ICD-10-CA/CCI coding algorithms for defining clinical variables to assess outcome after aortic and mitral valve replacement surgery

High-Throughput Computing to Automate Population-Based Studies to Detect the 30-Day Risk of Adverse Outcomes After New Outpatient Medication Use in Older Adults with Chronic Kidney Disease: A Clinical Research Protocol

Background

Safety issues are detected in about one third of prescription drugs in the years following regulatory agency approval. Older adults, especially those with chronic kidney disease, are at particular risk of adverse reactions to prescription drugs. This protocol describes a new approach that may identify credible drug-safety signals more efficiently using administrative health care data.

Objective

To use high-throughput computing and automation to conduct 700+ drug-safety cohort studies in older adults in Ontario, Canada. Each study will compare 74 acute (30-day) outcomes in patients who start a new prescription drug (new users) to a group of nonusers with similar baseline health characteristics. Risks will be assessed within strata of baseline kidney function.

Design and setting

The studies will be population-based, new-user cohort studies conducted using linked administrative health care databases in Ontario, Canada (January 1, 2008, to March 1, 2020). The source population for these studies will be residents of Ontario aged 66 years or older who filled at least one outpatient prescription through the Ontario Drug Benefit (ODB) program during the study period (all residents have universal health care, and those aged 65+ have universal prescription drug coverage through the ODB).

Patients

We identified 3.2 million older adults in the source population during the study period and built 700+ initial medication cohorts, each containing mutually exclusive groups of new users and nonusers. Nonusers were randomly assigned cohort entry dates that followed the same distribution of prescription start dates as new users. Eligibility criteria included a baseline estimated glomerular filtration rate (eGFR) measurement within 12 months before the cohort entry date (median time was 71 days before cohort entry in the new user group), no prior receipt of maintenance dialysis or a kidney transplant, and no prior prescriptions for drugs in the same subclass as the study drug. New users and nonusers will be balanced on ~400 baseline health characteristics using inverse probability of treatment weighting on propensity scores within 3 strata of baseline eGFR: ≥60, 45 to <60, <45 mL/min per 1.73 m2.

Outcomes

We will compare new user and nonuser groups on 74 clinically relevant outcomes (17 composites and 57 individual outcomes) in the 30 days after cohort entry. We used a prespecified approach to identify these 74 outcomes.

Statistical analysis plan

In each cohort, we will obtain eGFR-stratum-specific weighted risk ratios and risk differences using modified Poisson regression and binomial regression, respectively. Additive and multiplicative interaction by eGFR category will be examined. Drug-outcome associations that meet prespecified criteria (identified signals) will be further examined in additional analyses (including survival, negative-control exposure, and E-value analyses) and visualizations.

Results

The initial medication cohorts had a median of 6120 new users per cohort (interquartile range: 1469-38 839) and a median of 1 088 301 nonusers (interquartile range: 751 697-1 267 009). Medications with the largest number of new users were amoxicillin trihydrate (n = 1 000 032), cephalexin (n = 571 566), prescription acetaminophen (n = 571 563), and ciprofloxacin (n = 504,374); 19% to 29% of new users in these cohorts had an eGFR <60 mL/min per 1.73 m2.

Limitations

Despite our use of robust techniques to balance baseline indicators and to control for confounding by indication, residual confounding will remain a possibility. Only acute (30-day) outcomes will be examined. Our data sources do not include nonprescription (over-the-counter) drugs or drugs prescribed in hospitals and do not include outpatient prescription drug use in children or adults <65 years.

Conclusion

This accelerated approach to conducting postmarket drug-safety studies has the potential to more efficiently detect drug-safety signals in a vulnerable population. The results of this protocol may ultimately help improve medication safety.

Child maltreatment surveillance following the ICD-10-CM transition, 2016-2018

BACKGROUND

Child maltreatment is poorly documented in clinical data. The International Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification (ICD-10-CM) represents the first time that confirmed and suspected child maltreatment can be distinguished in medical coding. The utility of this distinction in practice remains unknown. This study aims to evaluate the application of these codes by patient demographic characteristics and injury type.

METHODS

We conducted secondary data analysis of emergency department (ED) discharge records of children under 18 years with an ICD-10-CM code for confirmed (T74) or suspected (T76) child maltreatment. Child age, sex, race/ethnicity, insurance status and co-occurring injuries (S00-T88) were compared by maltreatment type (confirmed or suspected).

RESULTS

From 2016 to 2018, child maltreatment was documented in 1650 unique ED visits, or 21.7 per 10 000 child ED visits. Suspected maltreatment was documented most frequently (58%). Half of all maltreatment-related visits involved sexual abuse, most often in females and individuals of non-Hispanic white race. Physical abuse was coded in 36% of visits; injuries to the head were predominant. Non-Hispanic black children were more frequently documented with confirmed physical abuse than suspected (38.7% vs 23.7%, p<0.01). The rate of co-occurring injuries documented with confirmed and suspected maltreatment differed by 30% (9.2 vs 12.5 per 10 000 ED visits, respectively).

CONCLUSIONS

The ability to discriminate confirmed and suspected maltreatment may help mitigate clinical barriers to maltreatment surveillance associated with delayed diagnosis and subsequent intervention. Racial disparities in suspected and confirmed cases were identified which may indicate biased diagnostic behaviours in the ED.

Defining and validating comorbidities and procedures in ICD-10 health data in ST-elevation myocardial infarction patients

Administrative health databases are used in research to define comorbid conditions, diagnosis, and procedures. Our objectives were to validate a diagnosis of ST-elevation myocardial infarction (STEMI) and invasive cardiac procedure coding against a comprehensive registry of STEMI patients and determine an optimal algorithm for defining comorbidities using administrative hospitalization and ambulatory databases, but without using a physician claims database, which is unavailable for use in many jurisdictions.A registry of consecutive STEMI patients was used to define a reference cohort and linked to the hospitalization and ambulatory databases. Four administrative case definitions for defining comorbidities, as well as STEMI diagnosis and in-hospital procedures using the International Classification of Diseases, 10th Revision (ICD-10) and the Canadian Classification of Health Interventions (CCI) were evaluated. Metrics were used to evaluate algorithm performance and compare discriminative ability using the C statistic.The 3236 patients had median age of 60 years (interquartile range 52-71) and 75.7% were male. A diagnosis of STEMI was correctly identified in the administrative records for 3043 (94.0%) patients. In-hospital procedures (coronary artery bypass grafting, percutaneous coronary intervention, and angiogram) were well identified using administrative definitions (Kappa statistic 0.83-1.00). Validation of comorbidities varied by condition but an algorithm using 2 inpatient/ambulatory visits in the previous 2 years maximized PPV, ranging from 28.6% for previous heart failure to 95.7% for previous MI. The c statistic was similar for each of the methods, ranging from 0.76 to 0.80.ICD-10 and CCI codes can identify hospitalized STEMI patients with high sensitivity and accurately define in-hospital cardiac procedures. Comorbidities can be defined with high PPV using a definition of 2 inpatient/ambulatory visits in the previous 2 years.

Population-based review of the outcomes following hepatic resection in a Canadian health region

BACKGROUND

Higher hospital and surgeon volumes have been associated with improved outcomes following hepatic resection; however, there appear to be additional factors that also play a role. The objective of our study was to examine the outcomes following hepatic resection over the past 13 years in a large urban Canadian health region.

METHODS

We used administrative procedure codes to identify all patients from 1991/92 to 2003/04 who underwent a hepatic resection in the Calgary health region, which has a referral base of about 1.5 million people. The primary outcome was operative mortality, defined as death before discharge.

RESULTS

There were 424 hepatic resections performed in the stated time period. Annual volume was stable until 2000, when it increased substantially. This corresponded to the formation of a multidisciplinary group that provided care to these patients. There were 25 deaths over the study period for a mean mortality of 5.9%. The mean length of stay in hospital was 14.6 (median 10) days. Over time, however, mortality steadily decreased. This corresponded to a concomitant increase in the volume of hepatic resections performed.

CONCLUSION

Over the past 13 years, the number of hepatic resections performed has increased; there has been a corresponding improvement in mortality rates. The improved rates are likely the result of multiple factors.

Use of administrative data or clinical databases as predictors of risk of death in hospital: comparison of models

OBJECTIVE

To compare risk prediction models for death in hospital based on an administrative database with published results based on data derived from three national clinical databases: the national cardiac surgical database, the national vascular database and the colorectal cancer study.

DESIGN

Analysis of inpatient hospital episode statistics. Predictive model developed using multiple logistic regression.

SETTING

NHS hospital trusts in England.

PATIENTS

All patients admitted to an NHS hospital within England for isolated coronary artery bypass graft (CABG), repair of abdominal aortic aneurysm, and colorectal excision for cancer from 1996-7 to 2003-4.

MAIN OUTCOME MEASURES

Deaths in hospital. Performance of models assessed with receiver operating characteristic (ROC) curve scores measuring discrimination (<0.7=poor, 0.7-0.8=reasonable, >0.8=good) and both Hosmer-Lemeshow statistics and standardised residuals measuring goodness of fit.

RESULTS

During the study period 152 523 cases of isolated CABG with 3247 deaths in hospital (2.1%), 12 781 repairs of ruptured abdominal aortic aneurysm (5987 deaths, 46.8%), 31 705 repairs of unruptured abdominal aortic aneurysm (3246 deaths, 10.2%), and 144,370 colorectal resections for cancer (10,424 deaths, 7.2%) were recorded. The power of the complex predictive model was comparable with that of models based on clinical datasets with ROC curve scores of 0.77 (v 0.78 from clinical database) for isolated CABG, 0.66 (v 0.65) and 0.74 (v 0.70) for repairs of ruptured and unruptured abdominal aortic aneurysm, respectively, and 0.80 (v 0.78) for colorectal excision for cancer. Calibration plots generally showed good agreement between observed and predicted mortality.

CONCLUSIONS

Routinely collected administrative data can be used to predict risk with similar discrimination to clinical databases. The creative use of such data to adjust for case mix would be useful for monitoring healthcare performance and could usefully complement clinical databases. Further work on other procedures and diagnoses could result in a suite of models for performance adjusted for case mix for a range of specialties and procedures.