Use of International Classification of Diseases, Ninth Revision, Clinical Modification, codes to identify inpatient fall-related injuries

Factors Associated with Acute Injurious Falls in Elderly Hospitalized Patients: A Multicenter Descriptive Study

BACKGROUND

The Centers for Medicare & Medicaid Services Hospital-Acquired Conditions (CMS-HAC) links Medicare payments to health care quality. Experiencing a serious disability or death associated with a fall in a health care facility based on diagnosis codes has been identified as an opportunity for improvement. Multiple factors contribute to an inpatient fall, including medications that affect cognition in older adults. The primary aim of this study was to investigate the effect of the commonly prescribed classes of medications on the CMS-HAC falls and trauma definition in US hospitals in a large inpatient database from 2019 to 2021.

METHODS

The authors analyzed data from 835 hospitals in the Vizient Clinical Data Base between January 1, 2019, and December 31, 2021, on patients ≥ 65 years of age with CMS-HAC patient falls and trauma codes. Using logistic regression and stepwise Poisson regression analysis, the authors identified demographic, clinical, and hospital-related variables associated with falls meeting the CMS-HAC definition. The top 20 prescribed drug classes in these patients were also identified.

RESULTS

Among 11,064,024 patient encounters, 5,978 met the CMS-HAC definition of a serious fall. Patients who experienced a serious fall were significantly more likely to be > 79 years of age (p < 0.001, odds ratio [OR] 1.30, 95% confidence interval [CI] 1.23-1.37), have a history of prior falls (p < 0.001, OR 2.30, 95% CI 2.11-2.50), have a code for dementia (p < 0.001, OR 1.50, 95% CI 1.40-1.60), and have higher anticholinergic cognitive burden (ACB) scores (p < 0.001, OR 1.14, 95% CI 1.13-1.14). Specific medication classes associated with CMS-HAC falls were first-generation antihistamines (p < 0.00, OR 1.21, 95% CI 1.09-1.35), second-generation antihistamines (p ≤ 0.001, OR 1.15, 95% CI 1.13-1.19), and atypical antipsychotics (p < 0.001, OR 1.18, CI 1.13-1.29).

CONCLUSION

Patients who experience a fall meeting the CMS-HAC fall definition are significantly more likely to have a prior history of falling, dementia, and a higher ACB score. Results from this study may inform future quality improvement work aimed at reducing injurious falls.

Identification of Fall-Related Injuries in Nursing Home Residents Using Administrative Claims Data

BACKGROUND

Fall-related injuries (FRIs) are a leading cause of morbidity, mortality, and costs among nursing home (NH) residents. Carefully defining FRIs in administrative data is essential for improving injury-reduction efforts. We developed a series of novel claims-based algorithms for identifying FRIs in long-stay NH residents.

METHODS

This is a retrospective cohort of residents of NH residing there for at least 100 days who were continuously enrolled in Medicare Parts A and B in 2016. FRIs were identified using 4 claims-based case-qualifying (CQ) definitions (Inpatient [CQ1], Outpatient and Provider with Procedure [CQ2], Outpatient and Provider with Fall [CQ3], or Inpatient or Outpatient and Provider with Fall [CQ4]). Correlation was calculated using phi correlation coefficients.

RESULTS

Of 153 220 residents (mean [SD] age 81.2 [12.1], 68.0% female), we identified 10 104 with at least one FRI according to one or more CQ definition. Among 2 950 residents with hip fractures, 1 852 (62.8%) were identified by all algorithms. Algorithm CQ4 (n = 326-2 775) identified more FRIs across all injuries while CQ1 identified less (n = 21-2 320). CQ2 identified more intracranial bleeds (1 028 vs 448) than CQ1. For nonfracture categories, few FRIs were identified using CQ1 (n = 20-488). Of the 2 320 residents with hip fractures identified by CQ1, 2 145 (92.5%) had external cause of injury codes. All algorithms were strongly correlated, with phi coefficients ranging from 0.82 to 0.99.

CONCLUSIONS

Claims-based algorithms applied to outpatient and provider claims identify more nonfracture FRIs. When identifying risk factors, stakeholders should select the algorithm(s) suitable for the FRI and study purpose.

Pediatric Unintentional Fall-Related Injuries in a Statewide Trauma Registry

OBJECTIVES

The purpose of the study was to evaluate patterns of fall-related injury through childhood and identify risk factors for more severe fall-related injuries with the goal of informing targeted prevention strategies for different ages.

METHODS

The study population consisted of pediatric patients in the Iowa Trauma Registry from January 1, 2010, to December 31, 2014, who sustained an unintentional fall-related injury (N = 3856 patients). Multinomial logistic regression analysis was used to predict injury severity. Adjusted odds ratios were calculated characterizing the relationship between fall severity and age, sex, race, and fall type.

RESULTS

More males (62%) sustained a fall-related injury during the study period when compared with females (38%; P < 0.0001). Head injuries were the most common type of injury in the younger than 1 year age group (77%), whereas fractures were the predominant injury type in all other age groups, followed by head injuries. Those younger than 1 year (adjusted odds ratio, 4.0; 95% confidence interval, 2.36-6.90) and aged 15 to 18 years (adjusted odds ratio, 1.9; 95% confidence interval, 1.17-3.03) were more likely to have an Injury Severity Score of ≥16 than those aged 10 to 14 years.

CONCLUSIONS

Recommendations and prevention strategies need to focus on specific risk factors to reduce the harm of multilevel falls. As we have shown, patterns of fall injuries presenting to trauma hospitals differ by age, thus suggesting that prevention strategies focus on specific age groups.

In-Hospital Fall and Fracture Risk With Conditions in the Elixhauser Comorbidity Index: An Analysis of State Inpatient Data

OBJECTIVE

In-hospital falls (IHFs) are a significant burden to the healthcare industry and patients seeking inpatient care. Many falls lead to injuries that could be considered a hospital-acquired condition (HAC). We demonstrated how administrative data can be used to quantify how many IHFs occur and identify what conditions increase the risk for these falls.

METHODS

Iowa State Inpatient Database records from 2008 to 2014 for adults older than 50 years were used to quantify IHFs, falls resulting in an HAC (HAC IHFs), and fractures during in-hospital treatment. The medical conditions used in the Elixhauser Comorbidity Index were evaluated for the risk of the separate fall-related outcomes using Poisson regression.

RESULTS

There were 1770 records that had an IHF for an IHF rate of 0.26 per 1000 patient days. Psychoses (rate ratio = 1.95, 95% confidence interval = 1.63-2.34) and alcohol abuse (rate ratio = 1.77, 95% confidence interval = 1.40-2.24) showed the greatest increase in IHF risk. These conditions also increased the risk of HAC IHFs and in-hospital fractures. Fluid and electrolyte disorders, deficiency anemias, and chronic pulmonary disease increased the risk for IHFs/HAC IHFs but did not increase the risk of in-hospital fractures.

CONCLUSIONS

Administrative data can be used to track various fall-related outcomes occurring during inpatient treatment. Several conditions of the Elixhauser Comorbidity Index were identified as increasing the risk of fall-related outcomes and should be considered when evaluating a patient's risk of falling.

A Computational Adverse Event Detection Matrix

Harms caused during healthcare encounters are pervasive and occur at an alarming rate; therefore, building a set of computational detection methodologies in the adverse event area is urgently needed to address this problem. To understand the entire range of adverse event detection methods currently in practice we have developed a computational adverse event detection matrix. This structure is made of methods used presently at US hospitals to detect patient safety events. It contains adverse event 1) concepts and 2) synthesized detection strategies as well as calculations of overlap of coded data in the subset of algorithms implemented completely computationally. Most importantly, this matrix provides a clear picture of coverage gaps in the detection of adverse events.

Using machine learning to identify health outcomes from electronic health record data

Purpose of review

Electronic health records (EHRs) contain valuable data for identifying health outcomes, but these data also present numerous challenges when creating computable phenotyping algorithms. Machine learning methods could help with some of these challenges. In this review, we discuss four common scenarios that researchers may find helpful for thinking critically about when and for what tasks machine learning may be used to identify health outcomes from EHR data.

Recent findings

We first consider the conditions in which machine learning may be especially useful with respect to two dimensions of a health outcome: 1) the characteristics of its diagnostic criteria, and 2) the format in which its diagnostic data are usually stored within EHR systems. In the first dimension, we propose that for health outcomes with diagnostic criteria involving many clinical factors, vague definitions, or subjective interpretations, machine learning may be useful for modeling the complex diagnostic decision-making process from a vector of clinical inputs to identify individuals with the health outcome. In the second dimension, we propose that for health outcomes where diagnostic information is largely stored in unstructured formats such as free text or images, machine learning may be useful for extracting and structuring this information as part of a natural language processing system or an image recognition task. We then consider these two dimensions jointly to define four common scenarios of health outcomes. For each scenario, we discuss the potential uses for machine learning - first assuming accurate and complete EHR data and then relaxing these assumptions to accommodate the limitations of real-world EHR systems. We illustrate these four scenarios using concrete examples and describe how recent studies have used machine learning to identify these health outcomes from EHR data.

Summary

Machine learning has great potential to improve the accuracy and efficiency of health outcome identification from EHR systems, especially under certain conditions. To promote the use of machine learning in EHR-based phenotyping tasks, future work should prioritize efforts to increase the transportability of machine learning algorithms for use in multi-site settings.

Hospital safety among neurologic patients

Objective:

To examine the frequency and type of adverse events (AEs) experienced by neurologic patients in hospital.

Methods:

This population-based, retrospective cohort study used hospital discharge abstract data for children and adults admitted to hospital from 2009 to 2015 with 1 of 9 neurologic conditions (Alzheimer disease and related dementia, brain tumor, epilepsy, motor neuron disease, multiple sclerosis, parkinsonism/Parkinson disease, spinal cord injury, traumatic brain injury, and stroke). Neurologic conditions were identified with ICD-10-CA codes. Eighteen AEs were examined with ICD-10-CA codes. The proportion of AEs was calculated, and regression analysis was used to examine factors and outcomes associated with AEs (age, sex, comorbidity, length of stay, and mortality).

Results:

The overall proportion of admissions associated with an AE among those with a neurologic condition was 11 per 100 admissions. Those with a spinal cord injury had the highest proportion of AEs (39.4 per 100 admissions). The most common AEs were infections and respiratory complications (32.0% and 16.7%, respectively). Age and the presence of comorbidities were associated with higher odds of an AE, while readmission was associated with lower odds of an AE. Having an AE was associated with increased length of stay and higher odds of mortality.

Conclusions:

This study demonstrates that neurologic patients have a high proportion of AEs in hospital. The findings provide information on the quality and safety of care for people with neurologic conditions in hospital, which can help inform future quality improvement initiatives.

Main characteristics observed in patients with hematologic diseases admitted to an intensive care unit of a Brazilian university hospital

Objective

To evaluate the clinical characteristics of patients with hematological disease admitted to the intensive care unit and the use of noninvasive mechanical ventilation in a subgroup with respiratory dysfunction.

Methods

A retrospective observational study from September 2011 to January 2014.

Results

Overall, 157 patients were included. The mean age was 45.13 (± 17.2) years and 46.5% of the patients were female. Sixty-seven (48.4%) patients had sepsis, and 90 (57.3%) patients required vasoactive vasopressors. The main cause for admission to the intensive care unit was acute respiratory failure (94.3%). Among the 157 studied patients, 47 (29.9%) were intubated within the first 24 hours, and 38 (24.2%) underwent noninvasive mechanical ventilation. Among the 38 patients who initially received noninvasive mechanical ventilation, 26 (68.4%) were subsequently intubated, and 12 (31.6%) responded to this mode of ventilation. Patients who failed to respond to noninvasive mechanical ventilation had higher intensive care unit mortality (66.7% versus 16.7%; p = 0.004) and a longer stay in the intensive care unit (9.6 days versus 4.6 days, p = 0.02) compared with the successful cases. Baseline severity scores (SOFA and SAPS 3) and the total leukocyte count were not significantly different between these two subgroups. In a multivariate logistic regression model including the 157 patients, intubation at any time during the stay in the intensive care unit and SAPS 3 were independently associated with intensive care unit mortality, while using noninvasive mechanical ventilation was not.

Conclusion

In this retrospective study with severely ill hematologic patients, those who underwent noninvasive mechanical ventilation at admission and failed to respond to it presented elevated intensive care unit mortality. However, only intubation during the intensive care unit stay was independently associated with a poor outcome. Further studies are needed to define predictors of noninvasive mechanical ventilation failure.