Measuring the incidence of hospital‐acquired complications and their effect on length of stay using CHADx

Abstract

An automated CHADx reporting system can be used to collect data on patients with hospital-acquired complications. Such data can be used to increase emphasis on patient safety and quality of care and identify potential opportunities to reduce LOS.

Clinical and cost-effectiveness analysis of early detection of patients at nutrition risk during their hospital stay through the new screening method CIPA: a study protocol

“…Readmission rates, i.e., within 3 months post-discharge.Rate of surgical interventions during hospital stay.Incidence of clinical in-hospital complications according to the Classification of Hospital-Acquired Diagnoses (CHADx) [15]. The CHADx tool is a hierarchical classification of encoded diagnoses based on the International Classification of Diseases-10 (ICD-10), which allows quantification of the burden of in-hospital complications.…”

Intrahospital transfers and adverse patient outcomes: An analysis of administrative health data

“…Previous studies note the limitations of using administrative data, such as potential misclassification, lack of socio‐demographic information and the likely underreporting of diagnoses and procedures (Grosse, Boulet, Amendah, & Oyeku, ). Additionally, although CHADx outcomes have been used in several previous studies and found to be a reliable measure (Michel et al., ; Trentino, Swain, Burrows, Sprivulis, & Daly, ), their reliance on coded administrative data potentially exacerbates these issues and analyses should be viewed accordingly. In addition, other factors such as nurse staffing and skill mix, found in previous studies to be important predictors of patient outcomes (Duffield et al., ; Roche, Duffield, Aisbett, Diers, & Stasa, ), were not measured.…”

“…CHADx outcomes have been used in several previous studies and found to be a reliable measure Trentino, Swain, Burrows, Sprivulis, & Daly, 2013), their reliance on coded administrative data potentially exacerbates these issues and analyses should be viewed accordingly. In addition, other factors such as nurse staffing and skill mix, found in previous studies to be important predictors of patient outcomes (Duffield et al, 2011;Roche, Duffield, Aisbett, Diers, & Stasa, 2012), were not measured.…”

Survival studies: competing risks, immortality and censoring

“…Another example of this bias is when researchers study the impact of an event during a hospital stay (eg, a hospital‐acquired infection) on length of stay. Many researchers simply compare the total lengths of stay for patients who did and did not experience an infection, but this includes the time before participants had an infection, which introduces immortal time bias 8 , 9 . Another way to understand this is to think that the longer one is in hospital, the greater the risk of developing infection — a chicken and egg problem.…”

“…Many researchers simply compare the total lengths of stay for patients who did and did not experience an infection, but this includes the time before participants had an infection, which introduces immortal time bias. 8,9 Another way to understand this is to think that the longer one is in hospital, the greater the risk of developing infectiona chicken and egg problem. Including this immortal time in the study design means the effect of infection on length of stay is greatly exaggerated.…”