The Fall Risk Screening Scale Is Suitable for Evaluating Adult Patient Fall

Evaluation of a concise fall risk stratification among older adults with cataracts in day surgery settings: A historically controlled study

AIM

This study aimed to evaluate the use of a concise fall risk stratification in assessing and predicting falls compared with the Morse Falls Scale among older adults with cataracts in day surgery settings.

METHODS

A historically controlled study conducted from July 2020 to June 2022 was used in a municipal ophthalmic hospital in China. The concise fall risk stratification which directly graded fall risk by multifactorial judgment was used during the intervention period, while the Morse Falls Scale which graded fall risk by scale scores was used during the control period. The fall risk levels, fall assessment time, fall rates, fall-related injuries, predictive validity, and patient satisfaction with day surgery care were extracted. Propensity score matching was performed to balance baselines.

RESULTS

After matching, 4132 patients were included in the final analysis. Compared with the control group, the intervention group had significantly higher assessment results for fall risk level, a significantly shorter (by 48.15%) fall assessment time, and higher patient satisfaction. There were no differences in fall rates and fall-related injuries. Compared with the Morse Falls Scale, the concise fall risk stratification had higher sensitivity and negative predictive validity, and lower specificity and positive predictive validity, while the area under curve did not differ significantly.

CONCLUSION

The use of the concise fall risk stratification reduced fall assessment time, improved patient satisfaction, and is unlikely to impact falls with an overall predictive performance comparable to that of the Morse Falls Scale for older cataract adults in day surgery settings.

Validation and Improvement of the Saga Fall Risk Model: A Multicenter Retrospective Observational Study

Purpose

We conducted a pilot study in an acute care hospital and developed the Saga Fall Risk Model 2 (SFRM2), a fall prediction model comprising eight items: Bedriddenness rank, age, sex, emergency admission, admission to the neurosurgery department, history of falls, independence of eating, and use of hypnotics. The external validation results from the two hospitals showed that the area under the curve (AUC) of SFRM2 may be lower in other facilities. This study aimed to validate the accuracy of SFRM2 using data from eight hospitals, including chronic care hospitals, and adjust the coefficients to improve the accuracy of SFRM2 and validate it.

Patients and Methods

This study included all patients aged ≥20 years admitted to eight hospitals, including chronic care, acute care, and tertiary hospitals, from April 1, 2018, to March 31, 2021. In-hospital falls were used as the outcome, and the AUC and shrinkage coefficient of SFRM2 were calculated. Additionally, SFRM2.1, which was modified from the coefficients of SFRM2 using logistic regression with the eight items comprising SFRM2, was developed using two-thirds of the data randomly selected from the entire population, and its accuracy was validated using the remaining one-third portion of the data.

Results

Of the 124,521 inpatients analyzed, 2,986 (2.4%) experienced falls during hospitalization. The median age of all inpatients was 71 years, and 53.2% were men. The AUC of SFRM2 was 0.687 (95% confidence interval [CI]:0.678-0.697), and the shrinkage coefficient was 0.996. SFRM2.1 was created using 81,790 patients, and its accuracy was validated using the remaining 42,731 patients. The AUC of SFRM2.1 was 0.745 (95% CI: 0.731-0.758).

Conclusion

SFRM2 showed good accuracy in predicting falls even on validating in diverse populations with significantly different backgrounds. Furthermore, the accuracy can be improved by adjusting the coefficients while keeping the model's parameters fixed.

Insulin resistance induced by olanzapine and other second-generation antipsychotics in Chinese patients with schizophrenia: a comparative review and meta-analysis

PURPOSE

This systematic review aimed to determine whether olanzapine is more likely than other second-generation antipsychotics (SGAs) to induce insulin resistance in patients with schizophrenia in China.

METHODS

We reviewed all randomized controlled trials on insulin resistance and metabolic abnormalities caused by SGAs in the PubMed, China National Knowledge Infrastructure (CNKI), VIP, and Wanfang databases. Retrieved articles were published on or before December 2018. Meta-analysis was performed to determine the effect size of the treatment on the insulin resistance index (IRI), fasting blood glucose (FBG), and fasting insulin (FINS).

RESULTS

Forty studies (3725 participants in total) were included. All studies contained data suitable for comparing aripiprazole vs. olanzapine, ziprasidone vs. olanzapine, and risperidone vs. olanzapine. Patients treated with olanzapine had higher IRI, FBG, and FINS levels than did patients treated with aripiprazole, ziprasidone, or risperidone, with significant differences (aripiprazole vs. olanzapine: FBG: standardized mean difference [SMD] = 0.72, 95% confidence interval [95%CI] - 0.82, - 0.61; FINS: SMD = - 0.8, 95%CI - 1.00, - 0.61; IRI: SMD = - 0.80, 95%CI - 0.99, - 0.61; ziprasidone vs. olanzapine: FBG: SMD = - 1.19, 95%CI - 1.30, - 1.08; FINS: SMD = - 0.66, 95%CI - 0.85, - 0.47; IRI: SMD = - 0.71, 95%CI - 0.88, - 0.55; risperidone vs. olanzapine: FBG: SMD = - 0.17, 95%CI - 0.34, - 0.00).

CONCLUSIONS

Existing data suggest that olanzapine is associated with a significantly greater risk of IRI, FBG, and FINS, while other agents are associated with relatively lower risks. Thus, olanzapine is more likely to induce insulin resistance than are other SGAs in schizophrenic patients in China.