Development of a clinical prediction rule to identify physical activity after total hip arthroplasty

OBJECTIVE

To develop clinical prediction rule (CPR) of physical activity at 1-year after total hip arthroplasty (THA).

DESIGN

Retrospective cohort study.

SETTING

University hospital with orthopedic surgery.

PARTICIPANTS

The study group included 321 patients (56 men) who underwent primary THA.

INTERVENTION

Not applicable.

MAIN OUTCOMES MEASURES

The data collected included age, body mass index, clinical score from the questionnaires, hip pain, range of motion, muscle strength, and Physical functions (10-Meter Walk Test, Timed Up and Go test and Sit-to-stand). Patients were classified into sufficient and insufficient activity groups based on their University of California, Los Angeles (UCLA) activity score 1 year after THA. Variables measured preoperatively and at 3 weeks postoperatively were analyzed using univariate and multivariate methods to derive CPR for physical activity.

RESULTS

A CPR was developed using the following five factors and cut-offs: age, ≤70.5 years; preoperative UCLA activity score, ≥3.5; preoperative hip abduction strength, ≥0.54 Nm/kg; preoperative knee extension strength, ≥1.04 Nm/kg; and 10-Meter Walk Test, ≤8.49 s at 3 weeks after surgery. The presence of four of the five factors predicted a sufficient physical activity level at 1-year, with a positive likelihood ratio of 5.94 and probability of 85.4%. The presence of five predictor variables increased the probability of sufficient physical activity after THA to 94.7%.

CONCLUSION

This study developed a CPR for physical activity at 1-year after THA. Having four or more of the five measurements were useful indicators for predicting of physical activity at 1 year postoperatively.

Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients

BACKGROUND

Inpatient glucose management can be challenging due to evolving factors that influence a patient's blood glucose (BG) throughout hospital admission. The purpose of our study was to predict the category of a patient's next BG measurement based on electronic medical record (EMR) data.

METHODS

EMR data from 184,361 admissions containing 4,538,418 BG measurements from five hospitals in the Johns Hopkins Health System were collected from patients who were discharged between January 1, 2015 and May 31, 2019. Index BGs used for prediction included the 5th to penultimate BG measurements (N = 2,740,539). The outcome was category of next BG measurement: hypoglycemic (BG  ≤  70 mg/dl), controlled (BG 71-180 mg/dl), or hyperglycemic (BG > 180 mg/dl). A random forest algorithm that included a broad range of clinical covariates predicted the outcome and was validated internally and externally.

FINDINGS

In our internal validation test set, 72·8%, 25·7%, and 1·5% of BG measurements occurring after the index BG were controlled, hyperglycemic, and hypoglycemic respectively. The sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·77/0·81, 0·77/0·89, and 0·73/0·91, respectively. On external validation in four hospitals, the ranges of sensitivity/specificity for prediction of controlled, hyperglycemic, and hypoglycemic were 0·64-0·70/0·80-0·87, 0·75-0·80/0·82-0·84, and 0·76-0·78/0·87-0·90, respectively.

INTERPRETATION

A machine learning algorithm using EMR data can accurately predict the category of a hospitalized patient's next BG measurement. Further studies should determine the effectiveness of integration of this model into the EMR in reducing rates of hypoglycemia and hyperglycemia.

Limitations of Animal Studies for Predicting Toxicity in Clinical Trials: Is it Time to Rethink Our Current Approach?

Animal testing is used in pharmaceutical and industrial research to predict human toxicity, and yet analysis suggests that animal models are poor predictors of drug safety in humans. The cost of animal research is high-in dollars, delays in drug approval, and in the loss of potentially beneficial drugs for human use. Human subjects have been harmed in the clinical testing of drugs that were deemed safe by animal studies. Increasingly, investigators are questioning the scientific merit of animal research. This review discusses issues in using animals to predict human toxicity in pharmaceutical development. Part 1 focuses on scientific concerns over the validity of animal research. Part 2 will discuss alternatives to animal research and their validation and use in production of human pharmaceuticals.