mimic typical medical decompensation events routinely experienced in clinical care. Subjects were stratified as experienced or novice based on prior critical care training. A checklist of critical actions was designed using face validity for each scenario to evaluate task completion and performance. Simulation sessions were video recorded and scored by two independent raters. Construct validity was assessed under the assumption that experienced clinicians should perform better than novice clinicians on each task. Reliability was assessed as percentage agreement, kappa statistics and Bland-Altman plots as appropriate. RESULTS: Eleven experts and seven novices completed evaluation. The overall agreement on common checklist item completion was 84.8 %. The overall model achieved face validity and was consistent with our construct, with experienced clinicians trending towards better performance compared to novices for accuracy and speed of task completion. CONCLUSIONS: A standardized video assessment tool has potential to provide a valid and reliable method to assess 12 performances of clinicians facing simulated medical emergencies.

Improving the Accuracy of Cardiovascular Component of the Sequential Organ Failure Assessment Score.

OBJECTIVES: The Sequential Organ Failure Assessment score is an attractive risk prediction model because of its simplicity and graded assessment of morbidity and mortality. Due to changes in clinical practice over time, the cardiovascular component of the Sequential Organ Failure Assessment score no longer accurately reflects current clinical practice. To address this limitation, we developed and validated a modified cardiovascular component of the Sequential Organ Failure Assessment score that takes into account all vasoactive agents used in current clinical practice, uses shock index as a substitute for mean arterial pressure, and incorporates serum lactate as a biomarker for shock states. DESIGN: Retrospective cohort. SETTING: Mayo Clinic, Rochester, MN. PATIENTS: Adult patients admitted to one of six ICUs. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Score performance was assessed via area under the receiver operator characteristic curve. A total of 16,386 ICU admissions were included: 9,204 in the derivation cohort and 7,182 in the validation cohort. area under the receiver operator characteristic curve was significantly higher for modified cardiovascular component of the Sequential Organ Failure Assessment score than for cardiovascular component of the Sequential Organ Failure Assessment for in-ICU mortality (0.801 vs 0.718; difference = 0.083; p < 0.001), in-hospital mortality (0.783 vs 0.651; difference = 0.132; p < 0.001), and 28-day mortality (0.737 vs 0.655; difference = 0.082; p < 0.001). When modified cardiovascular component of the Sequential Organ Failure Assessment score was added to the remaining Sequential Organ Failure Assessment components, the modified Sequential Organ Failure Assessment score again outperformed the existing Sequential Organ Failure Assessment score: in-ICU mortality (0.836 vs 0.822; difference = 0.014; p < 0.001), in-hospital mortality (0.799 vs 0.784; difference = 0.015; p < 0.001), and 28-day mortality (0.798 vs 0.783; difference = 0.015; p < 0.001). Similar results were seen in the validation cohort. CONCLUSIONS: The modified cardiovascular component of the Sequential Organ Failure Assessment score outperforms the existing cardiovascular component of the Sequential Organ Failure Assessment score in predicting patient outcomes and improves the overall performance of the Sequential Organ Failure Assessment model. This score is easily calculated, includes serum lactate as a biomarker for shock states, and incorporates all vasopressors used in current clinical practice.