COVID-19 Admission Rates and Changes in Care Quality in US Hospitals.

Importance: Unprecedented increases in hospital occupancy rates during COVID-19 surges in 2020 caused concern over hospital care quality for patients without COVID-19. Objective: To examine changes in hospital nonsurgical care quality for patients without COVID-19 during periods of high and low COVID-19 admissions. Design, Setting, and Participants: This cross-sectional study used data from the 2019 and 2020 Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project State Inpatient Databases. Data were obtained for all nonfederal, acute care hospitals in 36 states with admissions in 2019 and 2020, and patients without a diagnosis of COVID-19 or pneumonia who were at risk for selected quality indicators were included. The data analysis was performed between January 1, 2023, and March 15, 2024. Exposure: Each hospital and week in 2020 was categorized based on the number of COVID-19 admissions per 100 beds: less than 1.0, 1.0 to 4.9, 5.0 to 9.9, 10.0 to 14.9, and 15.0 or greater. Main Outcomes and Measures: The main outcomes were rates of adverse outcomes for selected quality indicators, including pressure ulcers and in-hospital mortality for acute myocardial infarction, heart failure, acute stroke, gastrointestinal hemorrhage, hip fracture, and percutaneous coronary intervention. Changes in 2020 compared with 2019 were calculated for each level of the weekly COVID-19 admission rate, adjusting for case-mix and hospital-month fixed effects. Changes during weeks with high COVID-19 admissions (≥15 per 100 beds) were compared with changes during weeks with low COVID-19 admissions (<1 per 100 beds). Results: The analysis included 19 111 629 discharges (50.3% female; mean [SD] age, 63.0 [18.0] years) from 3283 hospitals in 36 states. In weeks 18 to 48 of 2020, 35 851 hospital-weeks (36.7%) had low COVID-19 admission rates, and 8094 (8.3%) had high rates. Quality indicators for patients without COVID-19 significantly worsened in 2020 during weeks with high vs low COVID-19 admissions. Pressure ulcer rates increased by 0.09 per 1000 admissions (95% CI, 0.01-0.17 per 1000 admissions; relative change, 24.3%), heart failure mortality increased by 0.40 per 100 admissions (95% CI, 0.18-0.63 per 100 admissions; relative change, 21.1%), hip fracture mortality increased by 0.40 per 100 admissions (95% CI, 0.04-0.77 per 100 admissions; relative change, 29.4%), and a weighted mean of mortality for the selected indicators increased by 0.30 per 100 admissions (95% CI, 0.14-0.45 per 100 admissions; relative change, 10.6%). Conclusions and Relevance: In this cross-sectional study, COVID-19 surges were associated with declines in hospital quality, highlighting the importance of identifying and implementing strategies to maintain care quality during periods of high hospital use.

COVID-19 Admission Rates and Changes in US Hospital Inpatient and Intensive Care Unit Occupancy.

Importance: The COVID-19 pandemic had unprecedented effects on hospital occupancy, with consequences for hospital operations and patient care. Previous studies of occupancy during COVID-19 have been limited to small samples of hospitals. Objective: To measure the association between COVID-19 admission rates and hospital occupancy in different US areas and at different time periods during 2020. Design, Setting, and Participants: This cross-sectional study used data from the Healthcare Cost and Utilization Project State Inpatient Databases (2019-2020) for patients in nonfederal acute care hospitals in 45 US states, including the District of Columbia. Data analysis was performed between September 1, 2022, and April 30, 2023. Exposures: Each hospital and week in 2020 was categorized based on the number of COVID-19 admissions per 100 beds (<1 [low], 1-4.9, 5-9.9, 10-14.9, or ≥15 [high]). Main Outcomes and Measures: The main outcomes were inpatient and intensive care unit (ICU) occupancy. We used regression analysis to estimate the average change in occupancy for each hospital-week in 2020 relative to the same hospital week in 2019. Results: This study included 3960 hospitals and 54 355 916 admissions. Of the admissions in the 40 states used for race and ethnicity analyses, 15.7% were for Black patients, 12.9% were for Hispanic patients, 62.5% were for White patients, and 7.2% were for patients of other race or ethnicity; 1.7% of patients were missing these data. Weekly COVID-19 admission rates in 2020 were less than 4 per 100 beds for 63.9% of hospital-weeks and at least 10 in only 15.9% of hospital-weeks. Inpatient occupancy decreased by 12.7% (95% CI, 12.1% to 13.4%) during weeks with low COVID-19 admission rates and increased by 7.9% (95% CI, 6.8% to 9.0%) during weeks with high COVID-19 admission rates. Intensive care unit occupancy rates increased by 67.8% (95% CI, 60.5% to 75.3%) during weeks with high COVID-19 admissions. Increases in ICU occupancy were greatest when weighted to reflect the experience of Hispanic patients. Changes in occupancy were most pronounced early in the pandemic. During weeks with high COVID-19 admissions, occupancy decreased for many service lines, with occupancy by surgical patients declining by 43.1% (95% CI, 38.6% to 47.2%) early in the pandemic. Conclusions and Relevance: In this cross-sectional study of US hospital discharges in 45 states in 2020, hospital occupancy decreased during weeks with low COVID-19 admissions and increased during weeks with high COVID-19 admissions, with the largest changes occurring early in the pandemic. These findings suggest that surges in COVID-19 strained ICUs and were associated with large decreases in the number of surgical patients. These occupancy fluctuations may have affected quality of care and hospital finances.

Temporal analysis of continuous chest compression rate and depth performed by firefighters during out of hospital cardiac arrest.

BACKGROUND: Quality of chest compressions (CC) during cardiopulmonary resuscitation (CPR) often do not meet guideline recommendations for rate and depth. This may be due to the fatiguing nature of physically compressing a patient's chest, meaning that CPR quality reduces over time. OBJECTIVE: This analysis investigates the effect of CPR duration on the performance of continuous CCs delivered by firefighters equipped with CPR feedback devices. METHODS: Data were collected from a first responder group which used CPR feedback and automatic external defibrillator devices when attending out-of-hospital cardiac arrest events. Depth and rate of CC were analysed for 134 patients. Mean CC depth and rate were calculated every 5 s during two-minute episodes of CPR. Regression models were created to evaluate the relationship between applied CC depth and rate as a function of time. RESULTS: Mean (SD) CC depth during the investigation was 48 (9) mm. An inverse relationship was observed between CC depth and CPR duration, where CC depth decreased by 3.39 mm, over two-minutes of CPR (p < 0.001). Mean (SD) CC rate was 112.06 (5.87) compressions per minute. No significant relationship was observed between CC rate and CPR duration (p = 0.077). Mean depth was within guideline range for 33.58% of patient events, while guideline rate was observed in 92.54% of cases. CONCLUSIONS: A reduction in CC depth was observed during two-minutes of continuous CCs while CC rate was not affected. One third of patients received a mean CC depth within guideline range (50 to 60 mm).

Trends in Severe Maternal Morbidity in the US Across the Transition to ICD-10-CM/PCS From 2012-2019.

Importance: Surveillance of severe maternal morbidity (SMM) is critical for monitoring maternal health and evaluating clinical quality improvement efforts. Objective: To evaluate national and state trends in SMM rates from 2012 to 2019 and potential disruptions associated with the transition to International Classification of Diseases, 10th Revision, Clinical Modification and Procedure Coding System (ICD-10-CM/PCS) in October 2015. Design, Setting, and Participants: This repeated cross-sectional analysis examined delivery hospitalizations from 2012 through 2019 in the Healthcare Cost and Utilization Project's National Inpatient Sample and State Inpatient Databases, an all-payer compendium of hospital discharge records from community, nonrehabilitation hospitals. Trends were evaluated using segmented linear binomial regression models that allowed for discontinuities across the ICD-10-CM/PCS transition. Analyses were completed from April 2021 through March 2022. Exposures: Time, ICD-10-CM/PCS coding system, and state. Main Outcomes and Measures: SMM rates, excluding blood transfusion, per 10 000 delivery hospitalizations, overall and by indicator. Results: From 2012 to 2019, there were 5 964 315 delivery hospitalizations in the national sample representing a weighted total of 29.8 million deliveries with a mean (SD) maternal age of 28.6 (5.9) years. SMM rates increased from 69.5 per 10 000 in 2012 to 79.7 per 10 000 in 2019 (rate difference [RD], 10.2; 95% CI, 5.8 to 14.6) without a significant change across the ICD-10-CM/PCS transition (RD, -3.2; 95% CI, -6.9 to 0.6). Of 20 SMM indicators, rates for 10 indicators significantly increased while 3 significantly decreased; 5 of these changes were associated with ICD-10-CM/PCS transition. Acute kidney failure had the largest increase, from 6.4 to 15.3 per 10 000 delivery hospitalizations (RD, 8.9; 95% CI, 7.5 to 10.3) with no change associated with ICD transition (RD, -0.1; 95% CI, -1.2 to 1.1). Disseminated intravascular coagulation had the largest decrease from 31.3 to 21.2 per 10 000 (RD, 10.2; 95% CI, -12.8 to -7.5), with a significant drop associated with ICD transition (RD, -7.9; 95% CI, -10.2 to -5.6). State SMM rates significantly decreased for 1 state and significantly increased for 21 states from 2012 to 2019 and associations with ICD transition varied. Conclusions and Relevance: In this cross-sectional study, overall US SMM rates increased from 2012 to 2019, which was not associated with the ICD-10-CM/PCS transition. However, data for certain indicators and states may not be comparable across coding systems; efforts are needed to understand SMM increases and state variation.

New evidence on geographic disparities in United States hospital capacity.

OBJECTIVE: To characterize the quantity and quality of hospital capacity across the United States. DATA SOURCES: We combine a 2017 near-census of US hospital inpatient discharges from the Healthcare Cost and Utilization Project (HCUP) with American Hospital Association Survey, Hospital Compare, and American Community Survey data. STUDY DESIGN: This study produces local hospital capacity quantity and care quality measures by allocating capacity to zip codes using market shares and population totals. Disparities in these measures are examined by race and ethnicity, income, age, and urbanicity. DATA COLLECTION/EXTRACTION METHODS: All data are derived from pre-existing sources. All hospitals and zip codes in states, including the District of Columbia, contributing complete data to HCUP in 2017 are included. PRINCIPAL FINDINGS: Non-Hispanic Black individuals living in zip codes supplied, on average, 0.11 more beds per 1000 population (SE = 0.01) than places where non-Hispanic White individuals live. However, the hospitals supplying this capacity have 0.36 fewer staff per bed (SE = 0.03) and perform worse on many care quality measures. Zip codes in the most urban parts of America have the least hospital capacity (2.11 beds per 1000 persons; SEM = 0.01) from across the rural-urban continuum. While more rural areas have markedly higher capacity levels, urban areas have advantages in staff and capital per bed. We do not find systematic differences in care quality between rural and urban areas. CONCLUSIONS: This study highlights the importance of lower hospital care quality and resource intensity in driving racial and ethnic, as well as income, disparities in hospital care-related outcomes. This study also contributes an alternative approach for measuring local hospital capacity that accounts for cross-hospital service area flows. Adjusting for these flows is necessary to avoid underestimating the supply of capacity in rural areas and overestimating it in places where non-Hispanic Black individuals tend to live.

Incorporating harms into the weighting of the revised Agency for Healthcare Research and Quality Patient Safety for Selected Indicators Composite (Patient Safety Indicator 90).

OBJECTIVE: To reweight the Agency for Healthcare Research and Quality Patient Safety for Selected Indicators Composite (Patient Safety Indicator [PSI] 90) from weights based solely on the frequency of component PSIs to those that incorporate excess harm reflecting patients' preferences for outcome-related health states. DATA SOURCES: National administrative and claims data involving hospitalizations in nonfederal, nonrehabilitation, acute care hospitals. STUDY DESIGN: We estimated the average excess aggregate harm associated with the occurrence of each component PSI using a cohort sample for each indicator based on denominator-eligible records. We used propensity scores to account for potential confounding in the risk models for each PSI and weighted observations to estimate the "average treatment effect in the treated" for those with the PSI event. We fit separate regression models for each harm outcome. Final PSI weights reflected both the disutilities and the frequencies of the harms. DATA COLLECTION/EXTRACTION METHODS: We estimated PSI frequencies from the 2012 Healthcare Cost and Utilization Project State Inpatient Databases with present on admission data and excess harms using 2012-2013 Centers for Medicare & Medicaid Services Medicare Fee-for-Service data. PRINCIPAL FINDINGS: Including harms in the weighting scheme changed individual component weights from the original frequency-based weighting. In the reweighted composite, PSIs 11 ("Postoperative Respiratory Failure"), 13 ("Postoperative Sepsis"), and 12 ("Perioperative Pulmonary Embolism or Deep Vein Thrombosis") contributed the greatest harm, with weights of 29.7%, 21.1%, and 20.4%, respectively. Regarding reliability, the overall average hospital signal-to-noise ratio for the reweighted PSI 90 was 0.7015. Regarding discrimination, among hospitals with greater than median volume, 34% had significantly better PSI 90 performance, and 41% had significantly worse performance than benchmark rates (based on percentiles). CONCLUSIONS: Reformulation of PSI 90 with harm-based weights is feasible and results in satisfactory reliability and discrimination, with a more clinically meaningful distribution of component weights.

Neonatal Abstinence Syndrome and Maternal Opioid-Related Diagnoses: Analysis of ICD-10-CM Transition, 2013-2017.

BACKGROUND AND OBJECTIVES: Hospital discharge records remain a common data source for tracking the opioid crisis among pregnant women and infants. The International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) transition from the International Classification of Diseases, Ninth Revision, Clinical Modification may have affected surveillance. Our aim was to evaluate this transition on rates of neonatal abstinence syndrome (NAS), maternal opioid use disorder (OUD), and opioid-related diagnoses (OUD with ICD-10-CM codes for long-term use of opioid analgesics and unspecified opioid use). METHODS: Data from the 2013-2017 Healthcare Cost and Utilization Project's National Inpatient Sample were used to conduct, interrupted time series analysis and log-binomial segmented regression to assess whether quarterly rates differed across the transition. RESULTS: From 2013 to 2017, an estimated 18.8 million birth and delivery hospitalizations were represented. The ICD-10-CM transition was not associated with NAS rates (rate ratio [RR]: 0.99; 95% confidence interval [CI]: 0.90-1.08; P = .79) but was associated with 11% lower OUD rates (RR: 0.89; 95% CI: 0.80-0.98; P = .02) and a decrease in the quarterly trend (RR: 0.98; 95% CI: 0.96-1.00; P = .04). The transition was not associated with maternal OUD plus long-term use rates (RR: 0.98; 95% CI: 0.89-1.09; P = .76) but was associated with a 20% overall increase in opioid-related diagnosis rates including long-term and unspecified use (RR: 1.20; 95% CI: 1.09-1.32; P < .001). CONCLUSIONS: The ICD-10-CM transition did not appear to affect NAS. However, coding of maternal OUD alone may not capture the same population across the transition, which confounds the interpretation of trend data spanning this time period.

Software annotation of defibrillator files: Ready for prime time?

BACKGROUND: High-quality chest compressions are associated with improved outcomes after cardiac arrest. Defibrillators record important information about chest compressions during cardiopulmonary resuscitation (CPR) and can be used in quality-improvement programs. Defibrillator review software can automatically annotate files and measure chest compression metrics. However, evidence is limited regarding the accuracy of such measurements. OBJECTIVE: To compare chest compression fraction (CCF) and rate measurements made with software annotation vs. manual annotation vs. limited manual annotation of defibrillator files recorded during out-of-hospital cardiac arrest (OHCA) CPR. METHODS: This was a retrospective, observational study of 100 patients who had CPR for OHCA. We assessed chest compression bioimpedance waveforms from the time of initial CPR until defibrillator removal. A reviewer revised software annotations in two ways: completely manual annotations and limited manual annotations, which marked the beginning and end of CPR and ROSC, but not chest compressions. Measurements were compared for CCF and rate using intraclass correlation coefficient (ICC) analysis. RESULTS: Case mean rate showed no significant difference between the methods (108.1-108.6 compressions per minute) and ICC was excellent (>0.90). The case mean (±SD) CCF for software, manual, and limited manual annotation was 0.64 ±â€¯0.19, 0.86 ±â€¯0.07, and 0.81 ±â€¯0.10, respectively. The ICC for manual vs. limited manual annotation of CCF was 0.69 while for individual minute epochs it was 0.83. CONCLUSION: Software annotation performed very well for chest compression rate. For CCF, the difference between manual and software annotation measurements was clinically important, while manual vs. limited manual annotation were similar with an ICC that was good-to-excellent.

Neonatal Abstinence Syndrome and Maternal Opioid-Related Diagnoses in the US, 2010-2017.

Importance: Substantial increases in both neonatal abstinence syndrome (NAS) and maternal opioid use disorder have been observed through 2014. Objective: To examine national and state variation in NAS and maternal opioid-related diagnoses (MOD) rates in 2017 and to describe national and state changes since 2010 in the US, which included expanded MOD codes (opioid use disorder plus long-term and unspecified use) implemented in International Classification of Disease, 10th Revision, Clinical Modification. Design, Setting, and Participants: Repeated cross-sectional analysis of the 2010 to 2017 Healthcare Cost and Utilization Project's National Inpatient Sample and State Inpatient Databases, an all-payer compendium of hospital discharge records from community nonrehabilitation hospitals in 47 states and the District of Columbia. Exposures: State and year. Main Outcomes and Measures: NAS rate per 1000 birth hospitalizations and MOD rate per 1000 delivery hospitalizations. Results: In 2017, there were 751 037 birth hospitalizations and 748 239 delivery hospitalizations in the national sample; 5375 newborns had NAS and 6065 women had MOD documented in the discharge record. Mean gestational age was 38.4 weeks and mean maternal age was 28.8 years. From 2010 to 2017, the estimated NAS rate significantly increased by 3.3 per 1000 birth hospitalizations (95% CI, 2.5-4.1), from 4.0 (95% CI, 3.3-4.7) to 7.3 (95% CI, 6.8-7.7). The estimated MOD rate significantly increased by 4.6 per 1000 delivery hospitalizations (95% CI, 3.9-5.4), from 3.5 (95% CI, 3.0-4.1) to 8.2 (95% CI, 7.7-8.7). Larger increases for MOD vs NAS rates occurred with new International Classification of Disease, 10th Revision, Clinical Modification codes in 2016. From a census of 47 state databases in 2017, NAS rates ranged from 1.3 per 1000 birth hospitalizations in Nebraska to 53.5 per 1000 birth hospitalizations in West Virginia, with Maine (31.4), Vermont (29.4), Delaware (24.2), and Kentucky (23.9) also exceeding 20 per 1000 birth hospitalizations, while MOD rates ranged from 1.7 per 1000 delivery hospitalizations in Nebraska to 47.3 per 1000 delivery hospitalizations in Vermont, with West Virginia (40.1), Maine (37.8), Delaware (24.3), and Kentucky (23.4) also exceeding 20 per 1000 delivery hospitalizations. From 2010 to 2017, NAS and MOD rates increased significantly for all states except Nebraska and Vermont, which only had MOD increases. Conclusions and Relevance: In the US from 2010 to 2017, estimated rates of NAS and MOD significantly increased nationally and for the majority of states, with notable state-level variation.

Effects of Hurricanes on Emergency Department Utilization: An Analysis Across 7 US Storms.

OBJECTIVE: Emergency departments (EDs) are critical sources of care after natural disasters such as hurricanes. Understanding the impact on ED utilization by subpopulation and proximity to the hurricane's path can inform emergency preparedness planning. This study examines changes in ED utilization for residents of 344 counties after the occurrence of 7 US hurricanes between 2005 and 2016. METHODS: This retrospective observational study used ED data from the Healthcare Cost and Utilization Project State Inpatient Databases and State Emergency Department Databases. ED utilization rates for weeks during and after hurricanes were compared with pre-hurricane rates, stratified by the proximity of the patient county to the hurricane path, age, and disease category. RESULTS: The overall population rate of weekly ED visits changed little post-hurricane, but rates by disease categories and age demonstrated varying results. Utilization rates for respiratory disorders exhibited the largest post-hurricane increase, particularly 2-3 weeks following the hurricane. The change in population rates by disease categories and age tended to be larger for people residing in counties closer to the hurricane path. CONCLUSIONS: Changes in ED utilization following hurricanes depend on disease categories, age, and proximity to the hurricane path. Emergency managers could incorporate these factors into their planning processes.

Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

Feedback on chest compressions and ventilations during cardiopulmonary resuscitation (CPR) is important to improve survival from out-of-hospital cardiac arrest (OHCA). The thoracic impedance signal acquired by monitor-defibrillators during treatment can be used to provide feedback on ventilations, but chest compression components prevent accurate detection of ventilations. This study introduces the first method for accurate ventilation detection using the impedance while chest compressions are concurrently delivered by a mechanical CPR device. A total of 423 OHCA patients treated with mechanical CPR were included, 761 analysis intervals were selected which in total comprised 5 884 minutes and contained 34 864 ventilations. Ground truth ventilations were determined using the expired CO 2 channel. The method uses adaptive signal processing to obtain the impedance ventilation waveform. Then, 14 features were calculated from the ventilation waveform and fed to a random forest (RF) classifier to discriminate false positive detections from actual ventilations. The RF feature importance was used to determine the best feature subset for the classifier. The method was trained and tested using stratified 10-fold cross validation (CV) partitions. The training/test process was repeated 20 times to statistically characterize the results. The best ventilation detector had a median (interdecile range, IDR) F 1-score of 96.32 (96.26-96.37). When used to provide feedback in 1-min intervals, the median (IDR) error and relative error in ventilation rate were 0.002 (-0.334-0.572) min-1 and 0.05 (-3.71-9.08)%, respectively. An accurate ventilation detector during mechanical CPR was demonstrated. The algorithm could be introduced in current equipment for feedback on ventilation rate and quality, and it could contribute to improve OHCA survival rates.

Association of ventilation with outcomes from out-of-hospital cardiac arrest.

AIM OF STUDY: To determine the association between bioimpedence-detected ventilation and out-of-hospital cardiac arrest (OHCA) outcomes. METHODS: This is a retrospective, observational study of 560 OHCA patients from the Dallas-Fort Worth site enrolled in the Resuscitation Outcomes Consortium Trial of Continuous or Interrupted Chest Compressions During CPR from 4/2012 to 7/2015. We measured bioimpedance ventilation (lung inflation) waveforms in the pause between chest compression segments (Physio-Control LIFEPAK 12 and 15, Redmond, WA) recorded through defibrillation pads. We included cases ≥18 years with presumed cardiac cause of arrest assigned to interrupted 30:2 chest compressions with bag-valve-mask ventilation and ≥2 min of recorded cardiopulmonary resuscitation. We compared outcomes in two a priori pre-specified groups: patients with ventilation waveforms in <50% of pauses (Group 1) versus those with waveforms in ≥50% of pauses (Group 2). RESULTS: Mean duration of 30:2 CPR was 13 ±â€¯7 min with a total of 7762 pauses in chest compressions. Group 1 (N = 424) had a median 11 pauses and 3 ventilations per patient vs. Group 2 (N = 136) with a median 12 pauses and 8 ventilations per patient, which was associated with improved return of spontaneous circulation (ROSC) at any time (35% vs. 23%, p < 0.005), prehospital ROSC (19.8% vs. 8.7%, p < 0.0009), emergency department ROSC (33% vs. 21%, p < 0.005), and survival to hospital discharge (10.3% vs. 4.0%, p = 0.008). CONCLUSIONS: This novel study shows that ventilation with lung inflation occurs infrequently during 30:2 CPR. Ventilation in ≥50% of pauses was associated with significantly improved rates of ROSC and survival.

Capnography: A support tool for the detection of return of spontaneous circulation in out-of-hospital cardiac arrest.

BACKGROUND: Automated detection of return of spontaneous circulation (ROSC) is still an unsolved problem during cardiac arrest. Current guidelines recommend the use of capnography, but most automatic methods are based on the analysis of the ECG and thoracic impedance (TI) signals. This study analysed the added value of EtCO2 for discriminating pulsed (PR) and pulseless (PEA) rhythms and its potential to detect ROSC. MATERIALS AND METHODS: A total of 426 out-of-hospital cardiac arrest cases, 117 with ROSC and 309 without ROSC, were analysed. First, EtCO2 values were compared for ROSC and no ROSC cases. Second, 5098 artefact free 3-s long segments were automatically extracted and labelled as PR (3639) or PEA (1459) using the instant of ROSC annotated by the clinician on scene as gold standard. Machine learning classifiers were designed using features obtained from the ECG, TI and the EtCO2 value. Third, the cases were retrospectively analysed using the classifier to discriminate cases with and without ROSC. RESULTS: EtCO2 values increased significantly from 41 mmHg 3-min before ROSC to 57 mmHg 1-min after ROSC, and EtCO2 was significantly larger for PR than for PEA, 46 mmHg/20 mmHg (p < 0.05). Adding EtCO2 to the machine learning models increased their area under the curve (AUC) by over 2 percentage points. The combination of ECG, TI and EtCO2 had an AUC for the detection of pulse of 0.92. Finally, the retrospective analysis showed a sensitivity and specificity of 96.6% and 94.5% for the detection of ROSC and no-ROSC cases, respectively. CONCLUSION: Adding EtCO2 improves the performance of automatic algorithms for pulse detection based on ECG and TI. These algorithms can be used to identify pulse on site, and to retrospectively identify cases with ROSC.

Value of capnography to predict defibrillation success in out-of-hospital cardiac arrest.

BACKGROUND AND AIM: Unsuccessful defibrillation shocks adversely affect survival from out-of-hospital cardiac arrest (OHCA). Ventricular fibrillation (VF) waveform analysis is the tool-of-choice for the non-invasive prediction of shock success, but surrogate markers of perfusion like end-tidal CO2 (EtCO2) could improve the prediction. The aim of this study was to evaluate EtCO2 as predictor of shock success, both individually and in combination with VF-waveform analysis. MATERIALS AND METHODS: In total 514 shocks from 214 OHCA patients (75 first shocks) were analysed. For each shock three predictors of defibrillation success were automatically calculated from the device files: two VF-waveform features, amplitude spectrum area (AMSA) and fuzzy entropy (FuzzyEn), and the median EtCO2 (MEtCO2) in the minute before the shock. Sensitivity, specificity, receiver operating characteristic (ROC) curves and area under the curve (AUC) were calculated, for each predictor individually and for the combination of MEtCO2 and VF-waveform predictors. Separate analyses were done for first shocks and all shocks. RESULTS: MEtCO2 in first shocks was significantly higher for successful than for unsuccessful shocks (31mmHg/25mmHg, p<0.05), but differences were not significant for all shocks (32mmHg/29mmHg, p>0.05). MEtCO2 predicted shock success with an AUC of 0.66 for first shocks, but was not a predictor for all shocks (AUC 0.54). AMSA and FuzzyEn presented AUCs of 0.76 and 0.77 for first shocks, and 0.75 and 0.75 for all shocks. For first shocks, adding MEtCO2 improved the AUC of AMSA and FuzzyEn to 0.79 and 0.83, respectively. CONCLUSIONS: MEtCO2 predicted defibrillation success only for first shocks. Adding MEtCO2 to VF-waveform analysis in first shocks improved prediction of shock success. VF-waveform features and MEtCO2 were automatically calculated from the device files, so these methods could be introduced in current defibrillators adding only new software.

Readmissions Following Inpatient Treatment for Opioid-Related Conditions.

BACKGROUND: Previous research suggests that relatively few hospitalized patients with opioid-related conditions receive substance use treatment during their inpatient stay. Without treatment, these individuals may be more likely to have subsequent hospitalizations for continued opioid use disorder. OBJECTIVE: To evaluate the relationship between receipt of inpatient drug detoxification and/or rehabilitation services and subsequent opioid-related readmission. METHODS: This study used combined hospital inpatient discharge and emergency department visit data from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project. Our sample consisted of 329,037 patients from seven states with an opioid-related index hospitalization occurring between March 2010 and September 2013. Multivariate analysis was conducted to examine the relationship between opioid-related readmission and the receipt of inpatient drug detoxification and/or rehabilitation during the index visit. RESULTS: A relatively small percentage (19.4%) of patients with identified opioid-related conditions received treatment for drug use during their hospital inpatient stay. Patients who received drug rehabilitation, but not drug detoxification, during an opioid-related index hospitalization had lower odds of an opioid-related readmission within 90 days of discharge (odds ratio = 0.60, 95% confidence interval = 0.54-0.67) compared with patients with no inpatient drug detoxification or rehabilitation. Conclusions/Importance: A low percentage of patients receive inpatient services for drug use during an index stay involving an opioid-related diagnosis. Our findings indicate that receipt of drug rehabilitation services in acute care hospitals is associated with a lower 90-day readmission rate. Further research is needed to understand factors associated with the receipt of inpatient services and readmissions.

Impedance Based Automatic Detection of Ventilations During Mechanical Cardiopulmonary Resuscitation.

Monitoring ventilation rate is key to improve the quality of cardiopulmonary resuscitation (CPR) and increase the probability of survival in the event of an out-of-hospital cardiac arrest (OHCA). Ventilations produce discernible fluctuations in the thoracic impedance signal recorded by defibrillators. Impedance-based detection of ventilations during CPR is challenging due to chest compression artifacts. This study presents a method for an accurate detection of ventilations when chest compressions are delivered using a piston-driven mechanical device. Data from 223 OHCA patients were analyzed and 399 analysis segments totaling 3101 minutes of mechanical CPR were extracted. A total of 18327 ventilations were annotated using concurrent capnogram recordings. An adaptive least mean squares filter was used to remove compression artifacts. Potential ventilations were detected using a greedy peak detector, and the ventilation waveform was characterized using 8 waveform features. These features were used in a logistic regression classifier to discriminate true ventilations from false positives produced by the greedy peak detector. The classifier was trained and tested using patient wise 10-fold cross validation (CV), and 100 random CV partitions were created to statistically characterize the performance metrics. The peak detector presented a sensitivity (Se) of 99.30%, but a positive predictive value (PPV) of 54.43%. The best classifier configuration used 6 features and improved the mean (sd) Se and PPV of the detector to 93.20% (0.06) and 94.43% (0.04), respectively. When used to measure per minute ventilation rates for feedback to the rescuer, the mean (sd) absolute error in ventilation rate was 0.61 (1.64) min-1. The first impedance-based method to accurately detect ventilations and give feedback on ventilation rate during mechanical CPR has been demonstrated.